UNIVERSITY OF CALIFORNIA
OAVIS
NOV 0 7 1984
EER. REC. LIBRARY
An Annual Planning
Model for Food Processing:
An Example of the
Tomato Industry
Giannini Foundation Research Report No. 332
Division of
— Agriculture and Natural Resources
UNIVERSITY OF CALIFORNIA
PRINTED MAY 1984
Table of Contents
Acknowledgements j
Objective 4
Methodology and Model 4
An Overview 4
Processing Plant Definition 5
Labor Requirements and Costs 8
Other Inputs 9
Evaporator Clean-up and Boiler Start-up Costs 11
Production Options 11
The Initializing Management Decisions 12
Acreage and Planting Dates 13
Summary of Model Development 18
Results 24
References 30
Appendices 31
The Giannini Foundation Research Report Series is designed to
communicate research results to specific professional audiences
interested in applications. The first Research Report was
issued in 1961 as No. 246, continuing the numbering of the GF
Mimeograph Report Series which the Research Report replaced.
Other publications of the Foundation and all publications of
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Single copies of this Research Report or the most recent
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Acknowledgements
I would like to express my appreciation to Christi Bengard of the U.C. Davis Agricultural
Economics Data Services for her excellent contribution in translating the planning model into the
computer program presented in the Appendix, and to a California food processing firm for
providing data on operating requirements and costs of tomato canning. Also, thanks to Ben C.
French, Department of Agricultural Economics, University of California, Davis, and L. L. Sammet,
Department of Agricultural and Resource Economics, University of California, Berkeley, for
reviewing the manuscript, and Patti Boyland, Research Assistant in the Department of
Agricultural Economics, University of California, Davis, for her aid in developing the heat -unit
model and reviewing the planning model. Finally, thanks also go to Robert Skinner, K. Charles
Ling and Jeffrey S. Royer of the Agricultural Cooperative Service of the U.S. Department of
Agriculture and to that agency for financially supporting this research project, and to the
Agricultural Economics Word Processing Unit of the University of California, Davis, for typing
assistance and manuscript preparation.
AN ANNUAL PLANNING MODEL FOR FOOD PROCESSING:
AN EXAMPLE OF THE TOMATO INDUSTRY
Samuel H. Logan 1
Food processors often face operations which differ from the continuous, year-around
operations of most manufacturing firms. Food processing is generally highly seasonal because of
the biological growth patterns of the commodities which are the major inputs in the processing
function. Also, the raw product may be perishable in its fresh state, input quality may be
variable, and the flow of raw product to the processing plant during the harvest season is
uncertain, depending largely on the climatical whimsy of nature. Although multiple products are
characteristic of many industries, food processors seldom use the planned batch-type of production
found in other manufacturing industries. Batch production in manufacturing operations typically
means exclusive production of one of a number of products for a time period, then a switch to the
production of a different product. But, many food processing firms must be able to channel raw
product into a variety of final goods being produced simultaneously on independent processing
lines, a characteristic demanded by the perishability and quality variability of the raw product.
Production management literature offers a variety of planning tools dealing with inventories
and procurement of inputs (for example, see Hillier and Lieberman 1980, or Dilworth 1983).
However, the perishability (i.e., the nonstorable nature) of raw food products as well as the
uncertainty of the available produce supply prevents the application of many of these models by
food processors to their major input-raw farm products. Such models, however, can be useful in
planning inventory levels for secondary, nonperishable inputs such as cans, cartons, and recipe
ingredients given an expected flow of raw product to the processing plant.
The short processing season and the raw product characteristics outlined above emphasize
the need for annual, aggregate planning and scheduling by the processor prior to the harvest
season in order to make efficient use of plant facilities and resources. Most food processors make
such plans several months in advance of the actual processing season, realizing that weather
conditions will likely alter the annual plan when the processing actually begins.
This paper presents a computer model for developing such an annual, aggregate plan.
Specifically, it is designed for a tomato processing firm which converts whole tomatoes into a
variety of products packaged in different sizes of containers. The goal of the systems model is to
find a least-cost plan of operation over the processing season, given a projected arrival pattern of
raw product to the plant. For firms which may stipulate delivery dates (or planting dates) to their
producers (growers), the model will also determine expected acreage and planting dates needed to
provide the scheduled arrivals of tomatoes at the plant.
Tomato processing follows the operational traits outlined in general terms above. Different
varieties and quality of tomatoes arrive daily during the harvest season. Quantities of arrivals of
raw product are not uniform over the season, but begin slowly in early summer, reach a peak
which is maintained for several weeks, then taper off in the early fall weeks. These tomatoes can
be converted into various products: whole (peeled) tomatoes, sauce, paste, puree, tomato juice, and
catsup. These products, in turn, can be packed in various sizes of cans and containers.
While the average interval between planting and harvesting of tomatoes generally is about
150 days, the actual length of this period depends on temperature and other climatic factors, a
situation which may produce unexpected shortages and gluts of raw product within the same
processing season.
1. Professor of Agricultural Economics and Agricultural Economist in the Experiment Station and on
the Giannini Foundation, University of California, Davis.
1
The initial planning for the upcoming processing season is done during the winter months
when the year's aggregate production goal is determined and that quantity is allocated among the
various final products which can be produced at the plant. These initial decisions are based on
maximizing some objective function such as profits and/or on meeting prior commitments (e.g.,
contracts) to producers or customers. Once the quantity targets have been established, it is the
task of the production manager to plan the short term (weekly) operations of the plant for the
processing season.
The plant operations consist of several more or less independent stages. 2 As used in the
analytical model later, these stages are denned as:
I. Receiving and general preparation. The incoming tomatoes are unloaded from trucks,
washed, and routed to either whole tomato processing or processed products processing.
II. Preparation-whole tomatoes. Tomatoes allocated to whole tomato processing are washed, and
checked for foreign matter and/or mold. Tomatoes not meeting these initial checks are
disposed of; the other tomatoes are further sorted for color, texture, and grade. Tomatoes
meeting the color, texture, grade requirements are peeled and continue to the whole tomato
processing operations; the others are diverted to processed products processing.
HI. Preparation-processed products. Those tomatoes initially allocated to processed products from
Stage I are washed and sorted for foreign matter and/or mold, ground (chopped) and sent as
hot broken tomatoes to the appropriate evaporators.
IV. Filling and processing -processed products. Material from Stage III is blended into the
particular final product desired and sent to the appropriate filling and can sterilizing line
where the cans are sealed.
V. Filling and processing-whole tomatoes. The raw material from Stage II is sent to a particular
whole tomato canning line where the cans are filled, syrup is added, and the cans are sealed.
VI. General processing. Canned items from Stages IV and V are cooked and the seams are
inspected.
VII. General service. This stage provides general, common service to the above operations and
includes mechanical repair, electrical operations, personnel administration services, and
quality control.
VIII. Brites stacking. The cans from the various canning lines (with the exception of those from
Stage IX) are cooled, stacked on pallets, and covered for transportation to the warehouse.
IX. Cooling floor. Cans from certain whole tomato canning lines are stacked while hot and are
air cooled prior to storage.
X. Pack receiving. Items from Stages VIII and IX are received and stored at the warehouse.
Most processing plants are similar in organization to the above format; however, minor
differences will be found among specific plants. Furthermore, the particular aspects of the model
developed in this paper are representative of actual plant operations and can easily be modified to
fit particular situations in other applications.
These functions emphasize the need for harmonious combinations of the capacities and
operations of the different stages to assure a smooth flow of product through the plant while
avoiding idle time (excess capacities).
The major canning operations (Stages IV and V) are performed on a series of can filling
lines, each of which has some limiting output capacity for a given final product. While some lines
can be utilized to process more than one final product (e.g., sauce or paste), the line can process
only one alternative product at a time and will generally be used for one product for an extended
length of time (e.g., a week) to avoid the costs involved with a product changeover. Furthermore,
each line is oriented to a fixed can size which is determined by the technical nature of the
2. For a detailed discussion of tomato processing operations see Uyeshiro (1972).
2
equipment on that line. Thus, the initial management decision about the quantity of individual
final products to be produced determines the priorities with which the raw product is sent to the
specific processing lines. Although the raw product flow may be common to several canning lines,
the lines operate with little interaction with each other because of the equipment constraints.
While the firm's initial production goals of the various final products rest on optimizing some
objective function (e.g., profit maximization), once the flow of raw product begins, the production
manager is generally concerned with minimizing the variable cost of producing a given weekly
level of output. 3 In this context, the basic decisions each week are then at what rate per time
period (hour) to produce and how many time periods (hours or shifts) per week to operate.
The rate of production refers either to the amount of final product (e.g., cases of canned
tomatoes) processed per period of time or the equivalent quantity of raw material processed in the
same period of time. 4 The rate of output per hour on a particular processing line generally can be
varied to some degree; however, the capacity of that line eventually reaches some technically
imposed limit. Furthermore, the labor required to operate the line at reduced output levels does
not decrease proportionately, but often remains near the amount needed for capacity output.
Thus, the lowest labor cost per unit of output for a given canning line is often achieved near (or
at) the peak capacity production. Because of this factor, plants tend to operate canning lines at or
near capacity and vary the plant's aggregate rate of output via duplicate or multiple lines rather
than altering production on a given line.
The other decision variable in planning for a particular aggregate weekly output is the
number of time periods, or number of shifts, operated. Several combinations of rate and time of
production can yield a given output, but costs will vary with the different combinations (see
French et al^ 1956). Labor agreements generally stipulate some minimum number of hours to be
worked, either daily or weekly, but the manager can schedule overtime work or add additional
shifts of operation. Overtime hours significantly increase wage costs (at 1.5 times the regular pay
for overtime), and additional shifts may require a premium payment (e.g., $.10 per hour extra pay)
for the evening and night shifts.
Other factors, however, complicate the decisions on rate and time of production. For
example, if the plant works less than three shifts per day, the processing equipment must be
cleaned at the end of the final shift, boilers must be turned off between the current day's last shift
and the next day's first shift, then started again. Operating three shifts per day for the entire
week on fewer lines (lower rate of output) may eliminate most of these cleanup and heating costs,
even though the labor costs may increase.
The rate and time dimensions of production operations have been discussed for food
processors by French et ah (1956). In their theoretical model for deriving the optimal rate and
time of production, the time dimension was generally viewed as linear for a given rate of
production. That is, output and cost for several time periods was simply a linear multiple of a
single period level, with possible adjustment for added overtime costs in some periods. This
specification is appropriate for a single line operation; however, in the case of tomatoes, it is
possible that not all the lines used in the first shift will be used in the second or third shifts. If a
firm allocates a certain proportion of its output to whole, peeled tomatoes and the remainder to
processed products, it may be able to process the former quantity in one shift per day, but need to
work two or more shifts on the processed lines, depending on the design and capacities of the
various canning lines. Thus, the total plant processing cost function represents many
combinations of rates and times of production, and could be viewed as
3. Only variable costs of processing are considered in this study inasmuch as the plant facility itself
is fixed. Thus only variable costs are relevant to the operating decisions.
4. Given the commonality of the basic raw product, tomatoes, in this study, it is more convenient to
consider rate of output in terms of raw product equivalent.
3
TC = £ QSi
i
where TC = total variable processing costs
C- = variable cost per shift of operating line i
Sj = number of shifts worked by line i.
The goal of the planning process is to consider the cost trade-offs between rates of output and the
time periods worked each week in such a manner as to find the lowest variable cost to process a
given level of raw product.
Objective
In order to plan procurement of labor and other inputs, given the planned arrivals of raw
tomatoes, management first must determine through its advanced planning function outlined
above the expected rate of output and number of hours (or shifts) to be worked for each week of
the processing season. This plan, in turn, is used to derive the number (and costs) of employees as
well as the quantities (and costs) of other inputs required to achieve the planned production
levels.
The goal of this paper, therefore, is to present a computerized annual, aggregate planning
systems model which can generate for a tomato processor such a seasonal plan or schedule in
terms of rates and hours of output that would minimize the cost of producing a set level of output.
Given the discrete nature of expansion and contraction of output caused by the use of multiple
canning lines as well as the relative constancy of labor over wide ranges of output for a given
line, the model must search among the feasible alternative combinations of rates and time of
output to find that combination which yields the lowest cost for processing the week's expected
arrivals of raw tomatoes. 5 Furthermore, the model should calculate the expected required acreage
and time of planting which will yield the expected weekly quantities of raw product.
The model presented here is based on operating specifications for an existing California
tomato processing plant with a given number of processing lines and a fixed combination of
possible final products. Many of the input requirements and their associated costs were provided
by the processing company; however, other data were obtained from previous studies, other
industry sources, and published historical data. The quantity of tomatoes to be processed over the
entire season is predetermined as are the desired proportions of total output to be assigned to the
various final products.
Methodology and Model
An Overview
As indicated above, the planning model is designed to produce weekly operating schedules
and costs; however, these derivations are based on several prior management decisions and a set
of input data. These management decisions include specification of (a) the annual quantity of
tomatoes to be processed, (b) the allocation of these quantities to the various final products (i.e.,
whole peeled tomatoes or processed products), (c) the priority with which the various products are
to be produced (this priority stipulates the order in which the various product canning lines will
be utilized), (d) the beginning and ending weeks of the plant's operating season, and (e) the
5. Several methods of aggregate planning have been reported in other studies including the linear
decision rule developed by Holt, et aL (1955), the search decision rule reported by Taubert (1968),
and linear programming as discussed by Bowman (1956). The method employed in this paper
would more nearly reflect Taubert 's search decision rule process. Because the labor costs of adding
a new line to the plant's operations are more or less fixed (indivisible) over a large range of output
and because of the importance of labor costs of associated operations which are not related directly
to any one canning line, the linear programming approach was not utilized in this study.
4
quantities of raw product arriving each week. This latter item (e) may be a management decision
if delivery dates are specified for the plant's growers, or, as in the case of this model, the
proportions of the annual quantity arriving each week can be based on past historical data.
The basic initial data include technical coefficients for (a) the efficiency level (percent of
rated capacity) with which the plant operates, (b) damage allowance levels for inputs such as cans
and cartons, (c) conversion of raw product into the various final products, (d) physical input
requirements for labor, utilities, cans, cartons and other inputs, (e) yields of tomatoes obtained by
growers, and (f) heat unit (temperature) requirements for tomato plant growth. In addition to the
technical coefficients, additional data are needed relating to the costs of the inputs and historical
weather (temperature) data.
The model then determines the quantities to be processed each week of the season, and sets
the number of days to be worked each week. Frequently the quantities of whole tomatoes and
processed products to be processed in a given week can be accommodated by any one of several
combinations of canning lines being operated and numbers of shifts worked. The planning model
finds each of these feasible alternative combinations and determines the labor and clean-up
(evaporator clean-up and boiler start-up) costs associated with each combination. Most of the
other costs (e.g., cans, cartons, etc.) remain fixed regardless of the combination selected, so only
the labor and clean-up costs for each feasible alternative combination are examined; the
alternative with the lowest such costs is then selected as that week's planned schedule. The costs
of all other inputs are then added to determine the week's total operating costs.
In addition, given the yield data, the total acreage required to supply the plant with the
week's planned deliveries is calculated. Furthermore, using historical temperature data and the
concept of heat -units (degree-days) to estimate time between planting and harvest, the model will
specify planting dates for different geographical regions supplying the plant.
The weekly schedule is printed out as well as a seasonal summary table of costs. The
procedure j ust described is also shown in Figure 1.
An additional benefit (to the scheduling per se) of such a computerized method of planning is
the ability to adjust the plan to different sets of assumptions related to the arrival rates of raw
product, desired proportions of final product forms, or costs of the individual inputs.
Processing Plant Definition
The processing plant in this model possesses 12 independent canning lines, seven of which
produce only whole tomatoes (in some form) in various sizes of cans and five of which produce
processed products either as sauce and puree or as paste. Of the latter five lines, two lines can
produce either sauce and puree or paste; the other three lines produce only paste.
The individual line data regarding product type, can size, and capacity in cases of final
product per hour are given in Table 1 along with the conversion coefficients to change the
capacity figures to pounds of raw equivalent. The rated hourly capacity of each canning line is
determined by the technical (mechanical) limitations of the equipment on that line.
The lines are numbered to indicate the priority with which they are to be added to the
production sequence. This priority reflects the order in which the management wishes to produce
the given products. Thus, for whole tomatoes, the initial product would begin with line 1
producing 303 size cans and expand through line 7 with 2-1/2 can size.
In this model, the lines 8 and 12 will be used to produce sauce and puree until the season's
goals for those products are met and then will be changed to produce paste for the remainder of
the season.
In terms of raw product equivalent, the plant has a rated hourly capacity of about 47 tons of
whole, peeled tomatoes, 122 tons of paste, and 42 tons of sauce and puree. If the plant produces
only whole tomatoes and paste, total rated capacity is 169 tons per hour; if it processes whole
tomatoes, paste, and sauce and puree, the total rated capacity is 159 tons per hour.
5
Figure 1
Input Basic Data
(Annual pack, proportion of weekly arrivals, proportions for various products, technical production
relationships, cost relationships, temperature data, etc.)
Determine Weekly Arrivals
Allocate Weekly Arrivals to Whole
Tomatoes, Processed Products
Find Number of Working Days for the Week
Find Average Daily Output of Whole
Tomatoes and Processed Products
Find Production Combinations of Shifts and
Lines Needed to Can Week's Pack
Calculate Week's Labor Requirements, Labor Costs, and
Cleanup Costs for Each Feasible Production Combination
Select Lowest Cost Option as Week's Production Plan
Calculate Cost of Other Inputs
Calculate Number of Cases Produced on Each
Canning Line and Number of Cans Needed
Find Total Cost of Operations for Week T
Find Number of Acres Needed to Supply Week's Pack
Calculate Planting Date for Week T
Repeat for Each Week of Season
Find Total Costs of Season's Operation
6
Table 1. Canning Lines, Products, Can Sizes,
Output Capacities, and Conversion Coefficients
Line
Product
Can Size
Capacity
(Cases/hour)
Lbs. Raw Product/ Case 3 "
Conversion Coefficient
1
Whole
303
350
28.000
2
Whole
303
450
28.000
3
Whole (stewed)
303
550
28.000
4
Whole
10
200
45.388
5
Whole
10
400
45.388
6
Whole
2-1/2
140
49.420
7
Whole
2-1/2
450
49.420
8
Sauce & Puree
10
420
113.470
Paste
10
350
213.972
9
Paste
48/6
430
95.040
10
Paste
24/12
500
114.972
11
Paste
48/6
430
95.040
12
Sauce & Puree
2-1/2
300
123.550
Paste
2-1/2
125
232.980
a Derived from Brandt e^al., 1978, p. 114.
