scholarly journals Mathematical Programming Models for Fresh Fruit Supply Chain Optimization: A Review of the Literature and Emerging Trends

2021 ◽  
Vol 3 (3) ◽  
pp. 519-541
Author(s):  
Tri-Dung Nguyen ◽  
Tri Nguyen-Quang ◽  
Uday Venkatadri ◽  
Claver Diallo ◽  
Michelle Adams
Keyword(s):  
Supply Chain ◽  
Price Volatility ◽  
Fresh Fruit ◽  
Mixed Integer ◽  
Supply Chain Optimization ◽  
Emerging Trends ◽  
Planning Framework ◽  
Production And Distribution ◽  
Processor Speed ◽  
Functional Areas

The fresh fruit agricultural and distribution sector is faced with risks and uncertainties from climate change, water scarcity, land-use increase for industrial and urban development, consumer behavior, and price volatility. The planning framework for production and distribution is highly complex as a result. Mathematical models have been developed over the decades to deal with this complexity. With improvements in both processor speed and memory, these models are becoming increasingly sophisticated. This review focuses on the recent progress in mathematically based decision making to account for uncertainties in the fresh fruit supply chain. The models in the literature are mostly based on linear and mixed integer programming and involve variants such as stochastic programming and robust optimization. The functional areas of application include planting, harvest optimization, logistics and distribution. The perishability of the fresh fruit supply chain is an important issue as is the cycle time of cultivation and harvest.

Coordinating lot sizing and integrated production and distribution scheduling with batch delivery and holding cost

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maedeh Bank ◽  
Mohammad Mahdavi Mazdeh ◽  
Mahdi Heydari ◽  
Ebrahim Teimoury
Keyword(s):  
Supply Chain ◽  
Lot Sizing ◽  
Mixed Integer ◽  
Time Interval ◽  
Content Type ◽  
Integrated Production ◽  
Holding Costs ◽  
Integrated Production And Distribution ◽  
Distribution Scheduling ◽  
Production And Distribution

PurposeThe aim of this paper is to present a method for finding the optimum balance between sequence-dependent setup costs, holding costs, delivery costs and delay penalties in an integrated production–distribution system with lot sizing decisions.Design/methodology/approachTwo mixed integer linear programming models and an optimality property are proposed for the problem. Since the problem is NP-hard, a genetic algorithm reinforced with a heuristic is developed for solving the model in large-scale settings. The algorithm parameters are tuned using the Taguchi method.FindingsThe results obtained on randomly generated instances reveal a performance advantage for the proposed algorithm; it is shown that lot sizing can reduce the average cost of the supply chain up to 11.8%. Furthermore, the effects of different parameters and factors of the proposed model on supply chain costs are examined through a sensitivity analysis.Originality/valueAlthough integrated production and distribution scheduling in make-to-order industries has received a great deal of attention from researchers, most researchers in this area have treated each order as a job processed in an uninterrupted time interval, and no temporary holding costs are assumed. Even among the few studies where temporary holding costs are taken into consideration, none has examined the effect of splitting an order at the production stage (lot sizing) and the possibility of reducing costs through splitting. The present study is the first to take holding costs into consideration while incorporating lot sizing decisions in the operational production and distribution problem.

scholarly journals Local Blood Supply Chain Optimization: A Case Study

2020 ◽  
Vol 18 (4) ◽  
Author(s):  
Reza Babazadeh ◽  
Ali Sabbaghnia ◽  
Fatemeh Shafipour
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Blood Donation ◽  
Programming Model ◽  
Human Life ◽  
Mixed Integer ◽  
Model Parameters ◽  
Supply Chain Optimization ◽  
Blood Supply Chain ◽  
Proposed Model

: Blood and its products play an undeniable role in human life. In recent years, although both academics and practitioners have investigated blood-related problems, further enhancement is still warranted. In this study, a mixed-integer linear programming model was proposed for local blood supply chain management. A supply network, including temporary and fixed blood donation facilities, blood banks, and blood processing centers, was designed regarding the deteriorating nature of blood. The proposed model was applied in a real case in Urmia, Iran. The numerical results and sensitivity analysis of the key model parameters ensured the applicability of the proposed model.

