A holistic approach for the design and management of an agri fresh produce supply chain, a literature review

Ho Thanh Phong et.
al. | 137

A holistic approach for the design and
management of an agri-fresh produce
supply
chain: A literature review
HO THANH PHONG
International University, Vietnam National University HCMC –


CHANDANA HEWEGE
Swinburne University of Technology –

PHAN THUY KIEU
International University, Vietnam National University HCMC –


Abstract
The paper is a systematic review of the existing literature on the
operational research models applied by previous researchers in the
context of an agri-fresh produce supply chain. The paper aims to
generate a deeper understanding of the trends and mathematical
modelling methods used in previous research by employing a
structured taxonomy of literature classification. A chain of
intermediaries tends to play a critical role in delivering agri-fresh
commodities from farmers to consumers. These intermediaries perform
various functions including the collection, preservation and delivery of
agri-fresh produce, creating linkages among all stakeholders of the
supply chain. Essentially, the study summarizes most of the technical,
organizational and functional solutions that have been used to design

optimal fresh produce supply chain management approaches. Findings
of the paper generated by applying a systematic literature review
protocol have significant practical implications and stress the crucial of
holistic approaches for the design and management of Agri-fresh
produce supply chains with a view to overcoming current challenges in
production, distribution and inventory management.
Keywords: supply chain; operational research; design; agri-fresh
produce; modelling; performance measurement.


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1. Introduction
A supply chain encompasses all activities associated with the
flow and transformation of goods from the raw material stage to
end users, as well as the associated information flows. As part of
operations, the supply chain management (SCM) also plays an
important role in maintaining the flow of materials from
processing units to the production of finished goods, and then to
end consumers (Chopra and Meinnd, 2001; 2007). While
producing perishable goods is vital to satisfying the growing
global demand for food, the supply chain management of agrifresh produce is equally crucial to delivering goods to the market
at the right time and with minimum loss of food quantities and
qualities, i.e. perishability may occur in one or more stages
across the chain (Amorim et al., 2013; Boselie et al., 2003;
Nahmias, 1982) from the extremely short processing to
transportation times (Gigler et al., 2002; Van Der Vorst et al.,
1998). Other characteristics, such as seasonality of Agriculture
production (Salin, 1998; Tsubone et al., 1983), long supply lead
times (Aramyan et al., 2006; Lowe and Preckel, 2004) and

uncertainty in harvest time due to unpredictable weather
conditions (Dorfman and Havenner, 1991; Salin, 1998), also
make the Agri-supply chain management even more complex. As
a result, the operational research of Agri-fresh produce supply
chain (APSC) has attracted considerable interest of researchers
as a mechanism to generate management efficiency through the
use of modern decision technology tools.
It is evident that the major trends related to the principles of
operational research models have spread across the scientific
literature. Over the last 20 years, many lead authors across the
world have dramatically concentrated on designing and
managing agri-fresh produce supply chains (Greenberg, 1995;
Carter, 2011; Pokharel and Mutha, 2009; Seuring and Muller,
2008; Srivastava, 2007). However, it is found that there is a
significant level of inconsistency and confusion in segmenting
fruits, flowers, and vegetables. Thus, there is an acute need for
the categorization of the produce to enhance the scalability of
the developed models. As such, this paper will adopt the
definition of Shukla and Jharkharia (2013) on agri-fresh produce
(Figure 1).


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Figure 1: Product differentiation (Shukla and Jharkharia, 2013)

In fact, this paper reviews the key literature on the operational
research models that are applied in the fresh produce supply
chains and thereby aims to generate deeper understanding of

the trends through the structured taxonomy of literature
classification. Concurrently, the authors synthesize the
progression of the previously published papers related to the
design and management of agri-fresh produce supply chains with
a view to providing directions for further studies.
The remainder of this paper is structured as follows. The next
section presents the review methodology we employed in this
study. This is followed by a detailed description of fresh produce
supply chain background. In section 4, we synthesize supply
chain models that have been developed for agri-fresh produce.
Then, we discuss the findings towards designing and managing
the fresh produce supply chain in Vietnam in section 5. Finally,
section 6 concludes this review paper.
2. Review methodology
“A literature review is a systematic, explicit, and reproducible
design for identifying, evaluating, and interpreting the existing
body of recorded documents” (Fink, 1998). Thus, the objective of
the literature review is to: (1) summarize existing research by
determining patterns and issues and (2) identify the conceptual
content of the field (Meredith, 1993).