7
The rated line capacities in Table 1 are those associated with 100 percent operation;
however, allowances must be made for downtime resulting from breakdowns and other stoppages.
In this case, the rated capacities were multiplied in the computer model by a factor of .7 to obtain
the actual line capacities, based on estimates from a tomato processing firm.
6
Labor Requirements and Costs
The hourly labor requirements for the 10 stages of operation given earlier were obtained
from industry sources. The various tasks performed by individual workers in each stage are
shown in Appendix Table 1 along with the base hourly wage rates. 7 The base hourly pay applies
to the first shift of the day. A $.10 per hour premium is added for the second shift, and a $.15 per
hour premium is added for the third shift. Overtime pay is 1.5 times the appropriate regular
hourly scale.
Much of the direct labor required in tomato processing operations is more or less constant
regardless of the rate of output. For example, most of the labor needed in the receiving and
general preparation operations, the general processing operations, the general service functions,
the brites stacking, cooling, and finished pack receiving operations remains essentially unchanged
no matter how many canning lines are being operated or what final products are being produced.
Thus, the number of workers shown for each task for a particular canning line represent full
capacity operation for that line. In the plant specified for this application, a total of 235
employees are required for full capacity operation (all 12 lines functioning). However, of that
number 185 are required even if only the first line is canning.
The computer model utilizes a concept of labor options in developing the appropriate labor
requirements for a given output. Initially, a base labor force for operating the first line of whole
tomato processing is specified as labor option A. This option shows the labor needed to initiate
operations of the plant on only the one canning line, but includes the labor requirements for all of
the associated operations in receiving, general processing, general service, etc. As additional
whole tomato processing lines are engaged, the incremental labor requirements (different options)
are added to the initial labor option. Because the processed products lines can be operated
independently with any combination of whole products lines, a base labor option (Labor Option H)
for the first processed product line (line 8) is established which adds the incremental labor needed
to the labor determined for the whole tomato operations. The subsequent labor additions for the
other processed products lines are added to that base processed products labor option. The
processed products labor requirements are then added to whatever combination of whole products
lines is used. Both the whole tomato canning lines and the processed products lines are added to
the operations in the sequence indicated by their line number. This sequence reflects the firm's
priority for producing the various final products, a priority which may be based on such factors as
expected market conditions or contractual arrangements with the firm's customers. Of course,
these sequences and their associated labor requirements can be changed to adapt to new market
or contractual conditions.
It is possible (and even likely), however, that the processed product lines may work
additional shifts without the whole products lines in operation. In this case, a separate base labor
option for the first processed products line must be defined which includes those general functions
that occur regardless of which canning lines are working. Labor Option M is defined as the base
requirement for line 8; the other options add the incremental labor to the base requirement as
other processed products lines are opened.
6. In this planning model, only the direct (hourly) labor requirements for the processing lines are
considered. For a discussion of other labor requirements see Uyeshiro (1972).
7. The wage rates were obtained for 1983 from industry sources and include an allowance of 35 per-
cent for fringe benefits.
8
The requirements for the various labor options are given in Appendix Table 2.
Other Inputs
The other major inputs included in the aggregate planning model include utilities
(electricity, gas, water), lye (required for whole tomato processing), cans, salt, and cartons.
Utility requirements were derived from previous work by Uyeshiro (1972), and from industry
estimates. The requirements in physical units are given in Table 2.
Uyeshiro (1972, p. 123) presents total annual electrical, gas, and water costs by product type
for a large tomato cannery. Each of these costs was converted to a cost per ton of raw product for
each of the three basic products considered here. If the cost rate per physical unit used for a
particular utility is the same for use in the various products processing, a ratio of the costs per
ton provides an approximate ratio of the physical requirements for the different usages.
Uyeshiro's electrical costs show equal levels of costs per ton of raw material processed for puree
and paste, while the electrical cost per ton of raw material processed into whole tomatoes was 4.25
times that level. Thus, 4.25R(Xw) + R(Xp) = KWH
where R = KWH per ton of raw material processed into processed products
Xw = tons of raw material used in whole tomatoes per time period
Xp = tons of raw material used in processed products per time period
KWH = total electrical usage per time period.
Based on an actual plant usage of 2,800,000 KWH for an annual production of 135,000 tons, R =
10.008 and 4.25R = 42.532 KWH per ton.
Similar procedures were used to estimate requirements for natural gas and water. The
ratios of gas usages were whole tomatoes 1, puree 1.43, and paste 1.05. Applied to an annual
usage of 2,596,150 therms, the requirements given in Table 2 are obtained.
The estimated water requirements ratios did not vary significantly by product type, so the
water consumption from actual plant data of 127,748,398.8 gallons resulted in a per ton use of
946.284 gallons. This level compares quite favorably with the average of 50 gallons/per case of
final product requirement estimated by Uyeshiro (1972, p. 54), (946.284 gallons per ton of raw
material processed is about 52 gallons per case of final product processed, on the average).
Costs of utilities were estimated at $.07 per KWH for electricity, $.52 per therm for natural
gas, and $.0004 per gallon for water.
The amount of lye used for processing whole tomatoes was 2.5 gallons per ton of raw product,
based on industry sources. The cost was $1.16 per gallon.
The quantities of cans and cartons required are easily calculated from the number of cases of
final product produced on each canning line. Five can sizes are used in this plant application with
the following numbers of cans per case: No. 303, 24 cans per case; No. 2-1/2, 24 cans per case; No.
10, 6 cans per case; 6 ounces, 48 cans per case; 12 ounces, 24 cans per case. A .005 allowance for
damaged (unusable) goods was added to the can and carton requirements.
Based on price quotations obtained from industry sources, the costs of the cans and the
appropriate cartons were set at:
Can Size
Cost/Can Cost/Carton (1983)
No. 303
No. 2-1/2
No. 10
6-ounce
12-ounce
$.113
.167
.467
.065
.096
$.178
.265
.225
.143
.138
9
a
Table 2. Utility Requirements for Tomato Processing
Electricity Natural Gas Water
Final Product (KWH/ton raw p roduct) (ther ms / ton raw) (gal. /ton raw)
Whole Tomatoes 42.532 17.553 946.284
Sauce & Puree 10.008 25.101 946.284
Paste 10.008 18.431 946.284
a See text for explanation of the derivation of these figures.
10
The other major variable input was salt, a factor which may vary as recipes change. In this
case, salt was utilized only for whole tomato products in the form of tablets per case of final
output. The requirements and cost per tablet were:
Can Size No. of Tablets Cost/Tablet (1983)
No. 303
No. 303 (stewed)
No. 10
No. 2-1/2
Evaporator Clean-up and Boiler Start-up Costs
For the plant in this problem, one evaporator is used for each processed product canning line.
Each time one of these lines ceases production (e.g., the associated line works only one or two
shifts per day), the evaporator must be cleaned and prepared for use the following day. With
three shift operations, of course, this cost is avoided on a daily basis and may be incurred only
once a week or even every other week, depending on the number of days worked. An estimated
cost of $300 for chemical compounds per cleanup, obtained from industry sources, was used as the
nonlabor cost of evaporator cleanup.
In addition to the evaporators, tomato processing requires large quantities of hot water. Two
boilers were stipulated for the plant, one with a capacity of 120,000 pounds and one with 80,000
pounds capacity. Operations of less than three shifts per day generally entail shutting down the
boilers and then reheating them for the next day. Boiler company personnel estimated that the
cost of reheating the 120,000-pound capacity boiler at $2,000 per occurrence and the cost of
reheating the 80,000-pound capacity boiler at $1,340 per occurrence. The larger boiler was
assumed to handle the requirements from lines 8, 9, and 10, while the smaller boiler was assigned
to the lines 11 and 12.
Thus, the combined cleanup and boiler start-up costs per occurrence for the processed
products lines were estimated as follows:
Line
Boiler Start-up
Evaporator Clean-up
Total
8
$2,000
$300
$2,300
8, 9
$2,000
600
2,600
8, 9, 10
2,000
900
2,900
8, 9, 10, 11
3,340
1,200
4,540
8, 9, 10, 11, 12
3,340
1,500
4,840
24
24
12
24
$.0030
.0022
.0099
.0053
Production Options
Given the production capacities in raw product equivalent (including the adjustment for
down time), the model calculates the possible production options available to the production
manager. These production options show the maximum amounts of raw product that can be
processed per day for the various combinations (sequences) of lines being operated and shifts
being worked. Three sets of calculations are needed: (1) production options for processing whole
tomatoes on various lines for different number of shifts worked per day; (2) production levels for
processing products over lines 8 through 12 for different numbers of shifts per day when sauce
and puree are being processed on lines 8 and 12 and paste on lines 9 through 11; and (3)
production levels over lines 8 through 12 when paste is also being produced on lines 8 and 12.
11
Given the priority sequence with which the lines are utilized, (see Table 1) the production
options for whole tomatoes are derived by multiplying the hourly capacity of line 1 by 8 hours,
then adding to that the hourly capacity of line 2 multiplied by 8 hours, and so on, through line 7.
The process is repeated using 12 hours for 1.5 shift operations, 16 hours for 2 shifts, 20 hours for
2.5 shifts, and 24 hours for 3 shifts. This process implicitly assumes that expansion of output is
accomplished by operating those lines being utilized the same number of shifts rather than using
line 1 for, say, two shifts and line 2 for only one shift. Given the nature of the labor requirements
for the associated operations which are independent of the lines operating, this specification is
reasonable. Thus, there are 35 combinations of rates and times, or production options, for whole
tomatoes. (Five shift possibilities times seven line possibilities per shift.)
This process is also used to determine two sets of production options for processed products.
When producing sauce and puree on lines 8 and 12, production from line 8 becomes the initial
base output to which are added sequentially the outputs from the other canning lines as they are
used. The total number of production options for the five canning lines of processed products is
25. (Five shift possibilities times five line possibilities per shift).
The other set of processed products production options is calculated in the same manner as
the second, only lines 8 and 12 are used to process paste.
The Initializing Management Decisions
The basic initializing decisions required to begin the seasonal operation computations
include (1) the total quantity of raw product (in tons) to be processed over the season; (2) the
beginning and ending dates of the processing season; (3) the proportions of total seasonal
production to be allocated to whole tomatoes, sauce and puree, and paste; and (4) the proportions
of total quantity processed each week of the season.
At this point the plan can be developed. The week's scheduled arrivals are first allocated to
whole products and to processed products. Each allocation is then divided by the maximum,
three-shift processing capacity of the plant for the appropriate product (whole or processed
products) to determine the minimum number of days the plant has to operate. The larger of the
two calculations becomes the number of days worked. Given the labor contracts and the flow of
raw product to the plant during the week, the minimum number of days of operation is five, even
if the quantity to be processed can be accommodated in less time. As the tomato harvest increases
during the middle of the processing season, the flow of tomatoes to the plant during the week may
force the plant to operate more than five days, even though the total quantity could be processed
in only five days. (A five-day week would require storing raw product arriving on Saturday until
Monday for processing, an interval which would result in spoilage of the product; hence, the use
of a six-day week becomes necessary.)
If the arrival of raw product exceeds the amount that can be processed in seven days, the
excess material is carried over to the following week.
Once the number of days of operation is determined, the average daily output of processed
products is calculated and used to select the feasible production combinations of canning lines for
each of the five shift possibilities. The feasible option from each shift alternative is denned as
that production option whose quantity is closest to, but greater than, (or equal to) the average
daily output requirement of processed products. Thus, a maximum of five production options-one
from each of the shift possibilities -can be selected to produce the week's processed product
requirement. Initially, these feasible options are selected from those combinations which include
production of sauce and puree. This procedure is used until the plant has met the seasonal
requirements of sauce and puree at which time the production options are selected from the third
set described above which uses lines 8 and 12 to produce paste.
12
The same type of procedure is used to find the feasible production options for producing
whole tomatoes.
Each feasible production option for producing whole tomatoes is then combined with each
feasible option for producing processed products to yield all possible feasible combination of lines
and shifts which can be used to accomplish the week's output. Without any constraint, there
would be 25 possible combinations each week (one option for each of the five shift alternatives for
both whole and processed products). However, the model is constrained to consider only
alternatives in which the number of shifts worked in producing processed tomatoes is equal to or
greater than the number of shifts producing whole tomatoes. This constraint results from the
larger allocation of raw product to processed products and from labor contract stipulations.
Thus, the possible feasible combinations are reduced to 15 for a five-day or six-day week.
These 15 combinations simply show the number of shifts to be worked by the whole tomato lines
in conjunction with the processed products lines and are indicated by the X's in the following
tableau:
Processed Product Lines Work:
Whole tomato
1
1.5
2
2.5
3
lines work:
shift
shifts
shifts
shifts
shifts
1 shift
X
X
X
X
X
1.5 shifts
X
X
X
X
2 shifts
X
X
X
2.5 shifts
X
X
3 shifts
X
Naturally, there may not be 15 feasible production combinations if the weekly quantity to be
processed exceeds the plant's capacity when it operates at one shift, for example. While the
number of shifts worked might be the same for two separate weeks, the production options
selected as feasible might vary because of differences in the total quantity to be processed between
the two weeks.
The production options selected in these 15 combinations in turn define the labor
requirements and, therefore, the labor costs. The weekly labor and cleanup costs for each feasible
combination are referred to as cost alternatives in the program. The cost alternative (cost of the
production option combinations) which is the lowest among the feasible alternatives is selected as
that week's schedule.
Operation for seven days per week requires working three shifts per day, although less than
the total the number of lines may be operated.
Given the tonnage of raw product and the ensuing allocation among the various lines (final
products), the week's requirements and costs of utilities, cans, cartons, and other inputs can be
computed.
Acreage and Planting Dates
Specification of the weekly flow of raw product provides the basis for estimating the acreage
needed to assure that quantity. In this case an average yield of 26 tons per acre was used to
estimate the needed acreage values each week.
Estimating the planting date to assure harvestable tomatoes at a given week in the
processing season is more complex. This facet of the planning model applies the concept of heat-
units or degree-days as related to the maturing of the tomato plant. The particular method
applied in this case has been presented in detail in Logan and Boyland (1983).
13
The heat-unit model utilizes a sine function to approximate the behavior of temperatures
during the day, based on the premise that temperature efficiently represents the relevant climatic
conditions for tomatoes between time of planting and time of harvest. Heat units are simply that
part of the temperatures during the day which is available for plant growth and are determined
by integrating the sine function between each 24-hour period (from minimum temperature in day
1 to minimum temperature in day 2). The heat unit formulation also incorporates the nature of
tomato plant growth reported in the plant science literature (for example, Went 1957, Went and
Cosper 1945, and Owens and Moore 1974) by including as constraints: (1) a temperature below
which plan growth stops (45° Fahrenheit); (2) a high temperature (80°) above which plant growth
remains unchanged for an interval up to (3) a maximum high temperature (100°) above which
plant growth is retarded.
Consider a sine function of the form in Figure 2:
Temperature = (y sin X) + pi
where y = the amplitude of the sine curve and in this case is simply
T-t „
— or T — fx
ix = mean of the sine curve or
X = time of day in radians (2 tt = 1 day).
The values of y and /x are shifters of the usual sine curve which has an amplitude of 1 and a
mean of 0. At X = tt/2, temperature will equal T, the day's maximum; at -tt/2 and 3n/2,
temperature will equal t, the day's minimum level. Of course, the sine function is an imperfect
approximator of the day's temperature pattern since it is symmetric whereas the temperature
pattern generally is not.
Given this approximation, the heat units available each day (Y) are the area under the sine
curve between the two minimum values, i.e., the integral of the above function over the interval
between minima. Since time in the sine function is represented in radians (1 day = 2n radians),
the result divided by 2tt to obtain the equivalent value of Y for one day.
If we first stipulate a base temperature, g, below which tomatoes register little or no
effective growth, we can insert that in the above function as in Figure 3. The area of available
heat units now is that area under the sine curve but above the base line, g. Thus , the integral is
now between points a and b with the axis shifted by /n - g. Or, the function is given by
where a = fx-g/T-fx. Dividing the quantity /x-g by T-fx simply converts the shifter to a relative
value needed in the integration process. Alpha defines the two end points of the interval on the
sine curve containing the available heat units; however, because it represents a value on the
temperature axis rather than on the X or time axis, it must be converted into radians by finding
its arcsine.
Thus, a =— arcsin a and b = 7r+arcsine a. When 1, its arcsine is defined as n/2,
resulting in integration of the sine function between its two minima. In this manner, only those
temperatures which are above the base level are considered in determining the available heat
units.
14
Temp.
Figure 2. Daily heat units (shaded area) without temperature limits on growth
Figure 3- Daily heat units (shaded area) with lower temperature limit on growth
Temp.
y g
_£
t
if
-r 0 L r 2x IT X
t t
Figure 4. Daily heat units (shaded area) with both lower and higher temperature limits on
growth
Temp
15
In addition, the growth function for tomatoes reflects a maximum level at some temperature
(defined as h) and declines when temperatures exceed some extreme high (defined as h'). In this
situation, we want to exclude temperatures between h and h' and include a negative effect for
temperatures above h'. In other words, the area between points c and d and above line h in Figure
4 must be deleted from the previous calculations because temperatures above h do not contribute
to plant growth. Furthermore, for temperatures above h' in the figure, an additional negative
adjustment must be included. The alternative used here is to subtract the area above line h' from
the heat -unit total after prior adjustment for g and h. In the same manner as was done
previously, we define
P T-fi
and
a , h'—n
T—fj.
which determine the points of intersection of the lines h and h' with the sine function. Points c,
d, e, and f are found by obtaining the arcsine values of )8 and /3' . Subtracting the integral of
the sine function between points c and d and e and f, however, excludes the entire area under the
curve from the sine function to the X axis, whereas we want to exclude only that portion above
lines h and h'. Therefore, an adjustment is made resulting in the sine heat -unit function as 8
Y = [ y J [sine X + a\dx - yjisine X - p)dx - yj [sine X - p'ldxl—
which after integration leaves
Y = [y[-cos b - (-cos a) + ba - act] - y[-cos d - (-cos c) - dfi + c/3]
- y {-cos f - ( - cos e) - ffi' + efi'U -gj-
Because of possible significant variation in the heat unit requirements over different
geographical regions, the location of the tomatoes to be planted should be specified and the mean
value of heat -units required at that location for maturity calculated (Logan and Boyland 1983).
