supply chain management analyzing/modelling
Saeid Kalantari; Hamed Kazemipoor; Farzad Movahedi Sobhani; Seyyed Mohammad Hadji Molana
Abstract
Purpose: Establishing the structure and expansion of sustainable closed-loop supply chains is critical to meeting environmental, economic, and social standards to strengthen their position in competitive markets. This study aims to decide on operational and tactical levels to configure the Stable Closed ...
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Purpose: Establishing the structure and expansion of sustainable closed-loop supply chains is critical to meeting environmental, economic, and social standards to strengthen their position in competitive markets. This study aims to decide on operational and tactical levels to configure the Stable Closed Chain Supply Chain Network (SCLSC) to maximize Net Present Value (NPV) and seek to minimize carbon emissions while maintaining environmentally friendly policies and considering inflation.Methodology: This paper considers a solid Fuzzy Robust Optimization (FRO) approach to deal with stable, closed-loop supply chain uncertainties. Also, due to the complexity of the model and its multi-objective, a new combined method of Heuristic algorithm (HA) and Multi-Choice Goal Programming with Utility Function (MCGP-UF) is used. The proposed Mixed Integer Linear Programming (MILP) model is applied in the electronics industry.Findings: The proposed model is evaluated in several experiments and discussed in different scenarios to confirm the efficiency and validity of the proposed model and method. The results were compared with the two factors of optimal gap and solution time, which showed the proper performance of the proposed method. Then, the tactical results and model strategy were presented for a case study in which the optimal flow between facilities, selection of suitable suppliers, selection of transportation type, and opening of facilities were presented. The findings showed that in different scenarios, the effective improvement of the obtained solutions by reducing the solution time by twenty percent could address large-scale problems.Originality/Value: By considering a new combined method of heuristic algorithm and multi-choice ideal programming with a utility function, this paper is done to solve the problem of designing a stable closed-loop supply chain network under uncertainty.
Robust optimization
Amin Ghaseminejad; Mohammad Fallah; Hamed Kazemipoor
Abstract
Purpose: The present paper deals with modeling and solving a multi-objective problem of robust facility layout problem under uncertainty with NSGA-II, MOPSO and MOGWO algorithms. Since the problem of facility layout is NP-Hard, the need to use meta-algorithms by providing a suitable chromosome to achieve ...
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Purpose: The present paper deals with modeling and solving a multi-objective problem of robust facility layout problem under uncertainty with NSGA-II, MOPSO and MOGWO algorithms. Since the problem of facility layout is NP-Hard, the need to use meta-algorithms by providing a suitable chromosome to achieve near-optimal solutions has been investigated in this article. The issue under consideration in this article includes several departments that are based on 5 different aspects (minimizing the flow time between departments, maximizing the number of equipment and facilities, minimizing the distance traveled to access firefighting equipment, minimizing the distance to access optimal climatic conditions and maximization of noisy departments from each other) should be arranged in different parts of the hall. In order to achieve the above objective functions at the same time, assigning departments to each section, equipping each section with different equipments and arranging the departments together are among the main objectives of the article.Methodology: In this paper, GA, PSO and GWO single-objective meta-heuristic algorithms and NSGA-II, MOPSO and MOGWO multi-objective meta-heuristic algorithms have been used to solve the problem.Findings: Computational results show that GA, PSO and GWO single-objective algorithms have high efficiency in achieving the optimal value of the objective function in a much shorter time, and their multi-objective methods show the high efficiency of the NSGA-II algorithm in achieving the average value of the objective function. First, second and fifth; the MOPSO algorithm has the highest expansion and metric distance in achieving the average number of efficient answers and computational time, and finally the MOGWO algorithm in obtaining the average value of the third and fourth objective functions. Statistical comparisons also showed a significant difference between the means of computational time. To evaluate and rank the algorithms, the TOPSIS method is used and the results show the high efficiency of the MOGWO algorithm in solving the model.Originality/Value: In this paper, a new model of the multi-objective robust facility layout problem under uncertainty conditions is modeled with respect to health and environmental safety aspects.