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To synthesize the existing body of knowledge pertinent to the
design and management of the supply chain of fruits and
vegetables, the authors review various published research
articles using a combined approach of deductive and inductive
reasoning. First of all, the authors focus on some key words by
using Google Scholar, including “design and management of agrifresh produce supply chain”, “Operational models applied for fruit

and vegetable supply chain”, “harvesting”, “scheduling”,
“Mathematical
programming
models”,
“Multi-objective
programming”, “supply chain linear programming models”,
“supply chain design”, and so on. The search for these key words
results in many relevant articles and then the articles are
screened by applying criteria of Thomson Reuters, SCOPUS,
Elsevier, Wiley or Emerald and in Web of Science to avoid
reference biases and select journals that are closely related to
the key words. Finally, the authors check each paper. We follow
three common rules: (1) papers written in English language; (2)
papers consisting of decision variables modelling the production
planning or logistic of fruit and vegetable supply chains, intended
for fresh consumption and for production; (3) and even papers of
other agri-fresh produce are selected after satisfying the rules 1
& 2. Our review results in selecting papers based on their
abstracts and highlights, covering 20 review articles and 62
papers in particular.
3. Agri-fresh produce supply chains background
Lowe and Prackel (2004) reviewed the literature on
applications of decision technology tools for a selected set of
agribusiness problems in crop planning calling for more research
into solving these problems. When engaging in a pioneering
review of planning models relating to the production and
distribution of the agri-food supply chains, Ahumada and
Villalobos (2009) pointed out: (1) agri-fresh produce supply chain
are more complex and harder to manage than other supply
chains, (2) relating to preserving freshness and product quality, it

requires more limited delivery deadlines, more controlled storage
conditions, better quality of end-products and minimum losses
due to deterioration (Dabbene et al., 2008; Verdouw et al., 2010).
Audsley and Sandars (2009) revise Operations Research models
in agriculture but they limit the review to British developments.
However, these authors only synthesized holistic systems
modelling of the agricultural sector in the past and gave some
arguments to explain observed shortcomings.


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What is more, Akkerman et al. (2010) reviewed the
quantitative operations management approaches to food
distribution management, mainly focusing on three aspects: food
quality, food safety, and sustainability. Proposing a taxonomy
framework based on the following elements: supply chain
structure, decision level, modelling approach, purpose, shared
information, limitations, novelty and application, Mula et al.
(2010) review mathematical programming models for supply
chain production and transport planning. More recently, Zhang
and Wilhelm (2011) provided growers and distribution managers’
insights into the variety of decision support possible and
benchmarks for improvements in the specialty crops industry,
including vegetables, fruits, grapes and wine, ornamentals, tree
nuts, berries and dried fruits. Meanwhile, Lehmann et al. (2012)
presented three cases covering agriculture, logistics and food
awareness to overview on technology, information content,
information organization, and communication and assure the

successful utilization of the potential of the future Internet.
Next, Shukla and Jharkharia (2013) conducted a detailed
literature review covering all the operational issues related to the
fresh produce supply chain management (fruits, flowers and
vegetables). They also paid attention to the consumer
satisfaction and revenue maximization with post‐harvest waste
reduction as a secondary objective. With the product‐problem‐
methodology mapping may serve as a framework for the
managers addressing issues in FSCM. Furthermore, Tsolakis et al.
(2014) considered Agri-food Supply Chain management as a
rapidly evolving research field and offered an integrated
hierarchical decision-making framework. Concurrently, the critical
issues for the design and planning of Agri-food Supply Chain
management are defined and discussed to identify gaps and
overlaps in the Agri-food Supply Chain literature. In addition,
Kusumastuti et al. (2016) reviewed harvesting and processing in
agricultural supply chains based on the findings from a
systematic review of 76 papers. Soto-Silva et al. (2016) reviewed
Operations Research (OR) methods in fresh fruit supply chains
and come up with future perspective of OR developments in this
field of application. Moreover, Borodin et al. (2016) provided a
state of the art OR use in agriculture under uncertainty while
Paam et al. (2016) looked in to optimization of agri-fresh food
supply chains, with a focus on loss minimization in the fruits and
vegetables supply chains. Lastly, in terms of food waste, Raak et
al. (2017) pointed out causes of processing and product-related
food waste, especially new insights from expert