In this study the heat -unit model was applied to experimental and commercial tomato production
data near Davis, California. The mean heat -unit value for 32 observations for 1965 through 1981
was 3,135 with a standard deviation of 259.
In the annual planning model, Wednesday arbitrarily was selected to represent the week
during the processing season. A 10-year historical average of daily minimum and maximum
temperatures was then used to determine when planting should occur to provide the necessary
arrivals of harvested tomatoes during each week of processing. That is, the heat units each day
are derived starting with Wednesday of week T and going backwards in time until the mean value
of 3,135 heat units is reached. The day when the total equals or exceeds the 3,135 heat units
defines the planting day for week T's supply.
8. If the day's expected high temperature is less than h' or h, then that respective part of the follow-
ing equation is omitted.
16
Frequently, tomatoes for processing originate from different geographic regions, depending
on climate patterns as well as other factors. Harvesting generally begins in the southern part of
the Central Valley with its warmer spring temperatures and then progresses northward furing
the middle and late summer months. In scheduling potential planting dates, the model allows for
different temperature data designated for particular regions and then computes the prospective
planting date for each region using the heat-unit function.
To illustrate the heat-unit calculations, assume that Wednesday of the first week of
processing is day 201. Based on the 10-year historical average for Davis for that day, the expected
high temperature is 91.9 degrees and the expected low temperature is 54.5 degrees. Then,
_ 9L9±545 m ?a2
and
y = iUhiM = 18 . 70
73.2 - 45 , c , ^ , . .„
a = Q1 Q ^777 = 1°1 > 1> so arcsin a = tt/2
yi.y — / 0.2*
_ 80 - 73.2 _
P 91.9-73.2 _ - 36
, = _100 7&2_ = i 43 >i so restraint is not applicable
p 91.9 - 73.2
a = -tt/2
b = 3tt/2
c = .37
d = 2.77
e = not applicable
f = not applicable
Y = [18.7 -cos 3tt/2 - (-cos -tt/2 4- 1.51 (2m 1 2 - -tt/2)]
-18.7 [-cos 2.77 - (-cos .37) - .36 (2.77 + .37)]] 1/2tt
= 25.28 heat units.
17
The same procedure would be used to calculate the available heat units for days 200, 199, etc.,
until the sum of the daily heat units reaches 3,135.
Summary of Model Development
Figure 1 and the following outline demonstrate how the model functions, given the above
development. 9 The computer program, written in Fortran, is given in Appendix Table 4.
I. Input the following data:
A. Processing line numbers (LINEU7)), capacities in cases per hour (CAPU7)), can size
(CAN(17)), and coefficients to convert a case of final product of a given can size to
pounds of raw product equivalent (LAMBDAQ4)). 10
B. Number of employees in each wage class and the cost per hour for each wage class
(LABOR.DAT.).
C. Labor options for a single shift giving the cumulative number of employees in each
class as new processing lines are added sequentially to production (LON(17)). These
options are derived from the basic number of employees for the first line (labor option
A) of whole tomatoes; this number includes those general employees needed for such
things as receiving and sorting. The employees needed for the remaining whole tomato
lines are then added incrementally to this first option. Labor for the basic line for
processed products (line 8) is labor option H. The other processed products lines' labor
requirements are added incrementally to option H, which is then added to the
appropriate whole tomato labor option to find the total number of employees for a given
number of canning lines in operation. There are also labor options for operating the
processed product lines when the whole tomato lines are inactive.
D. Daily index (1 - 365) and maximum (HITEMP) and minimum (LOTEMP) temperatures
for each day.
E. Proportions of the season's raw product supply delivered each week (DISTRIB(13)).
F. Year's projected pack of raw product (X).
G. Proportions of annual raw product supply allocated to whole tomatoes (WHOLE), sauce
and puree (SAUCE), and paste (PASTE).
H. Starting date for plant operations (DAYSTART).
I. Number of weeks in the processing season for the plant (IT).
J. Expected yield of raw product in tons per acre (YIELD).
K. Unit cost (price) of cans (CANCALC), cartons (CARTCALC), and raw product per ton
(TONCOST, ADDTON).
L. Cleanup and shutdown cost for processed product lines (CLEAN).
9. Definitions of the variables are given in Table 3.
10. For computational convenience, lines 8 through 12 are renumbered as lines 13 through 17 when
the plant is producing paste only on processed products lines.
18
M. Other input requirements and their costs per unit for electricity, gas, water, lye, and
salt are written directly into the program for the various final products. The weekly
costs are then derived. These inputs, their requirements and unit costs, are given in
Table 2.
N. Similarly, other parameters used in the calculations are written directly into the
program for available productive time (.7), allowance for unusable cans and cartons
(1.005), and the heat-unit constraints (g = 45, h = 80, and h' = 100).
0. The minimum days of plant operation per week are constrained to 5 for weeks 1, 2, 12,
and 13 of the season and 6 for all others.
Calculations of costs:
A. Labor costs are calculated from the files (LABOR.DAT.) containing the cost of each
labor class and the number of employees in each class on each line. The total hourly
cost is determined for each labor option for each shift (including premium payment for
second and third shifts) (LO(17)).
B. Find the raw product equivalent capacity of each line adjusted by expected downtime
and converted to tons (Z(14)).
1. Do one set with lines 8 and 12 processing sauce and puree.
2. Do one set with all processed products lines processing paste.
3. Find hourly capacity for aggregate whole tomato product in raw product
equivalent.
4. Find hourly capacity for aggregate processed products production with lines 8 and
12 producing sauce and puree.
5. Find hourly capacity for aggregate processed products production with all
processed products lines producing paste.
C. Calculate production options (capacities) varying the hours (shifts) worked and the
number of lines used (PCX 17, 5)).
1. Define Table 1 as production options for whole tomato lines.
2. Define Table 2 as production options for processed products lines with lines 8 and
12 producing sauce and puree.
3. Define Table 3 as production options for processed products with all lines
producing paste.
4. Shifts include 1, 1.5, 2, 2.5, and 3 shifts of eight hours each (SHIFTW and
SHIFTP).
D. Define corresponding labor options in relation to the production options (LCK17)).
E. Define corresponding cleanup costs for production options.
F. Define lines worked for each production option.
G. Distribute the year's aggregate pack by the proportions of deliveries each week
(ARRIVAL).
H. Find week's pack of whole tomatoes (XWT).
1. Find week 8 aggregate pack of processed products (XPT).
J. Find days to be worked in week T given allocation of arrivals of raw product (WDAYS,
PDAYS).
19
1. Processing weeks 1, 2, 12, and 13 can have minimum of five days; all others have
minimum of six days.
K. Find average daily pack of whole tomatoes in week T (XWDT).
L. Find average daily pack of processed products in week T (XPDT).
M. If days to be worked is equal to or greater than 7, go to step Q.
N. Select various production options to be evaluated for processed product lines. (Note
step S for rule for use of Table 2 producing sauce and puree vs. Table 3 for producing
paste only.)
1. For each shift find the production option closest (but not less than) the daily
average output of processed products, thus determining the number of canning
lines to be used on that shift (e.g., search Table 2 for number of lines capable of
processing XPDT in one shift, the number of lines needed for 1.5 shifts, 2 shifts,
etc.).
2. Find appropriate labor option and hourly cost for each of the five production
options selected.
O. Select various production options to be evaluated for whole tomato production lines.
For each shift find the production option closest to (but not less than) the given daily
average output for whole tomatoes from Table 1, thus determining the number of lines
needed to work 1 shift, 1.5 shifts, 2 shifts, 2.5 shifts, and 3 shifts.
1. Find the appropriate labor option for the five production options selected and add
to the labor options found for processed products in step N. SHIFTP must be
greater than or equal to SHIFTW in any combination. (Thus, there are 15
possible feasible production combinations.)
2. Find the labor cost, including overtime if required (LABOVT), of each feasible
combination and add required cleanup cost to define feasible cost alternatives
(COSTU5)).
P. Select the lowest cost alternative from the possible 15 combinations. Some of these
combinations won't be feasible, since the capacities of the smaller number of shifts may
be less than the amount to be processed.
Q. Find the cost of production if the days to be worked 1 7. The plant will operate all
12 lines, 3 shifts per day.
1. Allocate any excess deliveries to the following week's ivals.
R. Find output of each whole tomato and processed product line in raw product equivalent
(XIJTQ7)), and convert to cases of final product (QIJTU7)).
S. Determine if season's requirements for production of sauce and puree have been
met; if so, use production option Table 3.
T. Find cost of other supplies and of raw product (GAS, ELEC, WATER, SALT, LYE,
CANCOST, CARTCOST, TOMATOES).
U. Find week's total cost (TOTAL).
V. Repeat for each week of the season.
W. Find season's total costs (TOTAL).
20
Table 3. Definition of Variables
ACRES - acres of plantings needed to supply raw product requirements in week T.
ADDTON - premium price addition for late season tomatoes ($5 per ton for first week in October,
$7.50 per ton, thereafter).
ARRIVAL - weekly arrivals of raw products (tons).
CANCALC - cost per can for various can sizes.
CANCOST - total weekly cost of cans.
CAN(17) - can size used on each line. 1
CAPQ7) - capacity of each line in cases of final product per hour.
CARTCALC - cost per carton of cartons used for various can sizes.
CARTCOST - weekly cost of cartons.
CLEAN - weekly cleanup costs (boiler start up, evaporator cleanup) associated with various
production options.
COSTQ5) - labor and cleanup costs for each feasible combination of production options.
DAYSTART - day number for beginning of processing operations.
DISTRIBQ3) - proportions of season's deliveries of raw product allocated to each week of the
season.
DLABOR - daily labor cost.
ELEC - weekly cost of electricity.
GAS - weekly cost of natural gas.
HEAT - number of heat units per day.
HITEMP(305) - average maximum temperature by days where January 1 = day no. 1.
IDAY - day of week from which planting dates are calculated.
IT - week of processing season.
LABOVT - cost of overtime work in week T.
LAMBDAQ4) - conversion coefficient for each processing line to change a case of final product
into pounds of raw product.
LINEQ7) - processing line numbers.
LON(17) - number of employees working on each line.
LOPT - labor option selected.
LOTEMPT(305) - average minimum temperature by day.
LO(17) - cost of all employees in each option working one hour.
LYE - cost of lye for processing whole tomatoes per week.
NEMPLOY(16,3) - number of employees for each cost option and shift.
PASTE - proportion of raw product to be processed as paste.
PDAYS - days required to can week's processed products.
POPT - production option selected.
jU The numbers in parentheses used with several variables indicate the number of different values
that are to be specified for that particular variable. In the case of CAN(17), for example, there are
17 can sizes to be specified; one for each canning line as defined in the program. Some can sizes
may be the same for different canning lines.
21
Table 3 continued
PCX 17, 5) - production options by line and shift.
QIJT(17) - production of final products in cases, by line in week T.
SALT - cost of salt tablets used in processing whole tomatoes in week T.
SAUCE - proportion of raw product to be processed as sauce and puree.
SHIFTP(16) - number of shifts worked by processed products lines.
SHIFTW(16) - number of shifts worked by whole tomato
TOMATOES - cost each week of raw product.
TONCOST - cost per ton of raw tomatoes.
TOTAL - total weekly cost.
WATER - weekly cost of water.
WDAYS - number of days required to can week's whole tomatoes.
WLABOR - weekly labor cost.
WHOLE - proportion of raw product to be processed as whole tomatoes.
X - year's projected pack of raw product.
XIJT - raw product equivalent processed each week by each canning line.
XPDT - average daily production of processed product in raw product equivalent in week T.
XPT - total plant production in raw product equivalent of processed products in week T.
XWDT - average daily production in raw product equivalent of whole tomatoes in week T.
XWT - total plant production in raw product equivalent of whole tomatoes in week T.
YIELD - expected yield per acre of raw product.
Z(14) - adjusted capacity in raw product equivalent of each canning line.
22
III. Calculate the needed acreage for each week's deliveries (ACRES).
IV. Calculate the planting dates for deliveries in week T using Wednesday (IDAY) as the
representative starting point deriving expected daily heat units (HEAT) from historical data.
As an illustration of how the model operates for a given week, consider the following
situation for Week 1 of a 13-week processing season (the complete season's schedule for this case
is discussed in the "Results" section.)
The plant plans to process 135,000 tons of tomatoes over the season. Based on historical
arrival patterns, for instance, 5.3 percent of the deliveries should arrive in Week 1, resulting in a
canning level for the week of 7,155 tons (135,000 x .053). One third of the week's arrivals are
allocated to whole, peeled tomatoes, or 2,361.15 tons (7,155 x .33), while the remainder, 4,793.85
tons, goes to processed products.
Operating at full capacity, the plant could process both quantities in just under three days,
but given the contractual constraints, the number of days operated is set at five. This time period
yields an average daily output of 472 tons of whole, peeled tomatoes, and 959 tons of processed
products.
The next step is to determine the labor and cleanup costs of various alternative combinations
of lines and shifts operated. Reviewing first the production options for canning the processed
products, we note that the aggregate capacity of these lines is such that operating all processed
products lines for either 1 or 1.5 shifts, 5 days will not permit all arrivals to be processed. Hence,
the first feasible production option is to work 2 shifts and use lines 8, 9, 10, and 11 with a
combined daily capacity of about 1,046 tons. 11 Working 2 shifts, 5 days for these lines results in
cleanup and boiler start-up costs of $4,540 x 5 days = $22,700.
In a similar manner, we find that operating lines 8, 9, and 10 for 2.5 shifts has cleanup costs
of $2,900 x 5 days = $14,500 and operating lines 8, 9, and 10 for 3 shifts has a single cleanup cost
of $2,900 for the week.
The feasible production options for canning the 472 tons of whole tomatoes each day are
determined by the same process using production capabilities for lines 1 through 7. Here again,
the aggregate capacity for working 1 or 1.5 shifts is not sufficient to meet the week's supply.
However, we can operate lines 1-7 for 2 shifts (capacity = 527 tons); lines 1-6 for 2.5 shifts
(capacity 503.6 tons); or lines 1-5 for 3 shifts (capacity = 546.24 tons), 5 days.
The costs of these production options are found by using combined labor and cleanup costs.
In this illustration, these costs are cost option 10 (2 shifts whole and 2 shifts processed), cost
option 11 (2 shifts whole, 2.5 shifts processed), cost option 12 (2 shifts whole, 3 shifts processed),
cost option 13 (2.5 shifts whole and 2.5 shifts processed), cost option 14 (2.5 shifts whole and 3
shifts processed), and cost option 15 (3 shifts whole and 3 shifts processed).
11. The production options are obtained by finding the actual capacities for various sequences of can-
ning lines when operating different numbers of shifts. For lines 8, 9, 10, and 11, operating 2 shifts,
this capacity is calculated from Table 1 as follows:
(KRated capacity/hour) x (pounds/case) x (.7)] divided by 12,000 pounds]) x 16 hours which yields
the following :
Line
Actual Capacity for 2 Shifts
Cumulative Capacity
8
266.9 tons
266.9 tons
9
228.8 tons
495.7 tons
10
321.9 tons
817.6 tons
11
228.8 tons
1,046.4 tons
23
Cost option 10, for example, is calculated by combining the labor costs for operating lines 1
through 11 for both the first and second shifts (the sum of the 233 employees needed per shift
times their respective wage rates). These labor costs equal $223,231 and, when added to the
cleanup costs ($22,700) yield a cost alternative of $245,931. Applying the same procedures to the
other feasible production alternatives results in cost alternatives varying from $257,422 to
$327,741 (see Table 4a). Thus, the schedule selects the option (No. 10) of working 2 shifts for
lines 1-11 for Week 1.
The production from each canning line is prorated on the basis of that line's proportion of
the total capacity of those lines being operated which produce similar products (whole or
processed). Thus, for lines 1-7 in Week 1, the total actual capacity is 32.96 tons per hour. Line 1,
for instance, has a capacity of 3.43 tons per hour, equal to 10.41 percent of the total for lines 1-7.
Line 1, therefore, is allocated 245.66 tons for the week (.1041 x 2,361.15 tons) of whole tomatoes. 12
This production level, in turn, equals 17,547 cases of final product (245.7 tons x 2,000 pounds
divided by 28 pounds per case), or 421,138 cans (17,547 x 24 cans per case).
The related costs of the other inputs are then derived by applying the cost levels presented
earlier to the production levels for this week. 13
Results
As an initial specification, the annual pack in raw product equilavent was set at 135,000
tons; the only constraint on the length of the work week was that it be at least 5 days. Examples
of the ensuing weekly schedules (as printed out by the computer) for weeks #1 and #12 are
shown in Tables 4a and 4b, respectively.
The individual weekly data show the various feasible cost (production combinations)
alternatives which can be used to process the week's pack and notes the lowest cost alternative
selected. From that point, the number of shifts worked and the number of employees per shift are
presented, and the total tonnage of raw product processed, the total output of cases of final
product, and the number of cans required, are listed for each line. The week's costs for the
various inputs are summarized and the required acreage and planting dates given. For
computational and programming convenience, lines 8 through 12 are renumbered as lines 13
through 17 when the multiple product lines (8 and 12) are producing paste.
In the example of Week #1 in Table 4a, the only feasible cost alternatives are 10 through 15.
Cost alternatives 1 through 9 are not feasible because the quantity to be processed (7,155 tons)
exceeds the capacity of the plant when working less than two shifts. Cost alternative #10
utilizing 11 canning lines for two shifts has the lowest labor and clean-up costs ($245,931).
For the smaller quantity to be processed in week #12 (2,835 tons), all cost alternatives are
feasible with the production option (cost alternative #1) of working 9 lines, one shift per day, five
days a week, having the lowest labor and cleanup costs ($123,372).
In week #1, planting date 1 uses Davis temperatures and shows a zero value reflecting a
planting date prior to February 1, a cutoff point prior to which plantings are not allowed because
of higher risk of poor weather conditions. Planting date 2 is for Fresno. Thus, the model can be
used to reflect the appropriate regions for raw product production for given times in the
processing season.
The weekly data are summarized in an annual table as illustrated in Table 5.
