Transport Planning in Processing Plants for the Fruit Industry
Wladimir E. Soto-Silva
1
, Marcela C. González-Araya
2
,
Lluis M. Pla-Aragones
3
and Esteve Nadal-Roig
3
1
Programa de Doctorado en Ingeniería y Tecnologías de la Información, Universidad de Lleida, Lleida, Spain
2
Departamento de Ingeniería Industrial, Facultad de Ingeniería, Universidad de Talca,
Camino a Los Niches km 1, Curicó, Chile
3
Departamento de Matemática, Universidad de Lleida, Lleida, Spain
Keywords: Transportation Planning, Optimization, Fruit Industry, Operational Planning.
Abstract: Processing plants are central for the operation of fruit supply chains. One of the main aspects to consider is
fruit transportation to the processing plant. Hence, this work proposes a mixed integer linear programming
model to support the fruit transport planning from the storage facilities to the processing plant. The aim of
the model is to minimize the daily transportation costs and associated costs of different storage facilities
from where fruits are supplied to the plant in order to meet the demand. The model considers plant
processing capacity, fruit demand, number and type of trucks available and the inventory of fruit in each
type of storage facilities. The model was applied to a real case study of a processing plant located in the
O'Higgins Region (Chile), where reported savings only in transport costs reached about 23 percent.
1 INTRODUCTION
Catalá et al. (2013) agreed that in the recent years,
there has been an increase in the development of
optimization models to support decision making
regarding supply chain management (SCM) in
manufacturing industries. However, this increase in
proposals has not been observed in the agribusiness
sector (Plà et al., 2014). This is also confirmed by
reviews on models applied to the agribusiness sector
done by France and Thornley (1984), Glen (1987),
Lucas and Chhajed (2004), Weintraub and Romero
(2006), Ahumada and Villalobos (2009), Audsley
and Sandars (2009), Bjorndal et al. (2012) and Soto-
Silva et al. (2015).
The review of Ahumada and Villalobos (2009)
presented a classification of optimization models
proposed for the agri-food industry into two
categories: models for perishable agricultural
products (fresh products) and models for non-
perishable products. The fruit industry embraces
both products because as most of the fruit
production is consumed in fresh, there are another
important part processed (e.g canned) and consumed
as long life product.
In supply chains, storage plays an important role;
however, in the supply chain of perishable products,
this activity is critical. Hence, it is common to find
the fruit industry organized as fruit supply chains
with three main stages: production, storing and
processing-distribution (Nadal-Roig and Plà, 2015).
In this regard, Verdouw et al. (2010) stated that the
management of the supply chain of fresh fruit
requires special considerations to maintain freshness
and product quality, involving more limited delivery
times and more controlled storage conditions (low
temperature, less punts, etc.). Farmers perform
actions to minimize losses due to quality
deterioration like cool storage. Pittia et al. (1999)
and McHugh and Senesi (2000) discussed different
techniques of preserving the raw material quality
over time. These techniques refer to microbiological
and blanching treatments for preserving fruit quality
in cold storages facilities.
The development of optimization models to
support fruit supply chain management and
coordination, Blanco et al. (2005) presented a model
for production scheduling in an apples and pears
packing plant. The authors proposed a mixed integer
programming model to plan the production process,
in order to maximize profit estimated by fruit sale
incomes less raw material purchase, cold storage and
labor costs. As result, an annual schedule
considering packaging plant constraints was
Soto-Silva, W., González-Araya, M., Pla-Aragones, L. and Nadal-Roig, E.
Transport Planning in Processing Plants for the Fruit Industry.
DOI: 10.5220/0005646100710078
In Proceedings of 5th the International Conference on Operations Research and Enter prise Systems (ICORES 2016), pages 71-78
ISBN: 978-989-758-171-7
Copyright
c
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
71
developed. In a related research, Blackburn and
Scudder (2009) presented a mathematical model to
determine the loss in value of perishable agricultural
products depending on the type of storage. This
research corroborated that cold storages allow fruit
industry for a better fruit quality during postharvest.