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interviews, whereas Balaji and Arshinder (2016) identified 16
causes of food wastage in the Indian fruits and vegetables supply
chain.
In regards to supply chain network design, Beamon (1998)
reviewed the literature in the area of multi-stage supply chain
design and analysis with a view to developing a research agenda
that may serve as a basis for future supply chain research. Shen
(2007) reviewed recent developments in the area of integrated
supply chain design, in which the decision maker needs to take
into consideration inventory costs and distribution costs when
the number and locations of the facilities are determined.
Farahani et al. (2014) covered the field of supply chain network
design (SCND), particularly in competitive environments,
throughout 200 references which categorized different SCND
problems and the potential gaps as the future research areas.
Besides, it proposes a general framework for modelling the
competitive SCND problems. Eskandarpour et al. (2015) reviewed
87 papers to address at least two dimensions of sustainable
development and give a special focus on Life-Cycle Assessment
(LCA) based models.
The articles included in Table 1 (Appendix) are obtained from
the journals listed in Table 2. One group of three journals
represents 45% of all, including European Journal of Operational
Research, International Journal of Production Economics, and
Omega which account for 20%, 15%, 10% respectively. Eleven
journals consist of the rest representing highly statistical-focused
papers.

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4. Review of Fresh produce supply chain models
4.1. The structured taxonomy
This paper follows the proposed classification of the literature
used in Ahumada and Villalobos (2009); Mula et al. (2010); SotoSilva et al. (2016).

Figure 2: Proposed taxonomy criteria

4.2. Decision levels
This review is based on the published paper of Ahumada and
Villalobos (2009b) related to three levels of the decision strategy
directly: (1) The strategic level deals with decisions regarding
product design, what to make internally and what to outsource,
supplier selection, and strategic partnering as well as well as
decisions on the number, location, and capacity of warehouses
and manufacturing plants and the flows of material through the
logistics network; (2) The tactical level includes decisions of
harvest planning, scheduling of crops, selection of labour,
capacity, etc; (3) The operational level refers to production
scheduling activities on an hour-to-hour basis, lead time
quotations, storage, routing, and truck loading (Simchi et al.,
2008). In fact,


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this classification is always unclear when dealing with mixture of
activities between strategic and tactical decisions or between

tactical and operational levels (Shukla and Jharkharia, 2013;
Farahani et al., 2014).
Table 3 (Appendix) depicts decision levels of 62 reviewed
papers. Out of these, Twenty-six articles relates to strategic
issues, thirty-eight deals with tactical levels and twenty at the
operational decisions. Whereas fourty papers have some
reference to one specific decision level, twenty-four papers
simultaneously consider aspects related to two issues in the form
of strategic-tactical or tactical-operational stages. However, there
are several papers amongst of 26 papers at the strategy level,
which mention designing and managing supply chains while most
of them consider the optimal models on business aspects. To
illustrate our synthesis, we adopt Broekmeulen (1998) approach
toward formulating a tactical decision model for managing the
operations of a distribution center. Four key decisions specify the
amount of storage space needed in each zone within a storage
facility, the storage conditions in each zone, the assignment of
each product to a zone for storage, and the storage policy.
4.3. Modeling approach
Modeling approach is driven by the nature of the inputs and
the objective of the study. A variety of mathematical approaches
are used to model and solve decision problems related to the
reviewed fruit and vegetable supply chain.