12. The totals presented are those from Table 4a. Rounding error may cause a slight difference from
those total figures and the results obtained using the figures shown above in parentheses.
13. Can and carton costs are inflated by the allowance for damaged or unuseable items.
24
Table 4a
WEEK # 1
TABLE: 2
DAYS WORKED: 5
WEEKLY ARRIVAL: 7155. DAILY WHOLE:
472. DAILY PROCESSED:
959.
COST #SHIFTS WHOLE
#SHIFTS PROCESSED
10
245931
2.0
2.00
11
257422
2.0
2.50
12
267390
2.0
3.00
13
288278
2.5
2.50
14
298246
2.5
3.00
15
327741
3.0
3.00
COST
ALTERNATIVE
SELECTED:
10
NUMBER OF EMPLOYEES PER SHIFT: 233
233
0
LINE
CAN SIZE
CANS
XI JT
QIJT
1
1
421138
245.66
17547.
45
2
1
541464
315.85
22561 .
01
3
1
661789
386.04
27574.
56
4
3
60162
227.56
10027.
12
5
3
120325
455. 11
20054.
23
6
2
168455
173.44
7018.
98
7
2
541464
557.48
22561 .
01
8
3
129287
1222.52
21547.
95
9
4
1058927
1048.34
22061 .
00
10
5
615655
1474.65
25652.
32
11
4
1058927
1048.34
22061 .
00
LABOR
CLEAN UP
WATER
GAS
ELECTRICITY
CARTON COSTS
CAN COSTS
LYE
SALT
TOMATOES
TOTAL 1
223231.23
22700.00
2708.26
71736.56
10388.09
41571 .00
646817.63
6847.33
11679.98
186030.00
223709.88
ACRES:
256. PLANTING DATE1 : 0 PLANTING DATE2: 34
25
Table 4b
WEEK # 12
TABLE: 3
DAYS WORKED: 5
WEEKLY ARRIVAL: 2835. DAILY WHOLE: 187. DAILY PROCESSED: 380.
COST
#SHIFTS WHOLE
#SHIFTS PROCESSED
1
123372
1 .0
1 .00
2
142654
1 .0
1 .50
3
161464
1 .0
2.00
4
182558
1.0
2.50
5
194453
1.0
3.00
6
171364
1.5
1 .50
7
190174
1.5
2.00
8
211269
1.5
2.50
9
223164
1.5
3.00
10
218432
2.0
2.00
11
239526
2.0
2.50
12
251421
2.0
3.00
13
270595
2.5
2.50
14
282489
2.5
3.00
15
309500
3.0
3.00
COST ALTERNATIVE SELECTED: 1
NUMBER OF EMPLOYEES PER SHIFT: 228 0 0
LINE
CAN SIZE
CANS
XIJT
QIJT
1
1
218441
127.42
9101 .74
2
1
280853
163.83
11702.24
3
1
343265
200.24
14302.74
4
3
31205
118.03
5201 .00
5
3
62411
236.06
10401 .99
6
2
87376
89.96
3640.70
13
3
55064
981 .85
9177.36
14
4
541202
535.79
1 1275.04
15
5
314652
753.67
13110.51
LABOR
CLEAN UP
WATER
GAS
ELECTRICITY
CARTON COSTS
CAN COSTS
LYE
SALT
TOMATOES
TOTAL
108872.81
14500.00
1073-09
26743.84
4116.04
16298.86
245869.61
2713.09
4569.80
87885.00
512641 .31
ACRES: 101. PLANTING DATE1: 150 PLANTING DATE2: 169
Table 5
ANNUAL AGGREGATE PRODUCTION PLAN FOR PROCESSING 135000 TONS OF TOMATOES
3
WEEKS
1
2
3
4
5
6
7
8
9
10
1 1
12
13
TOTAL
DAYS WORKED
5
10
16
22
28
31
10
46
52
57
62
67
72
72
SHIFTS (WHOLE)
2
3
3
3
3
3
3
3
3
3
2
1
1
NA
SHIFTS (PROCESS)
2
3
3
3
3
3
3
3
3
3
2
1
1
NA
EMPLOYEES/ SHIFT
233
233
233
233
235
235
235
235
233
231
231
228
215
NA
RAW PRODUCT
7155
11310
12825
12825
11175
11175
11175
14175
12825
9990
7155
2835
1350
1 35000
PRODUCTION (CASES)
LINE 1
17517
27811
31452
31452
31763
31763
31763
31763
31452
21500
17517
9101
8250
338172
LINE 2
22561
35757
40439
10139
11696
11696
11696
11696
40439
31500
22561
1 1702
10607
131792
LINE 3
27574
13703
49426
19126
51628
51628
51628
51628
49426
38500
27574
14302
12964
531113
LINE 1
10027
15892
17973
17973
19865
19865
19865
19865
17973
11000
10027
5200
0
1 88526
LINE 5
20051
31784
35946
35916
39730
39730
39730
39730
35946
28000
20054
10401
0
377053
LINE 6
7018
11121
12581
12581
13905
13905
13905
13905
12581
9800
7018
3640
0
1 31 968
LINE 7
22561
35757
40439
10139
11696
11696
44696
44696
40439
31500
22561
0
0
412183
LINE 6
21517
31151
38623
38623
35623
35623
35623
35623
38623
32339
23161
9177
8454
387198
LINE 9
22060
31961
39543
395 4 3
36471
36171
36471
36471
39543
39731
284 5 6
11275
0
401005
LINE 10
25652
10656
45980
45980
42409
12109
42409
42409
45980
16198
33088
131 10
0
466285
LINE. I I
31961
3954 3
395 4 3
36471
36171
36471
36471
39543
0
0
0
0
321543
LINE 12
0
0
0
0
25415
25115
25445
25445
0
0
0
0
0
101782
AVG DAILY WHOLE
172
718
705
705
779
779
779
779
705
659
472
187
89
NA
AVG DAILY PROC.
958
1519
1432
1432
1582
1582
1582
1582
1432
1338
958
379
180
NA
COSTS (DOLLARS)
LABOR
223231
335778
426976
426976
128105
128105
428105
428405
426976
332955
221 354
108872
103036
1319780
CLEAN UP
22700
1510
4540
4540
1810
4840
1810
4840
4540
2900
14500
14500
11500
103620
WATER
2708
4292
4854
4854
5365
5365
5365
5365
4854
3781
2708
1073
510
51099
GAS
71736
1 1 3695
128581
128584
116181
146181
146181
146181
128584
94240
67496
26743
12735
1357126
ELECTRICITY
10388
16464
18620
18620
20580
20580
20580
20580
18620
14504
10388
4116
1960
196001
CARTONS
11571
65886
71511
74514
81276
84 276
84 276
84276
74511
56845
40713
16298
7606
789571
CANS
616817
1025144
1159390
1 159390
1299178
1299178
1299178
1299178
1159390
864901
619456
245869
110552
12187625
LYE
6817
10852
12273
12273
13565
135>5
13565
13565
1r273
9560
6847
2713
1291
129195
SALT
11679
1851 1
20935
20935
23139
23139
23139
23139
20935
16307
11679
4569
2042
220157
TOMATOES
186030
294 84 0
333450
333150
368550
368550
368550
368550
333150
259740
186030
87885
45225
3531300
TOTAL
1223709
1890005 21 84 1 38 2 1 84 1 38 2 3 94 082
2391082
2394 082
2394082
2181138
1655735
1181173
512641
296460
22888172
ACRES NEEDED
255
405
458
158
506
506
506
506
158
356
255
101
18
1821
PLANTING DAY
1
1
1
1
1
1
1
1
1
1
1
1
1
NA
The convenience of computer simulation in testing changes in specifications, assumptions,
etc., is illustrated by constraining the processing plant to work at least six-day work weeks for
week 3 through 11 when the arrivals of fresh tomatoes may occur daily. Using the same 135,000-
ton seasonal processing goal, the only changes are in weeks 10 and 11 which in the initial run
operated only 5 days (all schedules for other weeks remain unchanged). As a result of this
change, the total season's costs increase from $22,888,472 in the base model to $22,923,106 in the
constrained version because of higher labor and cleanup costs.
One can also utilize this type of planning model to analyze the effects on average cost per
ton of raw product processed of altering the season's pack. As an example, the season's pack was
increased about 30 percent to 175,000 tons and the model was run with the work week constrained
to be no less than five days (see Table 6). Using the same weekly proportions of arrivals as the
base model, the plant worked 7 days for most of the season (weeks 2 through 9). The additional
shifts and overtime work pushed the season's labor costs up by 34 percent to $5,798,903 from
$4,319,780. Other input costs went up less proportionately; however, the cost per ton of raw
product processed dropped from $169.54 at 135,000 tons per season to $168.70 for the 175,000-ton
level.
In this manner the changes in costs associated with changes in output for the season can be
determined by running the model with several quantity alternatives.
Other possible simulation experiments can also be made with the plant operations. Wage
rates can be altered, product mixes can be varied (by altering the priority with which the canning
lines operate), and, of course, the structure of the model itself can be revised (e.g., more processing
facilities included).
Similarly, the plan generated by this model can be updated periodically prior to and during
the processing season as additional information about such factors as weather and yields becomes
available.
While the model has been developed for a particular set of plant operating conditions and
technology, (i.e., input-output coefficients), the model can be made applicable to other specific
plants and operations by changing its parameters directly. The model is deterministic in that the
season's supply of tomatoes, the weekly arrivals, farm yields, and weather data are used at their
expected value. Stochastic simulation could be developed in the context of this model to estimate
the effects of the probabilistic nature of these items on the cost of production.
28
Table 6
ANNUAL AGGREGATE PRODUCTION PLAN FOR PROCESSING 175000 TONS OF TOMATOES
WEEKS
1
2
3
1
5
C
O
1
O
0
n
y
1 n
1 u
1 1
1 1
1 3
1 3
TOTAL
DAYS WORKED
5
12
19
26
33
Ml)
117
RH
6.1
O 1
fi7
73
77
ft?
SHIFTS (WHOLE)
2
3
3
3
3
3
3
■3
3
0
J
J
c
1
NA
SHIFTS (PROCESS)
2
3
3
3
3
3
3
3
3
3
3
1
1
NA
EMPLOYEES/ SHIFT
233
233
235
235
235
235
235
^33
<=33
c3 1
C J I
220
NA
RAW PRODUCT
9275
11700
16625
16625
18375
18375
18375
18375
16625
12950
9275
3675
1750
175000
PRODUCTION (CASES)
8623
117897
LINE 1
22716
36051
10772
10772
11 160
111160
11 160
11 160
10772
31759
22716
9012
LINE 2
29215
16351
52121
52121
5291 9
5291 9
5291 9
c 0 0 1 0
3<:9 I 9
C Oll0 1
H AQO O
HUOJ J
1 1 >\ft7
1 1Aft7
1 1 UO I
33 l£yi
LINE 3
35711
56652
61070
61070
61680
64 680
61680
61680
61070
U9907
35711
14163
13551
656696
LINE 1
12998
2 0 600
23298
23298
23520
23520
O OCO A
OOCO A
OQOQft
1 R1 lift
1 O 1 HO
1 900ft
1 eyyo
O I ou
LINE 5
25996
11 201
16597
16597
ll 7/1 ll A
li *rnn n
UTAH A
H f UMU
ll 7AM n
nooy f
3 £.9 On
joe yo
pc.QQn
coyyo
1 0300
0
LINE 6
9098
1 1120
1 6308
1 6308
1 All All
1 0*1 OM
1 All All
1 OH OH
1 All All
1 All All
I OH OH
1 e f U J
Oft Oft
O AAC
JOUO
0
1 6^70Q
LINE 7
29215
16351
52121
52121
52920
co no n
5<?9c0
CO QO A
cono A
COIIOI
linft'3'3
eye ho
1 mft7
1 < J 0 1
0
526209
LINE 8
27932
•44270
ii 1 Tfln
li 1 ?cn
ll Q 1 11 0
h 0 1 **y
11 (11 110.
ll Q 1 IIQ
H 0 1 **7
11 1 92 1
3002 H
ftM 7
0 j 1 r
7flQ0
i 07V
175216
LINE 9
28597
15321
12775
II
( (3
11 no nc
ll 00 AC
li no AC
llAOOC
Hycryo
U7^^n
cicno
J u 00 1
10463
Q6QU
JOT*
51 2513
LINE 10
33253
52702
19739
li nTi n
19739
C7O OA
D fJ<?U
C.70OA
CCAll 0
c.0ftfi7
oyoo 1
1 ?1 if.7
1 CI U|
0
LINE 11
28597
15321
12775
12775
11 no nc
li no nc
ll AO AC
•*y^*n
li no nc
11 73.
H f J JO
A
u
A
u
1 niin^
0
ill UUS7
•t 1 173 1
LINE 12
0
0
29813
29e l »3
0 11 o no
3*o9<?
Oil OQO
oh i no
A
u
A
U
A
U
A
u
0
» Jl t3 1
AUG DAILY WHOLE
612
693
783
783
791
791
791
791
783
712
612
2M2
115
NA
AUG DAILY PROC.
1212
1106
1591
1 cm
1391
1033
1 833
1 033
1 033
1 £01
1 uu£
1 MHO
1 3115
1 tic
HQ?
nye
NA
nil
COSTS (DOLLARS)
LABOR
279505
566966
561995
561995
5781 31
5781 31
5781 31
5781 31
Dot> ooy
II O QOO O
M^09t 3
OOQ*70 *7
<i90(3 1
1 1 1 1 ll A
1 1 1 my
1 nco
1 U3c03
3 ( yoyu3
CLEAN UP
22700
0
0
0
0
0
0
0
0
2900
2900
22700
13000
64200
WATER
3510
5561
6292
6292
6955
6955
6955
6955
6292
1901
3510
1391
662
66239
GAS
92991
117383
171117
171117
190123
190123
190123
190123
156831
122163
87195
31667
16508
1762629
ELECTRICITY
13166
21312
21137
21137
25181
25181
25181
25181
21137
18801
1 3*»66
5335
2510
219292
CARTONS
53888
85107
98813
98813
105015
105015
105015
105015
92220
73688
52776
20385
10216
1006361
CANS
838167
1328891
152 3727
1523727
1627016
1627016
162701 6
1627016
1135250
1 121 168
802998
317265
157171
15556737
LYE
8876
11067
15910
15910
16061
16061
16061
16061
15910
12393
8876
3516
1671
161381
SALT
15110
23996
27139
27139
27397
27397
27397
27397
27139
21139
15110
5999
2720
275112
TOMATOES
211150
382 200
132250
132250
177750
177750
177750
177750
132250
336700
211 150
1 1 3925
58625
1581500
TOTAL
1569696 2575819 2861712
2861712
3051235
3051235
3051235
3051235
2755811
2112779
1527051
636336
368133
29522388
ACRES NEEDED
331
525
593
593
656
656
656
656
593
162
331
131
62
6250
PLANTING DAY
1
1
1
1
1
1
1
1
1
1
1
1
1
NA
References
Bowman, Edward H. "Production Scheduling by the Transportation Method of Linear
Programming," Operations Research, Vol. IV, No. 1, 1956.
Brandt, Jon A., Ben C. French, and Edward V. Jesse. "Economic Performance of the Processing
Tomato Industry," Giannini Foundation Information Series No. 78-1, Bulletin 1888, University
of California, April 1978.
California League of Food Processors. California Tomato Situation Annual Summary for 1981,
Sacramento, March 1982.
Dil worth, James B. Production and Operations Management, 2nd edition, (New York: Random
House) 1983.
French, B. C, L. L. Sammet, and R. G. Bressler, Jr. "Economic Efficiency in Plant Operations with
Special Reference to the Marketing of California Pears," Hilgardia (University of California),
Vo. 24 (19), July 1956, p 543-721.
Hillier, Frederick S. and Gerald J. Lieberman. Introduction to Operations Research, 3rd edition,
(San Francisco: Holden-Day, Inc.) 1980.
Holt, Charles C, Franco Modigliani, and Herbert A. Simon. "A Linear Decision Rule for
Production and Employment Scheduling," Management Science Vol. 2, No. 1, October 1955, pp.
1-10.
Logan, Samuel H. and Patricia B. Boyland. "Calculating Heat Units via a Sine Function," Journal
of The American Society for Horticultural Science, Vol. 108 (6), November 1983, pp. 977-980.
Owens, Thad 0., Jr. and E. L. Moore. "A Comparison of Various Methods of Calculating Heat Unit
Requirements of Tomato," Mississippi Agricultural and Forest Experimental Station, Technical
Bulletin 70, 1974.
Taubert, William H. Jr. "A Search Decision Rule for the Aggregate Scheduling Problem,"
Management Science, February 1968, pp. 343-359.
Went, F. W. "The Experimental Control of Plant Growth," Chronica Botanica, Vol. 17 (Waltham,
Mass.: Chronica Botanica Co.) 1957.
Went, F. W. and Lloyd Cosper. "Plant Growth Under Controlled Conditions, VI. Comparison
Between Field and Air-conditioned Greenhouse Culture of Tomatoes," American Journal of
Botany, 32, pp. 643-654, 1945.
Uyeshiro, Ronald Y. Interregional Analysis of Costs in Multiproduct Tomato Processing Plants,
M.S. Thesis, Purdue University, January 1972.