In the literature, there are also optimization
models to support decisions in distribution and
transportation for supply chains of perishable and
non-perishable agricultural products. For instance,
Mula et al. (2006) and Mula et al. (2010) presented
reviews of the different optimization models applied
to transportation planning in supply chains. More
recently, Nadal-Roig and Plà (2015) proposed a
linear programming model that minimizes the
associated costs to transport from cooperatives to a
fruit logistic center. The model scheduled the daily
transport routes in order to satisfy the demand of the
logistic center, at a minimal cost. Furthermore, the
authors emphasized another complex task in fruit
supply chain like the planning and coordination of
different agents involved in production, processing,
storage and distribution.
Thus, it seems from literature that managing fruit
storages of different types and the coordination of a
fruit supply chain requires a sound transport
planning. Currently, optimization models to decision
support for daily transportation planning from
warehouses or storage centers, with different types
of cold storages, to the processing plants has not
been yet proposed. Even less, there are such
optimization models used in practice to the
knowledge of authors. Surprisingly, transportation
models for nearby industries of the primary sector
like forestry (Weintraub et al., 1996) or sugar
industry (Lopez-Milan and Plà-Aragonés, 2014)
have been developed successfully, but not to the
fruit industry (Oliva, 2011). So that, to cover this
gap in this paper, a mixed integer linear
programming model to find the optimal transport
planning from warehouses with different cooling
technologies to a fruit processing plant is proposed.
The model minimizes the total cost associated with
transport and proposes the opening of the cold
storage facilities grouped in warehouses. Finally,
we applied the model to a real case study in a
processing plant of Japanese plums and canned
peaches.
Therefore, this paper is structured as follows. In
section 2, the description of the problem of transport
and storage in Chile is presented. In section 3, the
proposed mathematical model for transportation
planning in the fruit industry is presented. In section
4, a case study obtained from a Chilean company for
establishing fruit transportation planning is carried
out. In section 5 are presented the conclusions as
well as further research.
2 FRUIT STORAGE AND
TRANSPORT TO PROCESSING
PLANTS
The planning and coordination of different agents
involved in production, processing, storage and
distribution are unavoidable tasks in a fruit supply
chain. In the network of fruit supply chains, each
stakeholders transport the fruit downstream to the
next level in the chain. The activities developed at
each level are briefly described below.
Harvesting is done by producers who are
generally the farms’ owners. Crops are collected
using containers, which are transported from the
farm to the storage facilities (cold storages), or
directly to the process plant. Note that the plant is
responsible for assigning the fruit destination. Each
plant purchases the fruits and takes care of his
retirement from the orchards.
The storage time depends on the fruit and
variety. For example, apples and pears, depending
on the variety, may be stored for up to 9 months. On
the contrary, cherries are not stored. They must be
processed the same day they are harvested.
However, for all stored fruit, the cooling system
must be considered to control the ripening process of
these (Nadal-Roig and Plà, 2015).
Processing is performed in processing plants
with a different degree of complexity. The simplest
process is exhibited in packing plants where fruit is
intended for fresh consumption. Minimal processing
is gaining popularity as a way to diversify the
production offered to the market.
Retailing varies depending on product quality,
added value and amount sent to the market.
Although the usual way is that retailing firms place
orders to processing plants and distribute the fruit
among consumers, it is also common for small and
local production the access to the market from
storage facilities hold by producers.
In general, the fresh fruit arrives to processing
plants from different cold storage facilities,
according to the plants and retail demands. So, an
important issue for managers is to decide which and
when a cold storage facility must be opened in order
to pick the fruit up for satisfying the demands. This
decision depends on the stored fruit and technology
of the cold storage facilities.
ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems
72
The structure of the fruit supply chain can have
different configurations, but the basic elements
inherent in the stakeholders remain the same
(Verdouw et al., 2010). For example, at the bottom
of the fruit supply chain in Chile are the farmers,
who sell their products to processing plants. The
processing plants either store the fruit in storage
centers (which are owned or rented) or process the
fruit immediately in the plant. This approach is
different from other countries like Spain, where
farmers usually are grouped in cooperatives owning
storages centers supplying to the processing plants.
In this case, there is not a change of ownership for
the fruit, because these stages are managed by a
cooperative of producers integrated in a sole
company.