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Besides, an array of decision support models is also mentioned
in this paper with the goal of providing perspectives to models

like LCA, sampling method, Stackelberg game, Fuzzy Logic,
reference model, Triple Helix Model, and so forth. Table 5
(Appendix) shows that a wide variety of methodologies are
applied. To illustrate this further, these methodologies include the
linear programing-based modelling approach (14 papers), multiobjective linear programming (6 papers), nonlinear programming
(2 papers), simulation models (2 papers), dynamic programming
(3 papers), and HEU and HYB (4 papers). The reviewed articles in
the current study also present the inclusion of two different
methods, achieved by MILP and HEU (5 papers), SM and DP, ILP
and stochastic programming, NIP and HEU, HEU and SM, LCA and
s-LCA, information discovery and a well-structured contract
allocation methodology, innovative strategies and an Expert
System (ES) using Fuzzy Logic, an algebraic modelling language
and an optimization software of mathematical programming, the
optimal solution of the pricing and lot-sizing problem. Likewise,
the main approaches appear to be Fuzzy multi-objective
programming model, Life Cycle Assessment Analysis, Sampling
method, Stackelberg game, a novel multi-objective hybrid
approach called MHPV, Optimal policies; Cross-docking centre,
the Triple Helix Model, Target Minimization of Total Absolute
Deviation (MOTAD) model, a planning methodology, a two-stage
stochastic program, Logistics service performance, a reference
model. To be more precise, Soto-Silva et al. (2017) are the first
authors to use Optimization models and a bi-objective method in
their paper.
4.4. Published papers
The distribution of 62 revised papers has been depicted in the
table below.

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The Figure describes the distribution of journals having 62
selected papers. It is noted that five journals account for 56.45%
of the total publications, including International Journal of
Production Economics (20.97%), European Journal of Operational
Research (9.68%), Computers and Electronics in Agriculture
(8.06%), Agricultural Systems (8.06%), Journal of Food
Engineering (4.84%), and Journal of the Operational Research
Society (4.84%).
4.4.1. Novelty seeking
The novelty associated with each of the reviewed papers is
presented briefly in Table 7 below.


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Table 7
Novelty seeking associated with reviewed papers
Author
Soto-Silva et al.
(2017)
Banasik et al.
(2017)
Garofalo et al.
(2017)
Mohammed and
Wang (2017)
Keizer et al. (2017)

Catalá et al. (2016)

Tecco et al. (2016)

Lamsal et al.
(2016)

Mirmajlesi and
Shafaei (2016
Suryaningrat et al.
(2015)

Etemadnia et al.
(2015)


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Author
Mason and
Villalobos (2015)
Zhang et al. (2015)

Govindan et al.
(2014)
Cai and Zhou
(2014)
Lambert et al.
(2014)
Munhoz and

Morabito (2014)
Agustina et al.
(2014)
Qin et al. (2014)
Soysal et al. (2014)

Velychko (2014)

Manzini and
Accorsi (2013)
Tsao (2013)

Ampatzidis et al.


Author
(2013)
Teimoury et al. (
2013)
Duan and Liao
(2013)
Catalá et al. (2013)

Perdana and
Kusnandar (2012)
Haddad and
Shahwan (2012)
Amorim et al.
(2012)
Yu et al. (2012)

Tan and Çömden
(2012)
Banaeian et al.
(2012)
Ahumada et al.
(2012)

Ahumada and
Villalobos (2011a)


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Author
Rong et al. (2011)

Ahumada and
Villalobos (2011b)
van der Merwe et
al. (2011)
Bohle et al. (2010)
Hsiao et al. (2010)

Verdouw et al
(2010)
Arnaout and
Maatouk (2010)
Van Der Vorst et
al. (2009)

Blackburn and
Scudder (2009)
Dabbene et al
(2008)

Bai et al. (2008)

Cittadini et al.
(2008)

Cholette (2007)


Author
Ferrer et al. (2007)
Ortmann et al.
(2006)

Caixeta-Filho
(2006)
Blanco et al.
(2005)
Leven and
Segerstedt (2004)
Vitoriano et al.
(2003)
Hester and Cacho
(2003)
Gigler et al. (2002)

Caixeta-Filho et al.
(2002)
Munhoz and
Morabito (2001)
Broekmeulen
(1998)
Maia et al. (1997)

Starbird (1988)


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Author

Willis and Hanlon
(1976)

4.4.2. Practical Application
In terms of practical application, each paper considers
supporting practical (real case) or case study at each researched
land. Ten works use numerical examples to emphasize modelling
or resolution (none); thirty-two of them are based on real cases
and twenty focus on case studies. Besides, Table 8 (Appendix)
also depicts types of agri-fresh produce and the projected
country/countries.
4.5. Performance Measurement in agri-fresh produce
supply chain
To design and manage a supply chain effectively, appropriate
performance measures need to be developed and applied.

