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A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry

Author

Listed:
  • Mohsen Abbaspour Onari

    (Urmia University of Technology)

  • Mustafa Jahangoshai Rezaee

    (Urmia University of Technology)

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 Decision-Making 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.

Suggested Citation

  • Mohsen Abbaspour Onari & Mustafa Jahangoshai Rezaee, 2022. "A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry," Operational Research, Springer, vol. 22(3), pages 2133-2171, July.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:3:d:10.1007_s12351-020-00606-1
    DOI: 10.1007/s12351-020-00606-1
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    References listed on IDEAS

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    1. Jahangoshai Rezaee, Mustafa & Yousefi, Samuel, 2018. "An intelligent decision making approach for identifying and analyzing airport risks," Journal of Air Transport Management, Elsevier, vol. 68(C), pages 14-27.
    2. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, April.
    3. Pramod Singh & Abhishek Nair, 2014. "Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach," Climatic Change, Springer, vol. 127(3), pages 475-491, December.
    4. Araz, Ceyhun & Ozkarahan, Irem, 2007. "Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure," International Journal of Production Economics, Elsevier, vol. 106(2), pages 585-606, April.
    5. Mulazzani, Luca & Manrique, Rosa & Malorgio, Giulio, 2017. "The Role of Strategic Behaviour in Ecosystem Service Modelling: Integrating Bayesian Networks With Game Theory," Ecological Economics, Elsevier, vol. 141(C), pages 234-244.
    6. Jahangoshai Rezaee, Mustafa & Yousefi, Samuel & Hayati, Jamileh, 2019. "Root barriers management in development of renewable energy resources in Iran: An interpretative structural modeling approach," Energy Policy, Elsevier, vol. 129(C), pages 292-306.
    7. Kyriakarakos, George & Patlitzianas, Konstantinos & Damasiotis, Markos & Papastefanakis, Dimitrios, 2014. "A fuzzy cognitive maps decision support system for renewables local planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 209-222.
    8. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
    9. Alizadeh, Somayeh & Ghazanfari, Mehdi, 2009. "Learning FCM by chaotic simulated annealing," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1182-1190.
    10. Ho, William & Xu, Xiaowei & Dey, Prasanta K., 2010. "Multi-criteria decision making approaches for supplier evaluation and selection: A literature review," European Journal of Operational Research, Elsevier, vol. 202(1), pages 16-24, April.
    11. Alexandros Nikas & Haris Doukas, 2016. "Developing Robust Climate Policies: A Fuzzy Cognitive Map Approach," International Series in Operations Research & Management Science, in: Michael Doumpos & Constantin Zopounidis & Evangelos Grigoroudis (ed.), Robustness Analysis in Decision Aiding, Optimization, and Analytics, chapter 0, pages 239-263, Springer.
    12. Yongbo Li & Amir-Reza Abtahi & Mahya Seyedan, 2019. "Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry," Annals of Operations Research, Springer, vol. 275(2), pages 461-484, April.
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