30
Appendix Table 1
Labor Classifications and Associated Hourly Wage
Rates for Tomato Processors, 1983 a
Stage and Work Classification Pay per Hour a
I. Receiving and general preparation
1. Supervisor $17.62
2. Weigh master 13.77
3. Janitor/cleanup 11.68
4. Crew leader 12.62
5. Bulk dumping worker 11.68
6. Lift driver 12.62
7. Flume control operator 11.68
8. Trash sorter 10.94
II. Preparation — whole tomatoes
9. Supervisor 16.60
10. Sorter 10.94
11. Crew leader 12.62
12. Lye peel operator 12.96
13. Janitor/cleanup 11.68
14. Ingredient supplier 11.68
15. Merry-go-round 12.62
III. Preparation — products
16. Supervisor 17.62
17. Pan operator 15.26
18. Cook's helper 12.62
19. Hot break worker 12.62
20. Finisher 12.62
21. Sauce blender 10.94
22. Janitor 10.94
23. Sorter 10.94
IV. Filling and processing — products
24. Products supervisor 15.26
25. Depalletizer 11.68
26. Can chaser 10.94
27. Seamer operator 11.68
28. Sterilizer 10.94
29. Janitor 10.94
31
Appendix Table 1 (continued)
Stage and Work Classification Pay per Hour
V. Filling and processing — whole
30. Filler $10.94
31. Crew leader 12.62
32. Seamer operator 11.68
33. Depalletizer 11.68
34. Can chaser 10.94
35. Empty can lift transporter 12.62
36. Janitor 10.94
VI. General processing
37. Cook room supervisor 17.62
38. Seamer mechanic 16.94
39. Seam checker 11.68
40. Janitor 10.94
41. Die setter 11.68
42. Greaser 12.62
43. Lid trucker 11.68
44. Red light hopper 12.62
45. Empty can shrouds 10.94
46. Cooker mechanic 16.94
47. Switchman 10.94
48. Empty can supplier 16.60
VII. General service
49. Supervisor 17.62
50. Supervisor (cleanup) 13.77
51. Boiler operator 15.26
52. Electrician 16.94
53. Cooking tower worker 12.62
54. Line mechanic 16.94
55. Sanitation worker 10.94
56. Janitor 10.94
57. Personnel clerk 10.94
58. Time keeper 10.94
59. Nurse 12.62
60. Quality control supervisor 15.26
61. Lab workers 11.68
62. Oiler/greaser 12.62
63. Screening plant worker 11.68
64. Payroll clerk 10.94
32
Appendix Table 1 (continued)
Stage and Work Classification Base Pay per Hour
VIII. New can stacking
65.
Supervisor
$1 6 fin
66.
Stock checker
12.62
67.
Palletizer
11.68
68.
Hand fork truck operator
11.68
69.
Lift truck operator
13.77
70.
Transport train operator
12.62
71.
Mechanic
17.62
72.
Mechanic's helper
12.62
73.
Cleanup worker
11.68
74.
Pack accounting clerk
12.62
75.
Stretch wrap worker
11.68
IX.
Cooling floor
76.
Stock checker
12.62
77.
Lift truck operator
13.77
X.
Pack receiving
78.
Stock checker
12.62
79.
Lift truck operator
13.77
a Includes allowances of 35 percent for fringe benefits.
33
Appendix Table 2
Labor Requirements for Sequential Use
of Tomato Processing Lines
Labor Option A
(Line No. 1
Only)
Stage
Labor
Class
Number of Employees
I.
Receiving and general preparation
Supervisor
1
1
Weigh master
2
1
Janitor/cleanup
3
2
Crew leader
4
1
Bulk dumping worker
5
2
Lift driver
6
1
Flume control operator
7
2
Trash sorter
8
28
II.
Preparation — whole tomatoes
Supervisor
9
1
Sorter
10
38
Crew leader
11
1
Lye peel operator
12
1
Janitor/ cleanup
13
2
Ingredient supplier
14
1
Merry-go-round
15
1
III.
Preparation — products
Supervisor
16
0
Pan operator
17
0
Cook's helper
18
0
Hot break worker
19
0
Finisher
20
0
Sauce blender
21
0
Janitor
22
0
Sorter
23
0
IV.
Filling and processing-products
Products supervisor
24
0
Depalletizer
25
0
Can chaser
26
o
Seamer operator
27
0
Sterilizer
28
0
Janitor
29
0
V.
Filling and processing — whole
Filler
30
15
Crew leader
31
1
Seamer operator
32
1
Depalletizer
33
4
Can chaser
34
2
Empty can lift transporter
35
1
Janitor
36
2
VI.
General processing
Cook room supervisor
37
1
Seamer mechanic
38
1
Seam checker
39
2
Janitor
40
1
Die setter
41
1
Greaser
42
1
Lid trucker
43
1
Red light hopper
44
1
Empty can shrouds
45
1
Cooker mechanic
46
1
Switchman
47
1
Empty can supplier
48
1
34
Appendix Table 2 (Coat.)
Labor Option A (Line No. 1 Only)"
Stage
Labor Class
Number of Employees
VII. General service
Supervisor
49
0
Supervisor (cleanup)
50
1
Boiler operator
51
1
Electrician
52
1
Cooking tower worker
53
1
Line mechanic
54
4
Sanitation worker
55
1
Janitor
56
2
Personnel clerk
57
1
Time keeper
58
1
Nurse
59
1
Quality control supervisor
60
1
Lab workers
61
8
Oiler/greaser
62
1
Screening plant worker
63
1
Payroll clerk
64
1
VIII. New can stacking
Supervisor
65
1
Stock checker
66
1
Palletizer
67
7
Hand fork truck operator
68
10
Lift truck operator
69
2
Transport train operator
70
1
Mechanic
71
2
Mechanic' 8 helper
72
1
Cleanup worker
73
1
Pack accounting clerk
74
1
Stretch wrap worker
75
2
IX. Cooling floor
Stock checker
76
1
Lift truck operator
77
2
X. Pack receiving
Stock checker
78
1
Lift truck operator
79
4
Given L0(A) , then L0(B)
Given L0(A) , then L0(C)
Given L0(A) , then L0(D)
Given L0(A) , then L0(E)
Given LO(A), then L0(F)
Given L0(A) , then L0(G)
L0(A) + 1 employee #8 + 1 #10 + 1 #32
L0(A) + 2 employee #8 + 2 #10 + 2 #32
L0(A) + 3 employee #8 + 4 #10 + 3 #32
L0(A) + 4 employee #8 + 6 #10 + 4 #32
L0(A) + 5 employee #8 + 7 #10 + 5 #32
L0(A) + 6 employee #8 + 8 #10 + 6 #32
The following processed products labor options are added to the option
selected from the set L0(A) through L0(G).
LO(H) adds 3 employee #8; 2 #16; 2 #17; 1 #18; 1 #19; 1 #20; 1 #21; 1 #22;
4 #23; 1 #24; 3 #25; 1 #26; 1 #27; 1 #28; and 1 #29
Given L0(H) , then L0(I) - L0(H) + 1 employee #27
Given L0(H), then L0(J) - L0(H) + 2 employee #27
Given L0(H), then L0(K) - L0(H) + 3 employee #27 + 1 #68
Given L0(H), then L0(L) - L0(H) + 4 employee #27 + 2 #68.
35
Appendix Table 3
Labor Requirements for Sequential Operations
of Processed Products Lines Only
Labor Option M (Line
No. 8 Only)
Stage
Labor Class
Number of Employees
I.
Receiving and general preparation
Supervisor
1
1
Weigh master
2
1
Janitor/ cleanup
3
2
Crew leader
4
1
Bulk dumping worker
5
1
Lift driver
6
1
Flume control operator
7
1
Trash sorter
8
8
II.
Preparation — whole tomatoes
Supervisor
9
0
Sorter
10
0
Crew leader
11
0
Lye peel operator
12
0
Janitor/ cleanup
13
0
Ingredient supplier
14
0
Merry-go-round
15
0
III.
Preparation — products
Supervisor
16
2
Pan operator
17
2
Cook's helper
18
1
Hot break worker
19
1
Finisher
20
1
Sauce blender
21
1
Janitor
22
1
Sorter
23
4
IV.
Filling and processing-products
Products supervisor
24
1
Depalletizer
25
3
Can chaser
26
1
Seamer operator
27
1
Sterilizer
28
1
Janitor
29
1
V.
Filling and processing — whole
Filler
30
0
Crew leader
31
0
Seamer operator
32
0
Depalletizer
33
0
Can chaser
34
0
Empty can lift transporter
35
0
Janitor
36
0
VI.
General processing
Cook room supervisor
37
1
Seamer mechanic
38
1
Seam checker
39
1
Janitor
40
1
Die setter
41
1
Greaser
42
1
Lid trucker
43
1
Red light hopper
44
0
Empty can shrouds
45
1
Cooker mechanic
46
0
Swi tchoan
47
1
Empty can supplier
48
1
JO
Appendix Table 3 (Coat.)
Labor Option M (Line No. 8 Only)
Stage
Labor Class
Number of Employees
UT T
V 1 1 •
General service
Su pe fv I so r
AQ
A
Qn r»o ■{ ar\r ( (<1 aa Mm *■» 1
DU yxz tVlbUl \ C iCttUU |J }
so
3 i
CI ^ f- A an
Lie cl ricitiu
53
J*.
Cooking tower worker
j J
Line me chanic
SA
OaUl LUL1UI1 WU [S.CI
.
Janitor
JO
-
reibullllcx ClciK
S7
Time keeper
DO
Nurse
RO
35
Quality control supervisor
ou
Lab workers
ftl
01
OH 1 T~ / fT r A 1 C A f
Ullci/ g I cd&c I
ft?
Screening plant worker
63
Payroll clerk
04
VIII,
New can s t ack i tig
Su pe rv i so r
Stock checker
00
r ax ie c lze r
ft7
0 /
nana i o tk. irucK operaLOL
68
Lift truck operator
ftQ
oy
Transport train operator
/u
71
Me chanic
Mechanic's helper
79
f.l panun unrkpr
73
j
Pack accounting clerk
74
Stretch wrap worker
75
IX.
Cooling floor
Stock checker
76
Lift truck operator
77
Pack receiving
Stock checker
78
1
Lift truck operator
79
2
Given L0(M), then L0(N) - L0(M) + 1 employee #27
Given L0(M) , then L0(0) - L0(M) + 2 employee #27
Given L0(M), then L0(P) - L0(M) + 3 employee #27
Given L0(M) , then L0(Q) - L0(M) + 4 employee #27.
37
Appendix Table 4
2-Mar-1984 09:01:59 VAX-
1-Sep-1983 11:57:26 DFA2
0001
0002
c
0003
c
ooot
c
0005
c
0006
0007
0008
0009
0010
0011
0012
0013
0014
0015
0016
0017
c
0018
0019
0020
0021
0022
0023
0021
0025
0026
0027
0028
0029
0030
c
0031
0032
0033
c
003 1 *
0035
c
0036
0037
c
0038
0039
c
0040
0011
c
0042
0013
c
0044
0045
c
0046
0047
c
0048
0049
0050
0051
20
0052
c
0053
c
0054
c
0055
0056
0057
PROGRAM TOMATO
WRITTEN BY C. BENGARD, PROGRAMMER FOR DATA SERVICES
AG ECONOMICS
UNIVERSITY OF CALIFORNIA
DAVIS, CALIFORNIA 95616
REAL T1,T2,T3,T4,T5,A,B,C,D,E,F,TDAYS,TLAB0R,TT0TAL,WCLEAN(16)
REAL DISTR IB ( 1 3 ) , LO ( 1 7 ) , XI JT ( 1 7 ) , QIJT ( 1 7 ) , CANC ALC ( 5 ) , CARTC ALC ( 5 )
REAL X , WHOLE , PASTE , SAUCE , ZWHOLE , ZPASTE , ZSAUCE , LYE , TONCOST , WAGE
REAL SHIFTW ( 1 6 ) .SHIFTP ( 1 6 ) , XWDT , XPDT , XWT , XPT , TXI JT ( 1 7 ) , WLABOR ( 1 6 )
REAL CAP(17),UMBDA(14),Z(14),P0(17,5),HITEMP1(305),L0TEMP1(305)
REAL TQIJT ( 1 7 ) .HITEMP2 ( 305 ) , L0TEMP2 ( 305 ) , HEAT 1 , HEAT2
INTEGER YIELD,L0PT(5),P0PT(5),CAN(17),L0N(17),CLEAN(5),0PT1(16)
INTEGER COST (16) .LINE (17) .DAYSTART , NEMPLOY ( 1 6 , 3 ) , I , K , L .TABLE
INTEGER NNEMPLOY ( 1 7 ) , NC ANS ( 5 ) , CANS (17) .TCANS (17), PT ABLE (32,14)
CHARACTER* 15 CTABLE ( 32 )
LOGICAL* 1 LOOP
CTABLE ARE HEADINGS FOR FINAL PRINT OUT
DATA CTABLE/
DAYS WORKED
EMPLOYEES/SHIFT
1
3
6
9
12
LINE
LINE
LINE
LINE
LINE
LABOR
GAS
CANS
TOMATOES
PLANTING DAY
SHIFTS (WHOLE)
RAW PRODUCT
LINE 2
LINE 4
LINE 7
LINE 10
•SHIFTS (PROCESS)'
LINE 5
LINE 8
LINE 11
AVG DAILY WHOLE', 'AVG DAILY PROC.
CLEAN UP
ELECTRICITY
LYE
TOTAL
' , ' WATER
• , • CARTONS
' , • SALT
• ,' ACRES NEEDED
DISTRIB IS WEEKLY DISTRIBUTION OF TOMATOES
DATA DISTRIB/. 053,. 084,. 095,. 095,. 105,. 105,. 105,. 105,. 095,. 074,
1 .053, .021, .01/
CLEAN IS CLEAN COSTS 1-5
DATA CLEAN/2300 , 2600 , 2900 , 4540 , 4840/
NCANS IS NUMBER OF CANS PER CASE BASED ON CAN SIZE
DATA NCANS/24,24,6,48,24/
CANCALC IS COST OF EACH CAN SIZE 1-5
DATA CANCALC/2.726,4.028,2.816,3.136,2.316/
CARTCALC IS COST OF EACH CARTON BY CAN SIZE 1-5
DATA CARTCALC/ . 1 79 , . 266 , . 226 , . 1 44 , . 1 39/
SHIFTW IS # OF WHOLE SHIFTS FOR EACH COST ALTERNATIVE 1-16
DATA SHIFTW/1, 1,1, 1,1, 1.5, 1.5, 1.5, 1.5, 2, 2, 2, 2. 5, 2. 5, 3, 3/
SHIFTP IS # OF PROCESSED SHIFTS FOR EACH COST ALTERNATIVE 1-16
DATA SHIFTP/1,1.5,2,2.5,3,1.5,2,2.5,3,2,2.5,3,2.5,3,3,3/
READ IN LINE CAPACITES
OPEN ( 1 , FILE = • CAP . DAT 1 , ST ATUS = ' OLD ' )
DO 20 1x1,14
READ ( 1 , • (5X, 11 ^4.0^9.0)' ) CAN (I ) , CAP (I ) ,LAMBDA(I )
Z ( I ) =C AP ( I ) • . 7 "LAMBDA (D/2000.
CONTINUE
CAN IS CAN SIZE, CAP IS CAPACITY IN CASES PER HOUR, LAMBDA IS
CONVERSION COEFF FOR LBS RAW PRODUCT PER CASE, Z IS RAW
PRODUCT CAPACITY IN TONS PER HOUR ~ ALL FOR EACH LINE
CAN(14) = 4
CAN(15) = 5
CAN(16) ■ 4
a The notation "C" in the left margin refers to an explanatory
comment on that line. These comments are not functioning
components of the program.