Measurement of supply chain performance gives decision makers
inside (producers, distributors or marketers) and outside (policy
makers or investors) full information for decision making, policy
development, or redesigning. A wide spectrum of supply chain
performance measures are discussed in the literature covering
areas such as customer satisfaction, services, responsiveness,
costs, categories of either qualitative and quantitative measures
(Beamon, 1996; 1998). However, this paper focuses on the types
of performance measures relevant for fresh agricultural products
supply chains, more specifically relating to fruits, vegetables and
flowers. The performance measures also focus on the behaviour
of actors such as growers, retailers, wholesalers and exporters.
The main operations consist of productions, storage, packing,
transportation and trading of these produce. Syntheses from the
analysis of the authors (Beamon ,1999; van der spiegel, 2004;
Lohman et al., 2004; Gunasekaran et al., 2004; van der vorst,
2006), Aramyan et al. (2006) classify performance measurement
as Table 9 (Appendix) below.
In this paper, the authors review and analyze performance
measurement in each reviewed article to determine the
efficiency or/and flexibility or/and responsiveness or/and food
quality, aimed at seeking the establishment of approciate, as an
important component in supply chain design and management.
As shown in Table 10 (Appendix), fourty-three papers focus on
efficiency indicators. Among them, cost minimization is


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the most widely used objective for an entire supply chain (total
cost) or for particular business units or stages. For example,
Soto-Silva et al. (2017) pointed out an average reduction of 8% in
total cost when supporting fresh produce supply chain. Moreover,
Etemadnia et al. (2015) declared that there was always a tradeoff between transportation costs and fixed costs of building hub
capacity to control excess capacity because of insufficient
quantities of food. Next, profit is considered in some papers, for
instance, Mirmajlesi and Shafaei (2016) showed the maximizing
profit is an indication of the effectiveness of the model or Zhang
et al. (2015) demonstrated the revenue sharing and cooperative
investment contract coordinates the supply chain. Moreover,
according to Ahumada et al. (2012), for the same level of risk
experimented by the producer, planning based on the proposed
stochastic models rendered increases expected profit to over
50%. At the same time when risk aversion policies are
implemented, the expected losses decrease significantly over
those recommended by deterministic planning models.
Based on the specification of Agri-fresh production, the
indicators reflect quality, divided into intrinsic (product) and
extrinsic (process) quality indicators by Luning et al. (2002). First
of all, the quality aspects of product consist of (1) shelf life
constraints for raw materials and perishability of products,
intermediates and finished products, and changes in product
quality level while progressing through the supply chain, such as
taste, color, appearance, texture and sound, (2) product safety
increased consumer attention concerning both product and
method of production, no risks for the consumer of foods are
allowed while health refers to food composition and diet, and
(3) product reliability refers to the compliance of actual product

composition with product description and convenience shows the
ease of use or consumption of the product for the consumer (Van
der Spiegel, 2004). For example, Suryaningrat et al. (2015)
depicted the details of flow of material, financial and information
are identified in this research and add value of selected products
of cassava, or Lambert et al. (2014) demonstrated from the
results which have an increase in production yield and fruit
quality in the orchard, or followed by van der Merwe et al. (2011),
selecting the best possible dates to harvest the respective
vineyard blocks in order to preserve grape quality. Secondly, the
quality aspects of process include (1) Production system refers
the way of the production throughout time (production of new or
additional products,


(2) Environmental issues, and (3) perceived quality, also relevant

for food application, e.g. advertisements or brands (marketing)
can have a considerable influence on quality