Different types of cold storage facility are
available, and these are: the Conventional Cold
technology storage facility (CC), where the
temperature control is performed by a thermostat,
thereby maintaining the fruit for a period
approximately of 3 months; The Smart Fresh
technology storage facility (SF), which has a built-
diffusion system phytoregulator for protecting the
fruit from the effects of ethylene during storage,
allowing the fruit to maintain a period approximately
of 6 months; and Controlled Atmosphere technology
storage facility (CA), where the concentrations of
oxygen, carbon dioxide and nitrogen are controlled
as well as the temperature and humidity. That is, the
atmosphere in the storage facility is controlled,
thereby maintaining the fruit for a period
approximately of 9 months. Generally, plants have
more storage, in order to have fruit for processing
throughout the year. There is also possible some
changes between types of storages. For instance, the
transformation of a conventional cold storage
facility into a Smart Fresh depends on the demand
and need for each company according to their
estimated time of fruit supply.
In Chile, in each cold storage facility is only
possible to store one type of fruit at a time (Oliva,
2011). According to the Center for Natural Resource
Information (CIREN, 2005), in 2004, Chile had
approximately 201 agro-companies and 2,349 cold
storage facilities.
Quite often, the fruit transportation from
warehouses is coordinated by processing plants to
satisfy subsequently the orders they receive at the
same time from retailers. This transport must be
planned according to the availability of trucks and /
or drivers (Hsiao et al., 2010), in order to meet the
demand of different varieties. Also, it is important to
consider daily variations of processed fruit
quantities, because of unforeseen changes in demand
or in order priorities. Moreover for the fruit
transportation, a fleet of trucks is rent and the
payment thereof is made according to the number of
daily trips made. On the other hand, the availability
of a fixed number of trucks is necessary to ensure a
minimum and maximum number of daily (Oliva,
2011).
3 MODEL FOR PLANNING THE
DAILY TRANSPORTATION
STORAGE FACILITIES TO
THE PROCESSING PLANTS
The formulation of the proposed model for planning
the daily transport from the storage facilities to
processing plants model is presented. The indices,
parameters and decision variables of the model can
be found in the Appendix.
Mathematical formulation
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
(1)
s.t
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(4)
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∀ ,.
(5)
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∀.
(7)
Transport Planning in Processing Plants for the Fruit Industry
73

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,
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(8)
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,
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(10)
The objective function minimizes the costs
associated with transport from cold storage to
processing plants and the fixed costs for the opening
of the different types of storage facilities. Thus, the
objective function can be decomposed as follows:
Minimize: transportation cost + fixed cost for
opening conventional cold storage facilities + fixed
cost for opening Smart Fresh storage facilities +
fixed cost for opening controlled atmosphere storage
facilities.
The aim of the fixed costs is to prioritize the
quality of the fruit to be processed, where the
technology of cold storage facility opening is
relevant. For this reason, the model has to consider
that in conventional cold storage facilities fruit
quality deteriorates before than fruits stored in
controlled atmosphere. Thus, the fixed for opening
conventional cold storage facilities is lower than that
for Fresh and controlled atmosphere. Likewise, fixed
for opening Smart Fresh storage facilities is lower
than that for controlled atmosphere.
Constraint (2) states that the amount of fruit
removed from storages by trucks should be greater
than or equal to processing plant demand. Constraint
(3) restricts the amount of fruit transported daily
from a warehouse to a plant by a truck must be less
or equal to the capacity of the truck by the number
of trips made on the day between the warehouse and
plant. Constraint (4) shows that each truck must not
exceed the maximum number of usage hours. The
TT
ivp
parameter counts the total hours of a trip
between a warehouse and a plant, where it is
considered the truck travel time, holding time, the
loading and unloading time in both the plant and
store. The restriction (5) shows that must not exceed
a certain amount of trips per each truck, between a
store and a plant, because the production capacity of
the plants. Constraint (6) limits the maximum
number of truck trips and the constraint (7) limits the
minimum number of truck trips. Thus, the
constraints 6 and 7 balance the number of trips that
can make the trucks and therefore, maintaining a
fixed number of trucks available for transportation
daily. Constraint (8) states that shipped weight in kg
for a certain kind of fruit to a processing plant, from
a particular cold storage must be less or equal to the
stock of fruit available. Constraints (9) and (10)
correspond to no negativity and integrality of the
decision variables, respectively.
