A bi-objective production-distribution problem in a supply chain network under grey flexible conditions

One of the main topics discussed in a supply chain is the production-distribution problem. Producing and distributing the products plays a key role in reducing the costs of the chain. To design a supply chain, a network of efficient management and production-distribution decisions is essential. Accordingly, providing an appropriate mathematical model for such problems can be helpful in designing and managing supply chain networks. Mathematical formulations must be drawn close to the real world due to the importance of supply chain networks. This makes those formulations more complicated. In this study, a novel multi-objective formulation is devised for the production-distribution problem of a supply chain that consists of several suppliers, manufacturers, distributors, and different customers. Also, a Mixed Integer Linear Programming (MILP) mathematical model is proposed for designing a multi-objective and multi-period supply chain network. In addition, grey flexible linear programming (GFLP) is done for a multi-objective production-distribution problem in a supply chain network. The network is designed for the first time to cope with the uncertain nature of costs, demands, and capacity parameters. In this regard, due to the NP-hardness and complexity of problems and the necessity of using meta-heuristic algorithms, NSGA-II and Fast PGA algorithm are applied and compared in terms of several criteria that emphasize the quality and diversity of the solutions.

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A multi-objective approach under uncertainty for designing a green meat supply chain network

10.32920/ryerson.14656173 ◽

2021 ◽

Author(s):

Fatemeh Mohebalizadehgashti

Keyword(s):

Linear Programming ◽

Supply Chain ◽

Integer Linear Programming ◽

Mixed Integer Linear Programming ◽

Mixed Integer ◽

Environmental Concerns ◽

Supply Chain Network ◽

Food Supply Chain ◽

Multi Objective ◽

Proposed Model

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature. Keywords: Meat supply chain; Decision tree; Multi-objective programming; Mixed-integer linear programming; Augmented εε-constraint.

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A multi-objective approach under uncertainty for designing a green meat supply chain network

10.32920/ryerson.14656173.v1 ◽

2021 ◽

Author(s):

Fatemeh Mohebalizadehgashti

Keyword(s):

Linear Programming ◽

Supply Chain ◽

Integer Linear Programming ◽

Mixed Integer Linear Programming ◽

Mixed Integer ◽

Environmental Concerns ◽

Supply Chain Network ◽

Food Supply Chain ◽

Multi Objective ◽

Proposed Model

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this thesis, environmental concerns with a mathematical model are investigated to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. A multi-objective mixed-integer linear programming formulation is developed to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization. To demonstrate the efficiency of the proposed optimization model, a green meat supply chain network for Southern Ontario, Canada is designed. A solution approach based on augmented εε-constraint method is developed for solving the proposed model. As a result, a set of Pareto-optimal solutions is obtained. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature. Keywords: Meat supply chain; Decision tree; Multi-objective programming; Mixed-integer linear programming; Augmented εε-constraint.

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A Pareto-based optimization algorithm for a multi-objective integrated production-distribution planning problem of a multi-echelon supply chain network design

International Journal of Services and Operations Management ◽

10.1504/ijsom.2020.10019250 ◽

2020 ◽

Vol 37 (1) ◽

pp. 1

Author(s):

Keyvan Sarrafha ◽

Abolfazl Kazemi ◽

Alireza Alinezhad ◽

Seyed Taghi Akhavan Niaki

Keyword(s):

Supply Chain ◽

Network Design ◽

Optimization Algorithm ◽

Planning Problem ◽

Supply Chain Network Design ◽

Supply Chain Network ◽

Distribution Planning ◽

Integrated Production ◽

Multi Objective ◽

Production Distribution

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Mathematical modeling for optimizing the blood supply chain network

Modern Supply Chain Research and Applications ◽

10.1108/mscra-09-2020-0024 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Amir Rahimzadeh Dehaghani ◽

Muhammad Nawaz ◽

Rohullah Sultanie ◽

Tawiah Kwatekwei Quartey-Papafio

Keyword(s):

Supply Chain ◽

Blood Supply ◽

Supply Chain Network ◽

Distribution Centers ◽

Nsga Ii ◽

Content Type ◽

Supply Chain Networks ◽

Multi Objective ◽

Blood Supply Chain ◽

The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

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A fuzzy approach to multi-objective mixed integer linear programming model for multi-echelon closed-loop supply chain with multi-product multi-time-period

Operations Research and Decisions ◽

10.37190/ord200102 ◽

2020 ◽

Vol 30 (1) ◽

Author(s):

Sema Akin Bas ◽

Beyza Ahlatcioglu Ozkok

Keyword(s):

Linear Programming ◽

Supply Chain ◽

Integer Linear Programming ◽

Mixed Integer Linear Programming ◽

Closed Loop ◽

Mixed Integer ◽

Fuzzy Approach ◽

Closed Loop Supply Chain ◽

Multi Objective ◽

Time Period

By the green point of view, supply chain management (SCM), which contains supplier and location selection, production, distribution, and inventory decisions, is an important subject being examined in recent years by both practitioners and academicians. In this paper, the closed-loop supply chain (CLSC) network that can be mutually agreed by meeting at the level of common satisfaction of conflicting objectives is designed. We construct a multi-objective mixed-integer linear programming (MOMILP) model that allows decision-makers to more effectively manage firms’ closed-loop green supply chain (SC). An ecological perspective is brought by carrying out the recycling, remanufacturing and destruction to SCM in our proposed model. Maximize the rating of the regions in which they are located, minimize total cost and carbon footprint are considered as the objectives of the model. By constructing our model, the focus of customer satisfaction is met, as well as the production, location of facilities and order allocation are decided, and we also carry out the inventory control of warehouses. In our multi-product multi-component multi-time-period model, the solution is obtained with a fuzzy approach by using the min operator of Zimmermann. To illustrate the model, we provide a practical case study, and an optimal result containing a preferable level of satisfaction to the decision-maker is obtained.

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