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perception. For instance, Ortmann et al. (2006) illustrated that
the infrastructure of the South African fresh fruit export industry
is more than adequate (infrastructure utilization was only
approximately 55% during the winter and 40% during the
summer) to accommodate export levels planned for 2003 as well
as those expected in ensuing years, or Garofalo et al. (2017)
pointed out the composting of waste and compost as the

fertilizer improves the environmental performance of the supplychain.
By the same token, fourty papers mention more than one
performance indicator. It is worth highlighting in relation to
Quality (Q) and Efficiency (E) in twenty-three papers, six papers
with Responsiveness (R) and Quality (Q), five with Efficiency (E)
and Responsiveness (R), and one examines Flexibility (F) and
Responsiveness (R) of Hsiao et al. (2010). Moreover, this current
review consists of two papers emphasizing three features,
Efficiency, Flexibility and Quality. Finally, there are two papers
highlighting all four aspects of performance measurement.
Munhoz and Morabito (2014) showed in the production, blending
and storage of juices; the blending process of different types of
juices to match product specifications, for example, using orange
acidity to calculate the ratio specification. Likewise, Ahumada
and Villalobos (2011a) proved that significant savings can be
obtained by managing the trade-off of the freshness at the
delivery of the product with the added labor and transportation
cost at the grower's side. Moreover, dynamic, information based
management practices might be preferred over traditional
practices based in fixed labor allocation and distribution
practices.
5. Discussion towards designing and managing Agri

Fresh Produce Supply Chains in Vietnam
Supply chain design is becoming more important as Agriculture
is a crucial part of the global economy, especially related to
timeliness, packaging, temperature and humidity control, and
storage time of perishable farm products (Fernandes, 2003;
Soysal et al. 2012). Likewise, Lowe and Preckel (2004) declared
that these challenges generate a requirement for management

efficiency and the use of modern decision technology tools.
Moreover, Vaart and Pieter (2003) pointed out the importance of
an inter-disciplinary approach, integrating the relational and
technical aspects from the corresponding fields of system
dynamics and partnership in order to deliver better order


replenishment performance. Rumbaugh (1996) claimed that
associations are


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very important, for example, the addition of multiple items,
complex transportation policies, or assembly nodes would make
the use of associations necessary. This added complexity further
justifies the use of a formal modelling approach. To illustrate,
Maia et al. (1997) applied a mixed-integer linear programming
model to propose the selection of technology routes for fruit and
vegetable crops between harvest and market in the case of
vegetables and fruits due to the seasonal fluctuations in
production and demand and the need for special storage
conditions for different products. Besides, Blanco et al. (2005)
detailed a packing plant planning model for the fruit industry in
Argentina. Based on usage and requirements, fruits can pass
through up to ten different processes as they flow through a
packing plant. Their model deals with eight fruit varieties in three
categories of quality with five different gauges (i.e., sizes) by
using MIP. In addition, Ferrer et al. (2007) solved a mixed-integer

linear programming model to support harvest scheduling, labor
allocation, and routing decisions. A quality loss function is used
to represent wine quality reduction at each vineyard block due to
premature or deferred harvest with respect to an optimal date.
Recently, Munhoz and Morabito (2014) showed that a frozen
concentrated orange juice aggregate production planning is
modelled using linear programming to support decision making
in the production process of a citrus company with multiple
products, stages and periods. Then the model is extended to take
into account uncertainty in some model parameters using a
robust optimization approach. Furthermore, Duan and Liao (2013)
proposed a simulation optimization framework for supply chain
inventory management of highly perishable products. A new
replenishment policy based on old inventory ratio is developed,
called OIR policy. It is an age-based policy using only partial age
information to measure the freshness of the entire inventory. The
efficiency of the new policy is evaluated in detail for a singlevendor-multi-buyer platelet (with a limited shelf life of 5 days)
supply chain.
On the other hand, because of the wide variety of Food supply
chains (FSCs) structures, Broekmeulen (1998) complemented his
heuristic with a simulation model to study the impact that a
product-to-zone assignment makes on daily requests for storage
and retrieval by testing three different storage policies: the
preferred zone policy, the free zone policy, and the temperature
policy. With the proposed model, incorporating factors such as
seasonality and perishability would enhance the efficiency of a
fruit and vegetable distribution centre. Hester and Cacho (2003)