4 CASE STUDY OF A CHILEAN
COMPANY
The studied company is currently one of the fruit
leading exporters of Chile. Their orchards are
located in the Metropolitan Region and the Region
of O'Higgins.
The company stores two types of fruits in cold,
with these Japanese plums and canning peaches.
In 2013, a total of 642.0 t of Japanese plums and
9392.2 t of canning peaches stored in cold storage
facilities, which must be transported during the
season to the plant for processing. In addition, the
company has six warehouses with 62 cold storage
facilities, of which 23 have a capacity of 400 bins,
30 have a capacity of 600 bins, and nine have a
capacity of 1000 bins. It should be noted that one bin
is a reservoir with a capacity of about 400 kg. The
distribution of cold storage in warehouses according
to their cold technology is presented in Table 1.
Table 1: Number of cold storage facilities in the
warehouses according to its technology.
Storage facilities’ type
Warehouse CC SF CA Total
#1 5 - - 5
#2 2 8 - 10
#3 - 4 - 4
#4 2 7 6 15
#5 2 2 2 6
#6 10 6 6 22
Total 21 27 14 62
As it is seen in Table 1, the company has 21 CC
storage facilities (Conventional Cold), 27 SF storage
facilities (Smart Fresh) and 14 CA storage facilities
(Controlled Atmosphere). The company performs
the distribution of the fruit to cold storage
warehouses, based on the experience of the charge
of the process, taking into account the distance
between each field and warehouse, plus the
availability of cold storage facilities.
For the transportation of fruit, the company has
four trucks. These trucks are assigned to different
warehouses based on the experience of the plant
manager and production manager of the company.
Transportation decisions to the plant are made
daily, based on the orders of costumers accepted for
the day. Three different instances are considered for
ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems
74
Table 2: Processing plant demands for each analyzed day.
Day 0 Day 30 Day 90
Fruit Type Variety Variety Bins t Bins t Bins t
Plum
Japanese plums
V1
Angeleno 80 32 0 0 0 0
Japanese plums
V2
Black Diamond 0 0 0 0 0 0
Japanese plums
V3
Larry Anne 0 0 0 0 0 0
Japanese plums
V4
Saphiro 0 0 0 0 0 0
Peach
Canning peaches
V5
Andross 40 16 80 32 50 20
Canning peaches
V6
Ross Peach 20 8 40 16 30 12
Canning peaches
V7
Loadell 0 0 0 0 25 10
Canning peaches
V8
Bowen 0 0 40 16 40 16
Canning peaches
V9
Carson 40 16 35 14 35 14
Canning peaches
V10
Klampt 40 16 0 0 0 0
Canning peaches
V11
Everst 0 0 0 0 20 8
Canning peaches
V12
Hesse 0 0 15 6 15 6
Canning peaches
V13
Kakama 0 0 0 0 0 0
Canning peaches
V14
Tirrenia 40 16 0 0 0 0
Canning peaches
V15
Rizzi 0 0 0 0 0 0
Total 260 104 210 84 215 86
Table 3: Scheduled trips from the warehouses to processing for each analyzed day.
Day 0 Day 30 Day 90
Truck Truck Truck
From
# 1 # 2 # 3 # 4 # 1 # 2 # 3 # 4 # 1 # 2 # 3 # 4
W1 0 1 1 0 1 1 0 0 0 1 0 0
W4 2 0 0 0 2 0 0 0 2 0 0 0
W5 0 0 0 1 0 0 1 1 0 0 0 1
W6 2 0 0 0 0 0 0 0 0 0 1 0
# Trips
4 1 1 1 3 1 1 1 2 1 1 1
Tons
41.6 12.8 26.4 23.2 31.2 11.6 14.8 26.4 20.8 12.8 26.0 26.4
the analysis of the case study. These instances
correspond to the production planning of the
processing plant in three different days. The first
instance is the starting day of the processing fruit
period, day 0, where all the stock is available. For
the second instance, we consider the data of the day
30
th
after the beginning of operations at the plant. At
this time, the stock of stored fruit has decreased a
25%. The third instance is the day 90
th
after the
beginning of operations at the plant. At this time,
fruit available in cold storage has decreased a 59%
compared to the beginning of the season.