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

scholarly journals The Nutritious Supply Chain: Optimizing Humanitarian Food Assistance

2021 ◽  
pp. ijoo.2019.0047
Author(s):  
Koen Peters ◽  
Sérgio Silva ◽  
Rui Gonçalves ◽  
Mirjana Kavelj ◽  
Hein Fleuren ◽  
...  
Keyword(s):  
Supply Chain ◽  
Programming Model ◽  
Food Assistance ◽  
Decision Makers ◽  
Mixed Integer ◽  
Sourcing Strategies ◽  
Humanitarian Agency ◽  
Operational Excellence ◽  
Functional Areas

The World Food Programme (WFP) is the largest humanitarian agency fighting hunger worldwide, reaching approximately 90 million people with food assistance across 80 countries each year. To deal with the operational complexities inherent in its mandate, WFP has been developing tools to assist its decision makers with integrating supply chain decisions across departments and functional areas. This paper describes a mixed integer linear programming model that simultaneously optimizes the food basket to be delivered, the sourcing plan, the delivery plan, and the transfer modality of a long-term recovery operation for each month in a predefined time horizon. By connecting traditional supply chain elements to nutritional objectives, we are able to make significant breakthroughs in the operational excellence of WFP’s most complex operations. We show three examples of how the optimization model is used to support operations: (1) to reduce the operational costs in Iraq by 12% without compromising the nutritional value supplied, (2) to manage the scaling-up of the Yemen operation from three to six million beneficiaries, and (3) to identify sourcing strategies during the El Niño drought of 2016.

scholarly journals Supply Chain Optimization for Farmer-Bepari System of Agricultural Products in Bangladesh

2019 ◽  
pp. 1-13
Author(s):  
Mohammad Khairul Islam ◽  
Md. Mahmud Alam ◽  
Mohammed Forhad Uddin
Keyword(s):  
Product Distribution ◽  
Labour Cost ◽  
Agricultural Products ◽  
Raw Material ◽  
Mixed Integer ◽  
Optimization Strategy ◽  
Supply Chain Optimization ◽  
Material Cost ◽  
Production And Distribution ◽  
Milp Model

This study, for the Farmer-Bepari system of agricultural products in Bangladesh, can be formulated as a mixed integer linear programming (MILP) model. Further, it will be investigated that the significant impact of profit the attributes such as labour cost, fertilizer cost, the raw material cost of different firms and also to estimate the product distribution in different locations. To solve this MILP model, with the help of a branch and bound algorithm by using A Mathematical Programming Language (AMPL). To investigate the model we have to collect data from seven locations of three districts in Bangladesh. Also, a numerical example presented this study, which objectives illustrate the models. From the sensitivity of the production, if the raw material cost, labour cost and fertilizer cost increase is about 5%, then decrease the profit by MILP model have 0.004%, 1.6% and 1.2% respectively. Labour cost is a significant factor in profit, which changes the profit more than the raw material cost and fertilizer cost of the product. The results are helping decision-makers to identify the desired agricultural production and distribution structure optimization strategy.

scholarly journals Partial Coordination May Increase Overall Costs in Supply Chains

2008 ◽  
Vol 2 (2) ◽  
pp. 47-62 ◽  
Author(s):  
Waldemar Kaczmarczyk
Keyword(s):  
Supply Chain ◽  
Computational Study ◽  
Mixed Integer ◽  
Single Model ◽  
Sequential Method ◽  
Production And Distribution ◽  
Planning Methods ◽  
Production And Distribution Planning ◽  
The Right ◽  
The Impact