38
■
TOMATO
2-Mar-1981 09: 01): 59 VAX-
1-Sep-1983 11:57:26 DRA2
0058
CAN (17) = 2
0059
CAP(11) = 130
0060
CAP(15) = 500
0061
CAP(16) = 130
0062
CAP(17) = 125
0063
CLOSE (1 )
0061
c
CALCULATE PRODUCTION OPTIONS
0065
DO 21 1=1,7
0066
21
ZWHOLE=ZWHOLE+Z(I)
0067
DO 22 1*8,12
0068
22
ZSAUCE=ZSAUCE+Z(I)
0069
ZPASTE=Z(9)+Z(10)+Z(11)+Z(13)*Z(11)
0070
DO 30 1=1,7
0071
DO 30 K=1 ,5
0072
IF ( I . EQ . 1 ) THEN
0073
PO(I.K)sZ(I)*(4*K+4 )
0071
ELSE
0075
P0(I,K)=P0(I-1 ,K)+(Z(I)»(1«K+D)
0076
END IF
0077
30
CONTINUE
0078
DO 10 1=8,12
0079
DO 10 K=1,5
0080
IF (I.BQ.8) THEN
0081
P0(I,K)=Z(I)«(1»KV4)
0082
ELSE
0083
P0(I,K)=P0(I-1 ,K)+(Z(I)»(1»K+D)
0081
END IF
0085
10
CONTINUE
0086
DO 50 K=1,5
0087
50
PO(13,K)=Z(13) , (1 , K+1)
0088
DO 60 K=1 ,5
0089
60
PO(11,K)=PO(13,KMZ(9) , (1«K+1))
0090
DO 70 K=1 ,5
0091
70
P0( 15,K)=P0(11,KMZ(10) , (1»K-»-1))
0092
DO 80 K=1 ,5
0093
80
P0(16,K)=P0(15,K)*(Z(11)«(1»K+1))
009 1 *
DO 90 K=1,5
0095
90
P0(17,K)=P0(16,K)+(Z(11)«(1«K+D)
0096
C
0097
C
READ IN COST OF SHIFT AND # OF EMPLOYEES
0098
OPEN (2, FILE = 'LABOR . DAT ' ,STATUS= 'OLD' )
0099
C
0100
DO 102 1=1,79
0101
READ(2. ' (2X.F5.2. 1712) ' ) WAGE (NNEMPLOY(K) K=1 17)
0102
DO 100 K=1 , 17
0103
LON ( K ) =LON (K ) +NNEMPLOY ( K )
0101
LO ( K ) =L0 ( K ) ♦ ( WAGE •NNEMPLOY (K) )
0105
100
CONTINUE
0106
102
CONTINUE
0107
CLOSE (2)
0108
C
0109
C
READ IN HIGH AND LO TEMPERATURE AVERAGES
0110
OPEN ( 3 , FILE =• TEMP . DAT ' , STATUS: ' OLD • )
0111
DO 101,1=1,305
0112
101
READ(3,*(3X,1F6.1)') HITEMPKI), LOTEMP1 (I ) .HITEMP2
0113
1 L0TEMP2(I)
0111
CLOSE ( 3 )
39
TOMATO 2-Mar-1984 09:04:59 VAX-'
1 -Sep- 1983 11:57:26 DRA2:
0115 C MAIN PROGRAM
0116 X= 175000 ! SEASON'S WORTH OF TOMATOES
0117 WHOLE =.33 I PROPORTION OF PACK AS WHOLE
0118 PASTE=.5067 I PROPORTION OF PACK AS PASTE
0119 SAUCE=.1633 I PROPORTION OF PACK AS SAUCE
0120 YIELD=28 I EXPECTED YEILD PER ACRES OF TOMATOES
0121 DAYSTART=201 ! STARTING DAY : MID POINT OF WEEK 1
0122 TONCOST=26 ! COST PER TON OF TOMATOES
0123 C FOR EACH WEEK DO THE FOLLOWING CALCULATIONS
0124 DO 10 IT=1,13
0125 C INITIALIZE WEEK'S EMPLOYMENT , WHOLE OPTION # AND COST OPTIONS
0126 DO 140 1=1,16
0127 NEMPLOY(I,1)=0
0128 NEMPLOY(I,2)=0
0129 NEMPLOY(I,3)=0
0130 OPTKI) = 0
0131 140 COST(I) = 0
0132 DO 105 1=1,17
0133 LINE(I) = 0
0134 XIJT(I) = 0
0135 105 QIJT(I) = 0
0136 C CALCULATE WHETHER SAUCE (TABLE 2) OR PASTE (TABLE 3) IS PRODUCED
0137 IF (IT. EC 1 )THEN
0138 TABLE=2 ! START WITH SAUCE
0139 ELSE IF ( ( SAUCEPRO/X ) . LT . SAUCE ) THEN
0140 TABLE=2 ! HAVEN'T MET SEASON'S SAUCE QUOTA
0141 ELSE
0142 TABLE=3 ! HAVE MET SEASON'S SAUCE QUOTA
0143 END IF
0144 C ARRIVAL IS WEEKLY DISTRIBUTION OF SEASON'S TOTAL TOMATOES
0145 ARRIVAL=X«DISTRIB(IT)+DIFF
0146 XWT=WHOLE«ARRIVAL ! AMOUNT WEEK'S PACK AS WHOLE
0147 XPT=((SAUCE*PASTE)«ARRIVAL) I AMOUNT WEEK'S PACK AS PROCESSED
0148 WDAYS = XWT/(24»ZWH0LE) ! # DAYS NEEDED TO PROCESS WHOLE
0149 DIFF=0
0150 IF (TABLE . EQ . 2 )THEN ! # DAYS NEEDED FOR SAUCE OR PASTE
0151 PDAYS=XPT/(24«ZSAUCE)
0152 ELSE
0153 PDAYS=XPT/(24»ZPASTE)
0154 END IF
0155 C SET # DAYS PER WEEK FOR PLANT TO OPERATE TO MAX OF WHOLE OR PROCESSED
0156 IF( PDA YS . GT . WDAYS )WDAYS=PDAYS
0157 IF(WDAYS.LT.5)WDAYS=5
0158 IF((WDAYS.GT.5).AND.(WDAYS.LE.6))WDAYS=6
0159 IF ( WDAYS . GT . 6 )THEN
0160 DIFF = XWT-(7»PO(7,5))
0161 IF ( DIFF . GT . 0 ) THEN
0162 XWT=7 , P0(7,5)
0163 XPT=DIFF*XPT
0164 IF ( TABLE .EQ. 2 )DIFF=XPT- (7 »P0 (12,5))
0165 IF(TABLE.EQ.3)DIFF=XPT-(7«P0(17,5))
0166 IF ( DIFF . GT . 0 ) THEN
0167 IF(TABLE.EQ.2)XPT=(7»P0(12,5))
0168 IF(TABLE.EQ.3)XPT=(7«PO(17,5))
0169 ARRIVAL=XPT*XWT
0170 END IF
0171 END IF
40
TOMATO 2-Mar-1981 09:01:59 VAX-'
1-Sep-1983 11:57:26 DRA2
0172 IF (DIFF.LT.O)DIFF=0
0173 WDAYS=7
0171 END IF
0175 C CALCULATE DAILY ARRIVAL OF WHOLE , PROCESSED TOMATOES
0176 XWDT=XWT/WDAYS
0177 XPDT=XPT/WDAYS
0178 C CALCULATE PROCESSED PRODUCTION OPTIONS
0179 DO 110 Ir1,5
0180 L0PT(I)=0
0181 110 POPT(I)=0
0182 IF ( TABLE . EQ . 2 ) THEN
0183 DO 112 1=1,5
0181 DO 112 K=8,12
0185 IF(POPT(I).EQ.O)THEN
0186 IF (PO(K,I).GE.XPDT)POPT(I)=K
0187 IF (PO(K,I).GE.XPDT)LOPT(I)=K
0188 END IF
0189 112 CONTINUE
0190 END IF
0191 IF (TABLE.EQ.3)THEN
0192 DO 120 1=1,5
0193 DO 120 K=13,17
0191 IF(POPT(I).EQ.O)THEN
0195 IF (PO(K,I).GE.XPDT)POPT(I)=K
0196 IF (P0(K,I).GE.XPDT)L0PT(I)=K-5
0197 END IF
0198 120 CONTINUE
0199 END IF
0200 IF ( TABLE . EQ . 2 ) THEN
0201 IF(P0PT(5).EQ.0)P0PT(I)=12
0202 IF(LOPT(5).EQ.0)LOPT(I)=12
0203 ELSE
0201 IF(POPT(5).EQ.0)POPT(I)=17
0205 IF(LOPT(5).EQ.0)LOPT(I)=12
0206 END IF
0207 IF (WDAYS.EQ.7) GO TO 200 ! CALCULATE COST ALTERNATIVE 16
0208 LOOP = .TRUE.
0209 C CHECK OUT PRODUCTION OPTIONS 1-7 FOR 1 SHIFT WHOLE, 1-3 SHIFTS PROC
0210 DO 150 1=1,7
0211 IF (LOOP )THEN
0212 IF (P0(I,1).GE.XWDT)THEN
0213 LOOP = .FALSE.
0211 C 1 SHIFT WHOLE 1 SHIFT PROCESSED
0215 IF (POPT(D.EQ.O)THEN
0216 COST(1) = 0
0217 ELSE
0218 OPT1 ( 1 ) = I
0219 NEMPLOY (1,1) = LON(I )*LON(LOPT( 1 ) )
0220 WLABOR(1)=(LO(I)*LO(LOPT(1)))«10
0221 IF ( WDA YS . EQ . 6 ) T HEN
0222 LABOVT = ( (XWDT+XPDT )/(?0(I , 1 )fPO(POPT( 1 ) , 1 ) ) )•
0223 1 (L0(I)+L0(L0PT(1))«12)
0221 WLAB0R(1)= WLAB0R(1 )+ LABOVT
0225 END IF
0226 WCLEAN ( 1 ) = CLEAN ( LOPT ( 1 )-7 )»WDAYS
0227 COST(I) ■ WLABOR ( 1 ) +WCLEAN(1)
0228 END IF
41
TOMATO
2-Kar-1984 09:04:59 VAX-
1-Sep-1983 14:57:26 DRA2 :
0229 C 1 SHIFT WHOLE 1.5 SHIFT PROCESSED
0230 IF (P0PT(2).EQ.0)THEN
0231 C0ST(2) = 0
0232 ELSE
0233 0PTK2) = I
023^ C*L0PT(2)+5
0235 HEMPL0Y(2,1) = LON(I )+L0N(L0PT(2 ) )
0236 NEMPL0Y(2,2) = LON(C)
0237 DLABOR = (LO(I )*L0(L0PT(2 ) ) )»8
0238 DLABOR = DLABOR+(LON(C)».40MLO(C)»4 )
0239 WLAB0R(2) =DLAB0R»5
0240 IF ( WDAYS . EQ . 6 ) THEN
0241 LABOVT = ( (XWDT+XPDT) /(PO(1, 1 )+P0(P0PT(2) ,2)) )•
0242 1 (1.5)»DLAB0R
0243 WLAB0RC2) =WLAB0R(2) +LABOVT
0244 END IF
0245 WCLEAN(2) s CLEAN(L0PT(2)-7) # WDAYS
0246 C0ST(2) = WLAB0R(2) +WCLEAN(2)
0247 END IF
0248 C 1 SHIFT WHOLE 2 SHIFTS PROCESSED
0249 IF (P0PT(3).EQ.0)THEN
0250 C0ST(3) = 0
0251 ELSE
0252 OPT 1(3) = I
0253 C=LOPT(3)+5
0254 NEMPL0Y(3,D = LON(I )+L0N(L0PT(3 ) )
0255 NEMPL0Y(3,2) = LON(C)
0256 DLABOR = (LO(I )+L0(L0PT(3) ) )*8
0257 DLABOR = DLABOR+(LON(C)».80)+(LO(C)»8)
0258 WLAB0R(3) =DLAB0R»5
0259 IF ( WDAYS . EQ. 6 ) THEN
0260 LABOVT = ( (XWDT+XPDT )/(PO(I , 1 )+P0(P0PT(3) ,3) ) )•
0261 1 (1.5)»DLAB0R
0262 WLAB0R(3) =WLAB0R(3) +LAB0VT
0263 END IF
0264 WCLEAN(3) = CLEAN(LOPT(3)-7) , WDAYS
0265 COST (3) = WLAB0R(3) +WCLEAN(3)
0266 END IF
0267 C 1 SHIFT WHOLE 2.5 SHIFTS PROCESSED
0268 IF (POPT(4).EQ.0)THEN
0269 C0ST(4) = 0
0270 ELSE
0271 0PT1 (4) = I
0272 C=LOPT(4)+5
0273 NEMPL0Y(4,1) = LON(I )+L0N(L0PT(4 ) )
0274 NEMPL0Y(4,2) = LON(C)
0275 NEMPLOY(4,3) = LON(C)
0276 DLABOR = (LO(I )+L0(L0PT(4 ) ) )»8
0277 DLABOR = DLABOR+(LON(C)».60)+(LO(C)«4)
0278 DLABOR = DLABOR+(LON(C)».80)+(LO(C)«8)
0279 WLAB0R(4) =DLAB0R*5
0280 IF (WDAYS.EQ.6 )THEN
0281 LABOVT r ( (XWDT+XPDT )/(P0 (I , 1 )+P0(P0PT(4 ) ,4) ) )•
0282 1 (1.5)»DLAB0R
0283 WLAB0R(4) =WLAB0R(4) +LABOVT
0284 END IF
0285 WCLEAN(4) ■ CLEAN(LOPT(4)-7)»WDAYS
42
T0MAT0 2-Mar-1984 09:01:59 VAX-'
1-Sep-1983 14:57:26 DRA2 :
0286 COST (4) ■ WLAB0R(4) *WCLEAN(4)
0287 END IF
0288 C 1 SHIFT WHOLE 3 SHIFTS PROCESSED
0289 IF (POPT(5).EQ.0)THEN
0290 COST (5) = 0
0291 ELSE
0292 0PTK5) = I
0293 C=L0PT(5)+5
0294 NEMPL0Y(5,1) = L0N(I )+L0N(L0PT(5) )
0295 NEMPLOY(5,2) = LON(C)
0296 NEMPLOY(5,3) = LON(C)
0297 DLABOR = (L0(I )+L0(L0PT(5) ) )»8
0298 DLABOR s DLABOR+(LON(C)«.80MLO(C)»8)
0299 DLABOR = DLAB0R+(L0N(C)»1 .20)+(LO(C)«8)
0300 WLAB0R(5) =DLAB0R»5
°301 IF (WDAYS.EQ.6)THEN
°302 LABOVT = ( (XWDT*XPDT)/(PO(I , 1 )+PO(POPT(5 ) ,5 ) ) )•
0303 1 (1.5)«DLABOR
0304 WLABORC5) =WLABOR(5) +LAB0VT
0305 END IF
0306 WCLEAN(5) = CLEAN(LOPT(5)-7)
°307 COST (5) = WLAB0R(5) +WCLEANC5)
0308 END IF
0309 END IF
0310 END IF
0311 150 CONTINUE
0312 C CHECK OUT 1.5 SHIFTS WHOLE 1.5-3 SHIFTS PROCESSED
0313 LOOP = .TRUE.
0314 DO 160 1=1,7
0315 IF (LOOP) THEN
0316 IF(PO(I,2).GE.XWDT)THEN
0317 LOOP = .FALSE.
0318 C 1.5 SHIFTS WHOLE 1.5 SHIFTS PROCESSED
0319 IF (P0PT(2).EQ.0)THEN
0320 C0ST(6) = 0
0321 ELSE
0322 OPT 1(6) = I
0323 NEMPLOY(6,1) = L0N(I)*L0N(L0PT(2))
0324 NEMPL0Y(6,2) = LON(I )+L0N(L0PT(2) )
0325 DLABOR = (L0(I)+L0(L0PT(2)))»8
0326 DLABOR = DLABOR+(LON(I)«.40)+(LO(I)»4)
0327 DLABOR = DLAB0R+(LON(LOPT(2))«.40)+(LO(LOPT(2))«4)
0328 WLABOR(6) =DLABOR»5
0329 IF ( WDAYS . EQ . 6 ) THEN
0330 LABOVT = ((XWDT+XPDT)/(P0(I,2)+P0(P0PT(2),2)))»
0331 1 (1.5)«DLAB0R
0332 WLABOR(6) =WLAB0R(6) ♦LABOVT
0333 END IF
0334 WCLEAN(6) = CLEAN(LOPT(2)-7)*WDAYS
0335 C0ST(6) = WLABOR(6) -»-WCLEAN(6)
0336 END IF
0337 C 1.5 SHIFTS WHOLE 2 SHIFTS PROCESSED
0338 IF (POPT(3).EQ.O)THEN
0339 COST (7) « 0
0340 ELSE
0341 OPTK7) = I
0342 C=L0PT(3)+5
43
TOMATO
2-Mar-1981 09: OH: 59
1-Sep-1983 11:57:26
VAX-
DRA2
03^3 NEMPL0Y(7,1) = L0N(I)+L0N(L0PT(3))