In Table 2, the demands of the plant for each
fruit varieties are presented in each of the proposed
instances (analyzed days).
As it is seen in Table 2, the first instance (day 0)
has a demand of 104 t, and 6 varieties of fruit are
claimed. In the second instance (day 30) the demand
is 84 t, where five types of fruit are demanded, and
in the third instance (day 90) has a demand of 86 t,
with a demand of 7 varieties of fruit.
Note that in all three scenarios some of the
varieties of fruit were not shipped, because in the
days analyzed were not required. However, these
varieties were required on other days during
planning.
The number of trips by trucks and the
warehouses which trucks should be directed are
presented in Table 3.
As it is seen in Table 3, a total of 7, 6 and 5 trips
to the processing plant are required at day 0, 30 and
90 respectively. Considering the three instances, the
truck #1 (truck type 1) is always performing most of
the trips representing the 40%, 37% and 24% of the
total number of tons transported at day 0, 30 and 90
respectively. The results concerning to the number
of fruit tons to be transported from different
warehouses to the processing plant in each truck are
presented in Table 4.
As it is seen in Table 4, 104.0 t are transported
from the storage facilities to the processing plant at
day 0, satisfying the demand of each of the varieties.
The model aims to use the fruit for those storage
facilities whose are Conventional Cold type (CC),
opening 5 cold storage facilities of this type,
followed by the opening of Smart Fresh (SF) 3
storage facilities, and finally, gives 1 controlled
atmosphere (CA) storage facilities, just to minimize
Transport Planning in Processing Plants for the Fruit Industry
75
Table 4: Transport planning from warehouses to processing plant at day 0.
Warehouse W1 W4 W 5 W 6
Type CC CC CC SF SF SF CA CC CC
Cold Storage # 1 5 20 22 25 37 40 43 46
Variety Truck #
Total (t)
V1
# 1 - - 0.8 - - - - - -
32.0
# 2 12.8 - - - - - - - -
# 3 18.4 - - - - - - - -
V5
# 4 - - - - - 16.0 - - - 16.0
V6
# 3 - 8.0 - - - - - - - 8.0
V9
# 1 - - - - 4.0 - - - 4.8
16.0
# 4 - - - - - - 7.2 - -
V10
# 1 - - - 16.0 - - - - - 16.0
V14
# 1 - - - - - - - 16.0 - 16.0
the fixed costs of opening of different types of cold
storage facilities. The opening of the CA storage
facilities is influenced because the demand for the
variety of fruit could not be satisfied with that was
stored in the standard CC and SF storage facilities.
In relation to transport costs and fixed opening costs
of cold storage, in Table 5 the results for the three
instances studied are presented.
Table 5: Transportation costs and opening fixed costs of
cold storage facilities (in $USD*).
Transportation
costs
Storage facility
opening fixed
Total
cost
Day 0
317 91
408
Day 30
244 93
337
Day 90
285 104
389
* 1 USD = 555.15 CLP, exchange rate in February 21st of 2014
(www.bcentral.cl).
Note that the instances (days 0, 30 and 90) are
real cases in the processing plant, so that the demand
and stocks at the beginning of the day are real for the
company.
Comparing the cost of transportation plans
derived from the optimal solution of each instance
versus actual planning that took place at each
studied day in the company, the first instance
reported savings of 22%, in the second scenario
savings of 26% and in the third scenario 20% saving
respectively representing an average savings of 23%
during the season.
5 CONCLUSIONS
In this research, a mathematical optimization model
with the aim to plan the daily transport from the
storage centers in the fruit processing plant,
minimizing the costs associated with transport is
presented. The model also performs an optimal
opening of the storage facilities depending on the
type presented technology associated with the
cooling of each of them. The processing plant makes
both its own and leased storage to have fruit
throughout the season progresses and thereby meets
demand.
