A Fuzzy Multi-objective Optimization Model in Sustainable Supply Chain Network Design Considering Financial Flow
Subject Areas : Fuzzy Optimization and Modeling JournalSeyed Hesamoddin Motevalli 1 , Adel Pourghader Chobar 2 , Maryam Ebrahimi 3 , Raheleh Alamiparvin 4
1 - Department of Future Studies, Eyvanekey University, Semnan, Iran
2 - Department of industrial engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran
3 - Department of Information Technology Management, Islamic Azad University, Electronic Branch, Tehran, Iran
4 - Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran
Keywords: Fuzzy Rule-based, Master Planning, Financial Flow, Goal Pprogramming, Fuzzy Multi-objective Solution Methods,
Abstract :
Integrated and coordinated planning of the main functions of the supply chain (procurement, production and distribution) often leads to economic efficiency and, as a result, more profit for the entire supply chain. On the other hand, the financial flow and the flow of goods and information are crucial and influential flows in any supply chain. In this paper, the main contribution is to integrated planning of procurement, production and distribution for a multi-product supply chain in order to maximize the producer's profit and also minimize the deviations of the producer's financial indicators by considering both the physical and financial flow. In this regard, the studded supply chain includes several suppliers, one producer and several customers. One of the prominent features of the proposed model is the use of mathematical programming to model the financial flow and achieve the producer's financial goals. Since the presented model is a bi-objective one, two fuzzy multi-objective interactive methods, Selim and Ozkarahan (SO) and Torabi and Hassini (TH) can adjust the degree of satisfaction of the objective functions have been applied. Next, the model is optimized using the goal programming method. Finally, the numerical results in optimizing the proposed fuzzy model show the proposed model's efficiency and the high quality of performance and applicability of the proposed model. The core achievement in the numerical results is that the total value of the distribution in the two models is equal. However, the SO method obtains more unbalanced solutions when the decision maker pays more attention to the first objective function.
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