This paper presents a computational study to evaluate the impact of coordinating production and distribution planning in a two-level industrial supply chain. Three planning methods are compared. The first emulates the traditional way of planning. The two other coordinate plans of the supplier and of all the buyers according to the Vendor Managed Inventory (VMI) approach. The monolithic method solves a single model describing the entire optimization problem. The sequential method copies the imperfect VMI practice. All three methods are implemented by means of Mixed Integer Programming models. The results presented prove that the right choice of planning method is very important for overall cost of the supply chain. In contrast to the previous research, it turned out that information sharing without full coordination may even lead to increase in the overall cost. For some companies applying the VMI approach, developing exact models and solving them almost optimally may therefore be very important.

scholarly journals Supply chain integration within mass customization: Tactical procurement, production and distribution modeling

2021 ◽  
Vol 14 (2) ◽  
pp. 250
Author(s):  
Mouad Benbouja ◽  
Achraf Touil ◽  
Abdelwahed Echchatbi ◽  
Abdelkabir Charkaoui
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Mass Customization ◽  
Programming Model ◽  
Exact Method ◽  
Raw Material ◽  
Mixed Integer ◽  
Dyadic Relationships ◽  
Distribution Modeling ◽  
Production And Distribution

Purpose: The actual market characteristic oriented toward customers’ requirements compels decision-makers to foresee customization abilities. Mass customization represents a valuable approach to combine customizable offers with mass production processes. From a supply chain standpoint, this paper attempts to develop an integrated procurement, production and distribution modeling to describe the generated framework structure formulation within tactical decision planning level.Design/methodology/approach: The paper provides a mixed integer linear programming model of a three echelon supply chain illustrated from the automotive industry with (a) customers: Original Equipment Manufacturers (OEMs) identified as leaders and (b) first-tier supplier: wiring harnesses manufacturer (c) second-tier supplier: raw material supplier, identified as followers. The model formulation is depicted through dyadic relationships between stakeholders considering the specific operation enablers of the environment such as make to order, modular approach in addition to the corresponding inventory management policy.Findings: The integrated model is solved by an exact method which illustrates the feasibility of the formulation in addition to the observance of the applied constraints. A sensitivity analysis is performed to highlight the interdependency across some key parameters to provide managerial insights within the studied framework while keeping the optimal solvability of the model.Research limitations/implications: The limitation of this study is the computational experiment study. An extensive experiment with a real-word case will outline the optimal solvability status of the exact method and the necessity for a performance benchmark through the approximate solving approaches.Originality/value: The present research aims to contribute as first studies toward mathematical modeling for supply chain decision planning endeavor operating within mass customization business model.

scholarly journals A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Paweł Sitek ◽  
Krzysztof Bzdyra ◽  
Jarosław Wikarek
Keyword(s):  
Supply Chain ◽  
Hybrid Method ◽  
Optimization Problems ◽  
Programming Model ◽  
Combinatorial Problem ◽  
Search Space ◽  
Mixed Integer ◽  
Optimization Models ◽  
Supply Chain Optimization ◽  
Logical Constraints

This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP) and constraint logic programming (CLP), were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method) helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems). TheECLiPSesystem with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

scholarly journals Optimization Model for Multimodal Transportation Networks Based on Supply Chain

2012 ◽  
Vol 4 (2) ◽  
pp. 77-96 ◽  
Author(s):  
Paweł Sitek ◽  
Jarosław Wikarek
Keyword(s):  
Supply Chain ◽  
Mixed Integer Linear Programming ◽  
Numerical Experiments ◽  
Cost Optimization ◽  
Mixed Integer ◽  
Supply Chain Optimization ◽  
Multimodal Transportation ◽  
Optimization Criteria ◽  
Sample Data ◽  
Mode Of Transport

Abstract The article presents the problem of supply chain optimization from the perspective of a multimodal logistics provider and includes a mathematical model of multilevel cost optimization in the form of MILP (Mixed Integer Linear Programming). The costs of production, transport, distribution and environmental protection were adopted as optimization criteria. Timing, volume, capacity and mode of transport were also taken into account. The model was implemented in the LINGO ver.12. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and supply chain optimization.

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