For the Chilean case, it is important to segment
the cold storage facilities according the cool
technology, since a plan about what cold storage
facilities will be opened first for the season, in order
to ensure the quality of the fruit that is sent to
process is needed. The mathematical formulation
presented by Nadal-Roig and Plà (2015) is the basis
for the extension developed in this paper where
various processing plants and different types of
storing facilities are considered.
As the model is intended for practical use, the
computational performance of the model is analyzed
under two scenarios in which the amount of storage
centers, trucks fleet, demand, availability of fruit in
cold storage and quantity of plants vary, obtaining
the optimal solution in both cases. For the smaller
scenario (6 warehouses, 62 cold storage facilities
with 10 million kilos of stored fruit and a processing
plant), the model provided the optimal solution after
10 seconds, while for the scenario where the
parameters mentioned increased twice, the optimal
solution was reached in 3600 seconds. This shows
that for the more complex cases that could be
expected in the Chilean fruit industry, it would be
possible to deliver an optimal solution in a
reasonable computational time.
Finally, the model is applied to a real case of a
fruit processing plant in the O'Higgins Region,
Chile, which has 62 cold storage facilities, grouped
into 6 warehouses. The company processes varieties
of peaches and plums, having a total of 15 varieties.
Each season holds approximately 10 million kilos of
fruit. The model, when applied to three days
observed in the 2013 season, achieved cost savings
ICORES 2016 - 5th International Conference on Operations Research and Enterprise Systems
76
average transport of about 23 percent relative to the
value observed transport cost. Regarding the
reduction in the costs of opening cold storage, it has
been possible to demonstrate the solution delivered
by the model is consistent with the priority
established in practice for opening storage facilities.
Future extensions of the model are exploring to
incorporate the temporary nature in the transport
planning, i.e. including tactical decisions like the
transportation planning for the entire season. Hence,
the optimal size of the fleet could be assessed. This
research would require good estimates of both truck
type’s characteristics and the demand for each
variety of fruit to be processed in a season.
Finally, it would be interesting to integrate this
model into existing mathematical models that
consider operations in the orchards or the filling of
different types of cold storage, depending on the
quality of harvested fruit.
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APPENDIX
Sets, parameters and variables used in the
Transport Planning in Processing Plants for the Fruit Industry
77
formulation
The sets used by the model are the following:
P: Processing plants set.
F: Varieties of fruit stored in different storage
facilities available for the storage of products.
V: Set of trucks available.
I: Set of storages available for the fruit
C
if
: Set of Conventional Cold storage facilities in the
storage i
C
is
: Set of ‘Smart Fresh’ storage facilities in the
storage i
C
ia
: Set of Controlled Atmosphere in the storage i.
C
i
: C
if
U C
is
U C
ia
: Set of storage facilities in the
storage i
The parameters considered by the model are the
following:
D
fp
: Fuit demand f, fF, from the processing plant p,
p P.
A
v
: Maximum capacity for the truck v, vV.
TT
ivp
: Travel time that warehouse i, i
I, with the
truck v, v
V, with destination to the processing
plant p, p
P.
H
v
: Maximum number of hours of driving for the
truck v, vV.
M
v
: Maximum number of trip for the truck v, vV.
R
v
: Minimum number of trip for the truck v, vV.
W
f
: Opening fixed cost for the storage facilities type
f (Type CC: Conventional cold).
W
s
: Opening fixed cost for the storage facilities type
s (Type SF: Smart Fresh).
W
a
: Opening fixed cost for the storage facility type a
(Type CA: Controlled atmosphere).
C
ivp
: Transportation cost from the warehouse i, i
I,
with the truck v, v
V, with destination to the
processing plant p, p
P.
N: Number of trucks allowed removing fruit to the
warehouses
S
icv
: Stock available in the storage i, and storage
facility c, c
C
i
, of the fruit f, f
F.
The decision variables of the model are the
following:
X
icvfp
: kg transported from the storage i, from the
storage facility c, c
C
i
, and fruit type v, v
V,
and truck f, f
F, with destination to the
processing plant p, p P.
Y
ivp
= Number of trips from the storage i done by the
truck v,v
V, to the processing plant p, p
P.
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