reported a biological model that describes the growth of an apple

tree using a carbon-


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balance approach. They use the output of this model, orchard
yield (fruit quantity, size, and weight) while the input to a related
economic model, forming a bio-economic model to predict the
weight of fruit. Results show that the relationship between
thinning and net present value is nonlinear and that net present
value is not maximized by the maximum price, yield, or weight of
the fruit. The authors use a dynamic optimization approach
consisting of a nonlinear model they formulate and a genetic
algorithm they devise. A limitation of this model is that it does
not deal explicitly with the stochastic aspects of the system.
Thus, addressing uncertainty explicitly remains a challenge for
future research. Specifically, Vitoriano et al. (2003) presented two
alternative mathematical models to attain the proposed
objective. The first model splits the time into discrete units
spread throughout the planning horizon; it is presented in
connection with flexible manufacturing. The second model keeps
a continuous time horizon; a scheduling model is used for which
a family of incompatibility conditions is introduced to avoid a
certain type of simultaneous usage of resources. Next, Verdouw
et al. (2010) addressed a basic model with templates for the
design of fresh and processed FSCs in a deep detail. The paper of
Rong et al. (2011) focused on the management of fresh food
quality modelling quality degradation, given a general approach
to fresh food and the problem of preserving the quality as
happens with fresh fruit. Moreover, the general structure of the

FSC described by Nadal-Roig and Plà (2015) was common in
modern FSCs, although the actors taking part may vary.
Therefore, as problems and needs in terms of modelling can be
different, slight variations in the structure can be found in
literature. For instance, Canavari et al. (2010) emphasized the
role of producers and consider three different FSCs. Similarly,
Srimanee and Routray (2012) modelled the FSCs in Thailand as a
network of five different FSCs linked by the producer who
establishes a percentage of production according to the final
consumer (local market, local collectors, cooperatives,
assemblers and export agents). A different approach is presented
by Zang and Fu (2010) who presented four different FSCs designs
existing in China (worldwide the first fruit producer) with different
characteristics, stakeholders and limitations.
Using the product's marginal value of time (MVT), Blackburn
and Scudder (2009) showed the rate at which the product loses
value over time in the supply chain. They also show the
appropriate model to minimize lost value in the supply chain is a
hybrid of a responsive model from post-harvest to cooling,


followed by an efficient model in the remainder of the chain and
these two segments of the supply chain are only loosely


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linked, implying that little coordination is required across the
chain to achieve value maximization. Similarly, Etemadnia et al.

(2015) developed a mixed integer linear programming (MILP) and
a heuristic solution for regional food access through optimal hub
locations to minimize total network costs which include costs of
transporting goods and locating facilities. A scenario study is
used to examine the model's sensitivity to parameter changes,
including travel distance, hub capacity, transportation cost, etc.
Catala et al. (2016) used a multi-period mixed integer linear
programming formulation for the medium-term planning of the
apples and pears supply chain is presented. Given the supply
chain structure, demand data, and harvesting dates, the
proposed
approach
integrates
production,
processing,
distribution, and inventory decisions considering two conflicting
objectives: profit and product supply shortage. The mathematical
model is solved by using the lexicographic method to deal with
the multi-objective optimization. Also, Banasik et al. (2017)
proposed a multi-objective mixed integer linear programming
model to quantify trade-offs between economic and
environmental indicators and explore quantitatively alternative
recycling technologies, developed to re-design the logistical
structure and close loops in the mushroom supply chain.
Furthermore, Mohammed and Wang (2017) described a
development of a product distribution planner for a three-echelon
green meat supply chain design in terms of issues including
numbers and locations of facilities that should be opened in
association with the product quantity flows. These issues are
formulated into a fuzzy multi-objective programming model

(FMOPM) with an aim to minimize the total cost of transportation
and implementation, the amount of CO2 emissions in
transportation and the distribution time of products from farms to
abattoirs and from abattoirs to retailers, and maximize the
average delivery rate in satisfying product quantity as requested
by abattoirs and retailers.
In terms of management supply chain, Perdana and Kusnandar
(2012) discussed the application of “triple helix model” on
interaction between universities, exporters, government, and
supporting institutions in developing fresh fruit and vegetable
supply chain management. Particularly, Manzini and Accorsi
(2013) presented a general and conceptual framework for the
assessment of food supply chain (FSC) and logistics of food
products in agreement with a multidisciplinary and integrated
view. The target of the proposed integrated approach to supply
chain design and management is the simultaneous control of
quality (1), safety (2), sustainability (3) and logistics efficiency