0344 NEMPL0Y(7,2) = LON(I )+L0N(L0PT(3 ) )
031)5 DLABOR = (L0(I)*L0(L0PT(3)))*8
0346 DLABOR * DLABOR+(LON(I)«.40)+(LO(I)»4)
0347 DLABOR = DLABOR+(LON(LOPT(3)) , .tO)+(LO(LOPT(3)) ,1 »)
0348 DLABOR = DLAB0R+(L0N(C)».40)+ (L0(C)"4 )
0349 WLAB0R(7) = DLABOR* 5
0350 IF ( WDAYS . EQ . 6 )THEN
0351 UBOVT = ((XWDT+XPDT)/(PO(I,2)+PO(POPT(3),2)))«
0352 1 (1.5)«DLABOR
0353 WLABOR(7) =WUBOR(7) +LABOVT
0351 END IF
0355 WCLEAN(7) = CLEAN(LOPT(3)-7)»WDAYS
0356 COST(7) = WUBOR(7) +WCLEAN(7)
0357 END IF
0358 C 1.5 SHIFTS WHOLE 2.5 SHIFTS PROCESSED
0359 IF (POPT(4).EQ.0)THEN
0360 C0ST(8) = 0
0361 ELSE
0362 OPT 1(8) = I
0363 C=LOPT(4)+5
0364 NEMPLOY(8,1) = L0N(I )+L0N(L0PT(4 ) )
0365 NEMPLOY(8,2) = LONU )+LON(LOPT(4 ) )
0366 NEMPLOY(8,3) = LON(C)
0367 DLABOR = (LO(I)+LO(LOPT(4)))«8
0368 DLABOR = DLABOR+(LON(I)«.40MLO(I)«4)
0369 DLABOR = DLABOR+(LON(L0PT(4))«.40)+(LO(LOPT(4))«4)
0370 DLABOR = DLAB0R*(L0N(C)»1.00ML0(O«8)
0371 WLAB0R(8) =DLAB0R*5
0372 IF ( WDAYS . EQ . 6 ) THEN
0373 UBOVT = ((XWDT+XPDT)/(PO(I,2)+PO(POPT(4),4)))»
0374 1 (1.5)»DLABOR
0375 WLAB0RC8) =WLABOR(8) +LABOVT
0376 END IF
0377 WCLEAN(8) = CLEAN(L0PT(4)-7)»WDAYS
0378 COST (8) = WLAB0R(8) +WCLEAN(8)
0379 END IF
0380 C 1.5 SHIFTS WHOLE 3 SHIFTS PROCESSED
0381 IF (POPT(5).EQ.0)THEN
0382 COST (9) = 0
0383 ELSE
0384 OPT 1(9) = I
0385 CsLOPT(5)+5
0386 NEMPL0Y(9,D = LON(I)*LON(LOPT(5))
0387 NEMPL0Y(9,2) = LON(I)+LON(LOPT(5))
0388 NEMPL0Y(9,3) = LON(C)
0389 DLABOR = (L0(I)i-L0(L0PT(5)))»8
0390 DLABOR = DLABOR+(LON(I)».40MLO(I)»4)
0391 DLABOR = DLAB0R+(LON(LOPT(5)) , .40)+(LO(LOPT(5)) , 4)
0392 DLABOR = DLABOR+(LON(C)«1.60MLO(C)»12)
0393 WLABOR(9) =DLABOR»5
0394 IF ( WDAYS . EQ . 6 ) THEN
0395 UBOVT = ((XWDT*XPDT)/(PO(I,2)+PO(POPT(5),5)))»
0396 1 (1.5)»DLABOR
0397 WUBOR(9) =WLABOR(9) +LABOVT
0398 END IF
0399 WCLEAN(9) = CLEAN(LOPT(5)-7)
44
TOMATO 2-Mar-1984 09:0*4:59 VAX-
1 -Sep- 1983 11:57:26 DRA2
0400 C0ST(9) = WLAB0R(9) *WCLEAN(9)
0401 END IF
0402 END IF
0403 END IF
0404 160 CONTINUE
0405 C CHECK OUT 2 SHIFTS WHOLE 2-3 SHIFTS OF PROCESSED
0406 LOOP = .TRUE.
0407 DO 170 1=1,7
0408 IF (LOOP )THEN
0409 IF (P0(I,3).GE.XWDT)THEN
0410 LOOP = .FALSE.
0411 C 2 SHIFTS WHOLE 2 SHIFTS PROCESSED
0412 IF (P0PT(3).EQ.0)THEN
0413 COSTdO) = 0
0414 ELSE
0415 OPTIMO) = I
0416 NEMPL0Y(10,1) = LON(I )*L0N(L0PT(3) )
0417 NEMPL0Y(10,2) = LONU )♦ L0N(L0PT(3) )
0418 DLABOR = (LO(I )+L0(L0PT(3 ) ) )"16
0419 DLABOR = DLABOR* (LON ( I ) • . 80 )+ ( LON (LOPT ( 3 ) ) • . 80 )
0420 WLABORdO) =DLAB0R»5
0421 IF ( WDA YS . EQ . 6 ) THEN
0422 LABOVT = ( (XWDT+XPDT )/(PO(I , 3)+P0(P0PT(3) ,3) ) )•
0423 1 (1.5) # DLAB0R
0424 WLABORdO) =WLABOR(10) +LABOVT
0425 END IF
0426 WCLEAN(IO) = CLEAN(L0PT(3 )-7 )«WDAYS
0427 COST(IO) = WLABORdO) *W CLEAN (10)
0428 END IF
0429 C 2 SHIFTS WHOLE 2.5 SHIFTS PROCESSED
0430 IF (P0PT(4).EQ.0)THEN
0431 COST(II) = 0
0432 ELSE
0433 0PTK11) = I
0434 C=L0PT(4)*5
0435 NEMPL0Y(11,1) = L0N(I )i-L0N(L0PT(4) )
0436 NEMPL0Y(11,2) = L0N(I )+LON (L0PT(4) )
0437 NEMPL0Y(11,3) = LON(C)
0438 DLABOR = (LO(I )*L0(L0PT(4 ) ) )»16
0439 DLABOR = DLAB0R+(L0N(I )».80 )+(L0N(L0PT(4 ) )».80 )
0440 DLABOR = DLAB0R+(L0N(C)».60ML0(O»4)
0441 WLAB0R(11) =DLAB0R»5
0442 IF ( WDA YS . EQ . 6 )THEN
0443 LABOVT = ( (XWDT+XPDT )/(PO(I , 3)+P0(P0PT(4 ) ,4 ) ) )•
0444 1 (1.5)«DLAB0R
0445 WLAB0R(11) =WLAB0R(11) 4-LABOVT
0446 END IF
0447 WCLEAN(11) = CLEAN (LOPT ( 4 )-7 )»WDAYS
0448 C0ST(11) = WLABOR(H) +WCLEAN ( 1 1 )
0449 END IF
0450 C 2 SHIFTS WHOLE 3 SHIFTS PROCESSED
0451 IF (P0PT(5).EQ.0)THEN
0452 C0ST(12) = 0
0453 ELSE
0454 0PTK12) s I
0455 C=L0PT(5)+5
0456 NEMPL0Y(12,1) = LON(I )+L0N(L0PT(5 ) )
45
TOMATO
2-Mar-1984 09:01:59 VAX-
1-Sep-1983 14:57:26 DRA2
0157 NEMPL0YO2.2) = LON(I )+L0N(L0PT(5) )
0458 HEMPL0Y(12,3) = LON(C)
0459 DLABOR = (L0(I )+L0(L0PT(5) ) )»1 6
0460 DLABOR = DLABOR+(LON (I )».80MLON(LOPT(4 ) )».80 )
0161 DLABOR = DLABOR-t-(LON(C)«1.20)+(LO(C)«8)
0462 WLAB0R(12) sDLAB0R»5
0163 IF ( WDA YS . EQ . 6 ) THEN
0464 LABOVT = ( (XWDT+XPDT )/(PO(1 , 3 WO ( POPT ( 5 ) ,5) ) )•
0165 1 (1.5)»DLAB0R
0466 WLAB0R(12) =WLAB0R(12) +LABOVT
0467 END IF
0468 WCLEAN(12) = CLEAN(LOPT(5)-7)
0469 C0STO2) ■ WLAB0R(12) +WCLE AN (12)
0470 END IF
0471 END IF
0472 END IF
0473 170 CONTINUE
0474 C CHECK OUT 2.5 SHIFTS OF WHOLE, 2.5-3 SHIFTS OF PROCESSED
0475 LOOP = .TRUE.
0476 DO 180 1=1,7
0477 IF (LOOP) THEN
0478 IF (P0(I,4).GE.XWDT)THEN
0479 LOOP = .FALSE.
0480 C 2.5 SHIFTS WHOLE 2.5 SHIFTS PROCESSED
0481 IF (POPT(4).EQ.0)THEN
0482 C0STO3) = 0
0483 ELSE
0484 0PTK13) = I
0485 NEMPL0Y(13,D = L0N(I)+L0N(L0PT(4))
0486 NEMPL0Y(13,2) = LON(I )+L0N(L0PT(4 ) )
0487 NEMPL0Y(13,3) = L0N(I )*L0N(L0PT(4 ) )
0488 DLABOR = (LOU )+L0(L0PT(4 ) ) )»20
0489 DLABOR = DLABOR+(LON(I )«1 .40)+(LON(LOPT(4 ) )»1 .40 )
0490 WLAB0R(13) =DLAB0R*5
0491 IF ( WDAYS . EQ . 6 ) THEN
0492 LABOVT z ( (XWDT+XPDT )/(PO(I ,4 )+P0(P0PT(4 ) ,4 ) ) )•
0493 1 (1.5) , DLAB0R
0494 WLAB0RO3) =WLAB0R(13) +LABOVT
0495 END IF
0496 WCLEANH3) = CLEAN(LOPT(4)-7)»WDAYS
0497 C0STO3) = WUB0R(13) +WCLEAN ( 1 3 )
0496 END IF
0499 C 2.5 SHIFTS WHOLE 3 SHIFTS PROCESSED
0500 IF (P0PT(5).EQ.0)THEN
0501 C0ST(14) = 0
0502 ELSE
0503 0PTK14) = I
0504 C=LOPT(5)+5
0505 NEMPL0Y(14,1) = L0N(I)+L0N(L0PT(5))
0506 NEMPL0YO4.2) = LON(I)+L0N(L0PT(5))
0507 NEMPL0Y(14,3) = L0N(I )+L0N(L0PT(5) )
0508 DLABOR = (LO(I )+L0(L0PT(5) ) )«20
0509 DLABOR = DLABOR+(LON(I)»1.40)+(LON(LOPT(4))«1.40)
0510 DLABOR = DLABOR*(LON(C)».60MLO(C)«4)
0511 WLAB0R(14) sDLAB0R*5
0512 IF ( WD A YS . EQ . 6 ) THEN
0513 LABOVT = ((XWDT+XPDT)/(PO(I,4WO(POPT(5),5)))»
46
TOMATO
2-Mar-198U 09:01:59
1-Sep-1983 14:57:26
VAX-
DRA2
0514
1 (1.5) •DLABOR
0515
WLAB0R(14) =WLAB0R(14) ♦LABOVT
0516
END IF
0517
WCLEAN(14) = CLEAN(LOPT(5)-7)
0518
C0STO4) = WLAB0RO4) +WCLEAN04)
0519
END IF
0520
END IF
0521
END IF
0522
180
CONTINUE
0523
C
CHECK OUT 3 SHIFTS WHOLE, 3 SHIFTS PROCESSED
0524
LOOP = .TRUE.
0525
DO 190 1=1 ,7
0526
IF (LOOP )THEN
0527
IF (P0(I,5).GE.XWDT)THEN
0528
LOOP = .FALSE.
0529
IF (P0PT(5).EQ.0)THEN
0530
C0ST(15) = 0
0531
ELSE
0532
OPTK15) = I
0533
NEMPLOY ( 1 5 , 1 ) = LON(I)+LON(LOPT(5))
0534
NEMPL0Y(15,2) = LON (I )+L0N(L0PT(5 ) )
0535
NEMPL0YO5.3) = L0N(I)+L0N(L0PT(5))
0536
DLABOR = (L0(I )*LO(LOPT(5 ) ) )*24
0537
DLABOR = DLABOR+(LON(I )*2.00 )+(L0N(L0PT(5 ) )*2.00 )
0538
WLAB0RO5) = DLABOR* 5
0539
IF (WDAYS. EQ. 6 )THEN
0540
LABOVT = ((XWDT+XPDT)/(PO(I,5)+PO(POPT(5),5))) #
0541
1 (1.5)*DLAB0R
0542
WLAB0R(15) =WLAB0R(15) +LAB0VT
0543
END IF
0544
WCLEAN(15) = CLEAN(LOPT(5)-7)
0545
C0ST(15) = WLABOR(15) +WCLEAN ( 1 5 )
0546
END IF
0547
END IF
0548
END IF
0549
190
CONTINUE
0550
GO TO 300
0551
200
CONTINUE
0552
C
CALCULATE WORKING 7 DAYS 3 SHIFTS WHOLE, 3 SHIFTS PROCESSED
0553
OPT 1(16) = 7
0554
NEMPLOY (16,1 ) = LON(7)+LON(LOPT(5))
0555
NEMPL0Y(16,2) » LON(7 )+L0N(L0PT(5) )
0556
NEMPL0Y(16,3) = L0N(7)+L0N(L0PT(5))
0557
DLABOR = ( LO ( 7 )+L0 ( LOPT ( 5 ) ) ) »24
0558
DLABOR = ( ( LON ( 7 ) ♦LON ( LOPT ( 5 ) ) ) *2 ) +DLABOR
0559
WLAB0RM6) =DLAB0R»5«-(DLABOR«1.5)
0560
WLAB0R(16) =WLABOfl06M((XWDT*XPDT)/
0561
1 ( PO(5,7)*PO(POPT(5),5) )) •DLABOR* 1.5)
0562
COST ( 1 6 ) =WLABOR ( 1 6 )
0563
WCLEAN(16)=0
0564
300
CONTINUE
0565
C
CALCULATE SMALLEST COST ALTERNATIVE
0566
K=1
0567
DO 301 1=1,16
0568
301
IF(COST(I).GT.COST(K))K=I
0569
DO 310 1=1,16
IF ((COST(I).LT.COST(K)).AND.(COST(I).GT.0))K=I
0570
310
47
TOMATO 2-Mar-1984 09:04:59 VAX-
1 -Sep- 1983 14:57:26 DRA2
0571
C
CALCULATE WHICH WHOLE TOMATO LINES ARE OPERATING
0572
DO 320 1=1,7
0573
320
IF(I.LE.0PT1(K))LINE(I)=1
0574
302
CONTINUE
0575
SLINE=0
0576
DO 322 1=1,7
0577
322
IF (LINE (I ) .GT . 0 )SLINE=SLINE+Z ( I )
0578
DO 324 1=1,7
0579
IF(LINE(I).GT.O)XIJT(I)=XWT»Z(I)/SLINE
0580
324
IF(LINE(I).GT.O)QIJT(I)=2000»XIJT(I)/LAMBDA(I)
0581
L=5
0582
IF((K.EQ.4).0R.(K.EQ.8).0R.(K.EQ.11).0R.(K.EQ.13))L=4
0583
IF((K.EQ.3).0R.(K.EQ.7).0R.(K.EQ.10))L=3
05BU
IF((K.EQ.2).0R.(K.EQ.6))L=2
0585
IF(K.EQ.1)L=1
0586
C
CALCULATE WHICH TABLE 2 LINES ARE OPERATING
0587
IF(TABLE.EQ.2)THEN
0588
DO 330 1=8,12
0589
330
IF(I.LE.P0PT(L))LINE(I)=1
0590
SLINE=0
0591
DO 332 1=8,12
0592
332
IF (LINE ( I ) .GT . 0 )SLINE=SLINE+Z ( I )
0593
DO 334 1=8,12
0594
IF(LINE(I).GT.O)XIJT(I)=XPT»Z(I)/SLINE
0595
IF ( LINE ( I ) . GT . 0 )QIJT ( I ) =2000»XIJT ( I ) /LAMBDA ( I )
0596
334
CONTINUE
0597
C
CALCULATE WHICH TABLE 3 LINES ARE OPERATING
0598
ELSE
0599
SLINE=0
0600
DO 340 1=13,17
0601
340
IF(I.LE.P0PT(L))LINE(I)=1
0602
IF ( LINE ( 1 3 ) . GT . 0 )SLINE=SLINE*Z (13)
0603
IF ( LINE ( 1 4 ) . GT . 0 )SLINE=SLINE+Z (It)
0604
IF ( LINE ( 1 5 ) . GT . 0 )SLINE=SLINE+Z ( 9 )
0605
IF ( LINE ( 1 6 ) .GT . 0 )SLINE=SLINE+Z (10)
0606
IF ( LINE ( 1 7 ) .GT . 0 )SLINE=SLINE+Z (11)
0607
IF (LINE ( 1 3 ) .GT . 0 )XI JT ( 1 3 )=XPT«Z( 1 3 ) /SLINE
0608
IF ( LINE ( 1 3 ) . GT . 0 )QI JT ( 1 3 ) =2000^X1 JT ( 1 3 ) /LAMBDA (13)
0609
IF(LINE(14).GT.0)XIJT(14)=XPT«Z(9)/SLINE
0610
IF(LINE(14).GT.0)QIJT(14)=2000»XIJT(14)/LAMBDA(9)
0611
IF(LINE(15).GT.0)XIJT(15)=XPT»Z(10)/SLINE
0612
IF(LINE(15).GT.0)QIJT(15)=2000»XIJT(15)/LAMBDA(10)
0613
IF ( LINE ( 1 6 ) . GT . 0 )XI JT ( 1 6 ) =XPT*Z ( 1 1 ) /SLINE
0614
IF ( LINE ( 1 6 ) . GT . 0 )QI JT ( 1 6 ) =2000»XI JT ( 1 6 ) /LAMBDA (11)
0615
IF(LINE(17).GT.0)XIJT(17)=XPT«Z(14)/SLINE
0616
IF (LINE ( 17 ) .GT . 0 )QI JT ( 1 7 )=2000»XI JT (17) /LAMBDA ( 1 4 )
0617
END IF
0618
C
ACCUMULATE SEASON'S SAUCE PRODUCTION
0619
DO 345 1=1,17
0620
345
IF ( ( I . EQ . 8 ) . OR . ( I . EQ . 1 2 ) )SAUCEPR0=SAUCEPRO+XI JT ( I )
0621
ELEC=(42.532"XWT».07M10.008«XPT».07) ! COST OF ELECTRICITY
0622
IF ( TABLE . EQ . 2 ) T HEN
0623
C
COST OF GAS FOR SAUCE
0624
GAS=(17.553 , XWT».52)*(25.101»XIJT(8)».52)+(25.101»XIJT(12)».52)+
0625
1 (l8.43iniJT(9) , .52) + (l8.431 , XIJT(10)«.52)+
0626
1 (18.431 , XIJT(11)«.52)
0627
ELSE
48
—
TOMATO 2-Mar- 19811 09:04:59 VAX-
1-Sep-1983 11:57:26 DRA2
0628 C COST OF GAS FOR PASTE
0629 GAS=(17.553 , XWT».52)+(18.«31 , XPT«.52)
0630 END IF
0631 WATER=946.284».000l»ARRIVAL I COST OF WATER
0632 LYE=1.16»2.5»XWT ! COST OF LYE , THEN SALT
0633 SALT= (QIJT(1 )«2U.».003)*(QIJT(2)«2K.«.003)*(QIJT(3) , 2H.«. 0022)+
063H 1 (QIJT(H)»12.».0099)+(QIJT(5) , 12.«.0099)+
0635 1 (QIJT(6)»24.«.0053)+(QIJT(7)«24.«.0053)
0636 C CALCULATE CAN COSTS
0637 CANCOST=0
0638 DO 350 1=1,17
0639 CANS(I)=QIJT(I)«NCANS(CAN(I))
0640 350 CANCOST=CANCOST+QIJT(I)«CANCALC(CAN(I))
0611 C CALCULATE CARTON COSTS
0642 CARTCOST=0
0613 DO 360 1=1,17
0611 360 CARTCOST=CARTCOST+QIJT(I)»CARTCALC(CAN(I))
0615 C ADDTIONAL COST PER TON FOR END OF SEASON RISK FACTOR
0616 ADDTON=0
0617 IF(IT.EQ.12)ADDTON=5
0618 IF(IT.EQ.13)ADDTON=7.50
0619 C COST OF TOMATOES
0650 T0MAT0ES=ARRIVAL»(T0NC0ST+ADDT0N)
0651 C TOTAL COST
0652 TOTAL=ELEC+GAS+WATER+LYE+SALT+CANC0ST+CARTCOST*TOMAT0ES+
0653 1 COST(K)
0651 C ACRES NEEDED FOR THIS WEEK
0655 ACRES= ARRIVAL /YIELD
0656 C DAY OF WEEK TO START CALCULATING PLANTING DATE AREA 1
0657 IDA Y 1 =DAYSTART
0658 HEAT 1=0
