A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry
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A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry Mohsen Abbaspour Onari1 · Mustafa Jahangoshai Rezaee1 Received: 21 October 2019 / Revised: 16 August 2020 / Accepted: 12 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Supplier Selection (SS) is a critical issue due to intense competition in the current market and the need to provide customer necessities with acceptable quality. On the other hand, SS depends on various criteria that make it a Multi-Criteria DecisionMaking problem. Hence, a novel framework has been proposed in the current study to evaluate and rank suppliers. The proposed framework by aggregating the Process Control Score (PCS) and Process Evaluation Score (PES) evaluate and rank suppliers. For calculating PCS, a new structure and logic of the Fuzzy Cognitive Map based on the Nash Bargaining Game (BG-FCM) has been proposed to solve FCM’s shortcoming in distinguishing between the important concepts in the real world. Moreover, for generating solutions with high separability and helping decision-makers to have a precise analysis of the system, a modified learning algorithm based on the Particle Swarm Optimization (PSO) and S-shaped transfer function (PSO-STF) has been utilized for training BG-FCM. For calculating PES, experimental mathematical equations in the inspected case have been utilized for important criteria of quality, delivery time, and price of the shipment. The proposed framework has been applied in an auto parts industry for validation. The results show that BG-FCM can successfully highlight the most important concepts and assign their original value. Also, PSO-STF in the comparison between other conventional FCMs’ learning algorithms has better performance in generating solutions with high separability. It can be concluded that BN-FCM with more progressive intelligence can analyze the complex systems and help decision-makers to have a vivid insight into the system. Keywords Supplier selection framework · Fuzzy cognitive map · Nash bargaining game · Particle swarm optimization · S-shaped transfer function · Auto parts industry
* Mustafa Jahangoshai Rezaee [email protected] 1
Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
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M. Abbaspour Onari, M. Jahangoshai Rezaee
1 Introduction Current supply management needs long-term partnerships with suppliers and uses fewer but more reliable suppliers. Therefore, selecting the proper supplier involves more than just a set of prices, and the choices depend on a range of quantitative and qualitative factors (Ho et al. 2010). Due to the high number of suppliers in today’s competitive industrial world, choosing the proper supplier is momentous. Current competitive markets require companies to respond quickly and effectively to customers’ demands to gain customer satisfaction and improve their market status. In such circumstances, the role of suppliers and their Supply Chain Man
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