0659 12 T1=(HITEMP1(IDAY1)+L0TEMP1(IDAY1))/2
0660 T5=HITEMP1(IDAY1)-T1
0661 T2=(T1-45)/T5
0662 T3=(80-T1)/T5
0663 T4=(100-T1)/T5
0661 IF (T2.GE.DTHEN
0665 A=-3. 1416/2
0666 ELSE
0667 A=-ASIN(T2)
0668 END IF
0669 B=3.1H6 - A
0670 IF(T3.GE.1)THEN
0671 EX1 = 0
0672 ELSE
0673 C=ASIN(T3)
0671 D=3.1H6 - C
0675 EX1= C0S(D)-C0S(C)+(D*T3)-(C»T3)
0676 END IF
0677 IF(TI.GE.I) THEN
0678 EX2 = 0
0679 ELSE
0680 E=ASIN(T4)
0681 F=3.1H6 - E
0682 EX2 = C0S(F)-C0S(E)+(F«T4)-(E»T4)
0683 END IF
0684 HEAT1=HEATU((T5/(2«3.1416))»(-C0S(B)*C0S(A)+(B»T2)-(A»T2)4-EXU
49
TOMATO 2-Mar-198U 09:04:59 VAX-
1-Sep-1983 14:57:26 DRA2
0685 1 EX2) )
0686 IDAY1 = IDAY1-1
0687 IF ( HEAT 1 .LT. 3135. AND. IDAY1 . GT . 0 )G0 TO 12
0688 C DAY OF WEEK TO START CALCULATING PLANTING DATE AREA 2
0689 IDAY2=DAYSTART
0690 HEAT2=0
0691 13 T1=(HITEMP2(IDAY2)+L0TEMP2(IDAY2))/2
0692 T5=HITEMP2(IDAY2)-T1
0693 T2=(T1-45)/T5
0691 T3=(80-T1)/T5
0695 T4=(100-T1)/T5
0696 IF (T2.GE.DTHEN
0697 A=-3. 1416/2
0698 ELSE
0699 A=-ASIN(T2)
0700 END IF
0701 B=3.1416 - A
0702 IF(T3.GE.1)THEN
0703 EX1 = 0
0704 ELSE
0705 C=ASIN(T3)
0706 D=3.1416 - C
0707 EX1 = C0S(D)-C0S(C)+(D»T3)-(C»T3)
0708 END IF
0709 IF(T4.GE.1) THEN
0710 EX2 = 0
0711 ELSE
0712 E=ASIN(T4)
0713 F=3.1416 - E
0714 EX2 = COS(F)-COS(E)+(F«T4)-(E»T4)
0715 END IF
0716 HEAT2=HEAT2+((T5/(2»3.1416))«(-C0S(B)+C0S(AH(B»T2)-(A»T2)*EX1 +
0717 1 EX2 ) )
0718 IDAY2 = IDAY2-1
0719 IF ( HEAT2 . LT . 3 1 35 . AND . IDA Y2 . GT . 0 )G0 TO 13
0720 C END OF CALCULATING PLANTING DATE LOOP
0721 DAYSTART=DAYSTART*7
0722 C :::::::::::::::::::::::::::::::::::::::::::::: tOUTPUT ;;;;;;;;;;
0723 WRITE^/U.A.W) M'.'WEEK #•, IT
0724 WRITE(6, '(1X^,12)') 'TABLE: ' .TABLE
0725 WRITE (6, ' (1X,A,I2//) ' ) 'DAYS WORKED: • .INT(WDAYS)
0726 WRITE(6,'(1X,A,F8.0,A,F7.0,A,F7.0//)') 'WEEKLY ARRIVAL: • ,
0727 1 ARRIVAL, ' DAILY WHOLE :', XWDT , • DAILY PROCESSED: ' ,
0728 1 XPDT
0729 WRITE(6,'(1X,A)') • COST #SHIFTS WHOLE #SHIFTS PROCESSED'
0730 DO 400 1=1,16
0731 400 IF(COST(I).GT.O)WRITE(6,'(1X,I2,I9,F9.1,F16.2)') I,COST(I),
0732 1 SHIFTW(I),SHIFTP(I)
0733 WRITE(6,'(1X,///,1X,A,I3)') 'COST ALTERNATIVE SELECTED: ' ,K
0734 WRITE(6,'(1X,A,3I6)') 'NUMBER OF EMPLOYEES PER SHIFT: ' ,NEMPLOY(K, 1 ) ,
0735 1 NEMPLOY(K,2),NEMPLOY(K,3)
0736 WRITE(6,'(//,1X,A)')'LINE CAN SIZE CANS XIJT QIJT'
0737 DO 410 1=1,17
0738 410 IF(LINE(I).EQ.1)WRITE(6,'(1X,I2,I9,I12,2F12.2)')I,CAN(I),
0739 1 INT(CANS(I)),XIJT(I),QIJT(I)
0740 WRITE(6,'(1X,//,1X,A,F16.2)«) 'LABOR ' , WLABOR(K)
0741 WRITE(6,'(1X,A,F13.2)') 'CLEAN UP', WCLEAN(K)
50
TOMATO
2-Mar-1984 09:04:59 VAX-
1-Sep-1983 14:57:26 DRA2
0712 WBITE(6, • ( 1X , A , F16.2) • ) 'WATER', WATER
0713 WRITE(6,'(1X,A,F18.2)') 'GAS' , GAS
0744 WRITE(6,'(1X,A,F10.2)') 'ELECTRICITY • , EL EC
07^5 WRITE(6,'(1X,A,F9.2)') ' CARTON COSTS', CARTCOST
0746 WRITE(6,'(1X,A,F12.2)') 'CAN COSTS* .CAMCOST
0747 WRITE(6,'(1X,A,F18.2)') 'LYE', LYE
07*48 WRITE(6,'(1X,A,F17.2)') 'SALT* , SALT
0719 WRITE(6,'(1X,A,F13.2)') 'TOMATOES', TOMATOES
0750 WRITE(6,'(1X,A,F16.2)') 'TOTAL', TOTAL
0751 WRITE(6, * (///1X,A,F7.0,A,I4,A,I4)' ) 'ACRES: • .ACRES,
0752 1 * PLANTING DATE1 : ' ,IDAY1 , ' PLANTING DATE2 : ' ,
0753 2 IDAY2
0751 C END OF WEEK'S WORK OF CALCULATIONS
0755 C NOW TIME FOR TOTALING
0756 TDAYS=TDAYS+WDAYS
0757 TXWT=XWT+TXWT
0758 TXPT=TXPT*XPT
0759 TLABOR =TLABOR+COST ( K )
0760 TWLABOR=TWLABOR+WLABOR(K )
0761 TWCLEAN=TWCLEAN+WCLEAN (K )
0762 DO 420 1=1,17
0763 IF(LINE(I).GT.O)THEN
0764 TCANS(I)=TCANS(I)+CANS(I)
0765 TQIJT(I)=TQIJT(I)+QIJT(I)
0766 TXIJT(I)=TXIJT(I)+XIJT(I)
0767 END IF
0768 420 CONTINUE
0769 TWATER=TWATER+WATER
0770 TGAS=TGAS+GAS
0771 TELEC=TELEC+ELEC
0772 TCARTCOST=CARTCOST+TCARTCOST
0773 TCANCOSTrTCANCOST+CANCOST
0774 TLYE=TLYE+LYE
0775 TSALT =TSALT+SALT
0776 TT0MAT0ES=TTOMATOES+TOMATOES
0777 TTOTAL=TT0TAL+T0TAL
0778 TACRES=TACRES+ACRES
0779 PTABLE ( 1 , IT )=TDAYS
0780 PTABLE(2,IT)=SHIFTW(K)
0781 PTABLE(3,IT)=SHIFTP(K)
0782 PTABLE(4,IT)=NEMPL0Y(K,1)
0783 PTABLE(5,IT)=ARRIVAL
0784 DO 430 1=1,7
0785 430 PTABLE((I*5),IT)=QIJT(I)
0786 DO 440 1=8,12
0787 IF (TABLE . EQ. 2 )PTABLE ( ( 1+5 ) , IT )=QIJT (I )
0788 440 IF(TABLE.EQ.3)PTABLE((I+5),IT)=QIJT(I+5)
0789 PTABLE(18,IT)=XWDT
0790 PTABLE(19,IT)=XPDT
0791 PTABLE (20, IT )=WLABOR(K)
0792 PTABLE(21,IT)=WCLEAN(K)
0793 PTABLE (22, IT )=WATER
0794 PTABLE(23,IT)=GAS
0795 PTABLE (24, IT )=ELEC
0796 PTABLE (25, IT) .CARTCOST
0797 PTABLE(26,IT)=CAJICOST
0798 PTABLE ( 27 , IT )=LYE
51
TOMATO
0799
0800
0801
0802
0803
0804
0805
0806
0807
0808
0809
0810
0811
0812
0813
08111
0815
0816
0817
0818
0819
0820
0821
0822
0823
0824
0825
0826
0827
0828
0629
0830
0831
0832
0833
083H
0835
0836
0837
0838
0839
0840
0841
0842
0843
0844
0845
0846
0847
0848
0849
0850
0851
0852
0853
0854
0855
2-Mar-1984 09:04:59 VAX-
1 -Sep- 1983 14:57:26 DRA2
10
c
450
131
441
460
PTABLE(28,IT)=SALT
PTABLE (29 , IT )=T0MAT0ES
PTABLE ( 30 , IT )=TOTAL
PT ABLE ( 31, IT )= ACRES
PTABLE(32,IT)=IDAY+1
CONTINUE
NOW PRINT OUT SEASON'S
WRITE (6,
WRITE(6,
WRITE(6,
WRITE(6,
DO 450 I
WRITE(6,
1
WRITE(6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE (6,
WRITE(6,
(A, A,/)') 'V
TOTAL
•SEASONS TOTALS'
(IX, A, 12//)') 'DAYS WORKED:', INT (TDAYS)
(1X,A,F12.2)') 'TOTAL COST OF LABOR:', TLABOR
(//,1X,A)') 'LINE CAN SIZE CANS QIJT
1.17
(1X,I2,I8,I13,2F13.2)')I,CAN(I),INT(TCANS(I)),TQIJT(I
TXIJT(I)
(1X,//,1X,A,F19.2)') 'LABOR', TWLABOR
(1X,A,F16.2)') 'CLEAN UP', TWCLEAN
•WATER', TWATER
'GAS', TGAS
•ELECTRICITY', TELEC
(1X,A,F12.2)') 'CARTON COSTS', TCARTCOST
(1X,A,F15.2)') 'CAN COSTS' , TCANCOST
LYE', TLYE
SALT' , TSALT
TOMATOES', TTOMATOES
XI JT'
).
(1X,A,F19.2)')
(1X,A,F21.2)')
(1X,A,F13.2)')
(1X,A,F21.2)')
(1X,A,F20.2)»)
(1X,A,F16.2)')
(1X,A,F19.2)' ) 'TOTAL', TTOTAL
(/1X,A,F7.0)')' ACRES : ' , TACRES
PRINT OUT FINAL TABLE
PTABLE (1,1 4 )=TDAYS
PTABLE (5, 14 )=X
DO 431 1=1,7
PTABLE ( ( 1 1-5 ) , 1 4 ) =TQIJT ( I )
DO 441 Ir8,12
PTABLE ( ( 1*5 ) , 1 1 ) =TQIJT ( I )«-TQI JT (1*5)
PTABLE(18,14)=TXWDT
PT ABLE ( 1 9 , 1 4 ) =TXPDT ,
PTABLE (20 , 1 ^ )= TWLABOR
PTABLE (21,14 ):TWCLEAN
PTABLE(22,14)=TWATER
PTABLE (23,1 4 )sTGAS
PTABLE(24,14)=TELEC
PTABLE (25 , 14 )=TCARTCOST
PT ABLE ( 26 , 1 4 ) =TC ANCOST
PTABLE (27,1 4 )sTL YE
PTABLE(28,14)=TSALT
PTABLE (29,14 )=TTOMATOES
PTABLE (30,1 4 )=TT0TAL
PTABLE(31,11)=TACRES
WRITE(6,'(A,40X,A,A,I8,A//)')'1','ANNUAL AGGREGATE PRODUCTION
1 ,' FOR PROCESSING', INT (X),' TONS OF TOMATOES'
WRITE(6,'(A,9X,13I8,A)') • WEEKS' , (1,1*1 , 13) , ' TOTAL'
WRITE(6,'(1X,A15, 1318,110)') CTABLEd ) ,(PTABLE(1 ,K) ,K=1 , 14)
DO 460 1=2,4
WRITE(6, , (1X,A15,13IB,A)') CTABLE(I) .(PTABLEU ,K) ,K=1 , 13) ,
1 ' NA '
WRITE(6,'(1X,A15, 1318,110)') CTABLEd) ,(PTABLE(I,K) ,K=1 , 14)
WRITE(6,«(1X,A)') 'PRODUCTION (CASES) '
DO 470 1=6, 17
PLAN '
52
TOMATO
2-Har-1984 09:04:59 VAX- 1
1-Sep-1983 14:57:26 DRA2 :
0856
470
WRITE (6,
' (1X.A15, 1318,110)') CTABLE(I),(PTABLE(1, 10,1=1, 14)
0857
WRITE(6,
•OX,/)')
0858
DO 480 I
=18,19
0859
480
WRITE (6,
'(1X,A15,13I8,A)' ) CTABLE(I),(PTABLE(I,K),K=1 ,13)
0860
1
, 1 NA'
0861
WRITE (6,
•OX,/)')
0862
WRITE (6,
•OX.A)') ' COSTS (DOLLARS)'
0863
DO 490 I
=20,30
0864
490
WRITE (6,
'(1X.A15, 1318, 110)') CTABLE(I),(PTABLE(I,K),IC=1,14)
0865
WRITE (6,
•dx,/)')
0866
WRITE (6,
'(1X.A15, 1318,110/)') CTABLE(3D,(PTABLE(31,K),K=1,14)
'(1X,A15,13I8,A/)') CTABLE(32),(PTABLE(32,K),K=1,13),
0867
WRITE (6,
0868
1
• NA'
0869
STOP
0870
END
PROGRAM SECTIONS
Name Bytes Attributes
0 ICODE 10561 PIC CON REL LCL SHR EXE RD NOWRT LONG
1 $PDATA 895 PIC CON REL LCL SHR NOEXE RD NOWRT LONG
2 1LOCAL 9984 PIC CON REL LCL NOSHR NOEXE RD WRT LONG
Total Space Allocated 21440
ENTRY POINTS
Address Type Name
0-00000000 TOMATO
VARIABLES
Address
Type
Name
2-000023E8
R»4
A
2-O0O023EC
R«4
B
2-000023F4
R»4
D
2-000023F8
R«4
E
2-000023FC
R»4
F
2-00002454
1*4
I
2-00002464
I»4
IT
2-000023D0
L»1
LOOP
2-00002494
R»4
SALT
2-000023D4
R«4
T1
2-000023E4
R»4
T5
2-000024DC
R«4
TCARTCOST
2-00002404
R»4
TLABOR
2-000024A8
R»4
TOTAL
2-000024D0
R«4
TWATER
2-000024C4
R»4
TXPT
Address
Type
Name
2-000024AC
R«4
ACRES
2-000023F0
R«4
C
2-00002450
I»4
DAYSTART
2-00002488
R«4
ELEC
2-0000248C
R«4
GAS
2-000024F4
in
IDAY
2-00002458
in
K
2-00002428
R«4
LYE
2-00002418
R«4
SAUCE
2-000023D8
R«4
T2
2-00002460
in
TABLE
2-00002400
R»4
TDAYS
2-000024E4
R»4
TLYE
2-000024E8
R»4
TSALT
2-000024CC
R«4
TWCLEAN
2-000024F8
R»4
TXWDT
Address
Type
Name
2-000024A0
R«4
ADDTON
2-00002498
R»4
CANCOST
2-00002470
R«4
DIFF
2-000024B4
R»4
EX1
2-00002444
R«4
HEAT1
2-000024BO
in
IDAY1
2-0000245C
in
L
2-00002414
R«4
PASTE
2-00002468
R»4
SAUCEPRC
2-000023DC
R»4
T3
2-000024F0
R»4
TACRES
2-000024D8
R«4
TBLEC
2-000024A4
R«4
tomatoe:
2-000024EC
R«4
TTOMATOI
2-000024C8
R«4
TWLABOR
2-000024C0
R»4
TXWT
53
TOMATO
2-Mar-1981 09:01:59 VAX-
1-Sep-1983 11:57:26 DRA2
2-00002190 R»1 WATER 2-00002171 R«1 WD AYS 2-00002110 R»1 WHOLE
2-00002138 R»1 XPDT 2-00002110 R»1 XPT 2-00002131 R f 1 XWDT
2-0000211C I«1 YIELD 2-00002120 R»1 ZPASTE 2-00002121 R»1 ZSAUCE
ARRAYS
Address
Type
Name
Bytes
Dimensions
2-0000 17F0
I«1
CAN
68
(17)
2-00000110
R«1
CANCALC
20
(5)
2-00001A68
I»1
CANS
68
(17)
2-0000026C
R»1
CAP
68
(17)
2-00000151
R«1
CARTCALC
20
(5)
2-00001878
1*1
CLEAN
20
(5)
2-0000 18CC
I«1
COST
61
(16)
2-00002 1F0
CHAR
CTABLE
180
(32)
2-00000010
R»4
DISTRIB
52
(13)
2-00000171
R»1
HITEMP 1
1220
(305)
2-00000E10
R»1
HITEMP2
1220
(305)
2-000002B0
R»1
LAMBDA
56
(11)
2-0000 190C
I'll
LINE
68
(17)
2-00000071
R»1
LO
68
(17)
2-00001831
1*1
LON
68
(17)
2-0000 17C8
I # 1
LOPT
20
(5)
2-00000938
R*1
LOTEMP 1
1220
(305)
2-0000 1301
R»1
L0TEMP2
1220
(305)
2-00001A51
I»1
NCANS
20
(5)
2-00001950
I«1
NEMPLOY
192
(16, 3)
2-00001 A 10
l*H
NNEMPLOY
68
(17)
2-00001 88C
!•<
0PT1
61
(16)
2-00000320
R«1
PO
310
(17, 5)
2-0000 17DC
POPT
20
(5)
2-0000 1AF0
m
PTABLE
1792
(32, 11)
2-000000FC
R«1
QIJT
66
(17)
2-00000 1A8
R»1
SHIFTP
61
(16)
2-00000168
R»1
SHIFTW
61
(16)
2-0000 1AAC
TCANS
68
(17)
2-00000DFC
R»1
TQIJT
68
(17)
2-00000 1E8
R»1
TXIJT
68
(17)
2-00000000
R»1
WCLEAN
61
(16)
2-0000022C
R«1
WLABOR
61
(16)
2-000000B8
R»1
XIJT
68
(17)
2-000002E8
R«1
Z
56
(11)
LABELS
Address
Label Address
Label
Address
Label
Address
Label
10
0-00001859
12
0-00001 9DD
13
n
20
30
40
•1
50
•t
60
90
100
•t
102
104
112
120
140
150
180
190
0-00001320
200
0-00001 39D
300
310
320
•1
322
•1
321
•1
331
310
•t
345
350
54
TOMATO
2-Mar-19B4 09:04:59 VAX-
1-Sep-1983 11:57:26 DRA2
410
M50
420
460
FUNCTIONS AND SUBROUTINES REFERENCED
Type Name Type Naae
FORECLOSE FOR $0 PEN
430
470
Type Name
R*4 MTH$ASIN
•t
Type Name
R«4 MTHICOS
COMMAND QUALIFIERS
FORTRAN /LIST TOMATO
/CHECK= (NOBOUNDS, OVERFLOW, NOUNDERFLOW)
/DEBUG: (NOSTMBOLS .TRACEBACK )
/STANDARD: ( NOSTNTAX , NOSOURCE_/ORM )
/SHOW: (NOPREPROCESSOR , NOINCLUDE , MAP )
/F77 /NOG_fLOATING /I4 /OPTIMIZE /WARNINGS /NOD_LINES /NOCROSS_REFERENCE /NOMACHINE.
COMPILATION STATISTICS
Run Time:
Elapsed Time:
Page Faults:
Dynamic Memory:
50.16 seconds
104.25 seconds
1008
501 pages
55
\\k% IM
m7 ^5