IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v22y2022i3d10.1007_s12351-020-00606-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-020-00606-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-020-00606-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Alizadeh, Somayeh & Ghazanfari, Mehdi, 2009. "Learning FCM by chaotic simulated annealing," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1182-1190.
    4. 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.
    5. Martin J. Osborne & Ariel Rubinstein, 1994. "A Course in Game Theory," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262650401, December.
    6. 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.
    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. 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.
    9. Nash, John, 1950. "The Bargaining Problem," Econometrica, Econometric Society, vol. 18(2), pages 155-162, April.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jacob Engwerda & Davoud Mahmoudinia & Rahim Dalali Isfahani, 2016. "Government and Central Bank Interaction under Uncertainty: A Differential Games Approach," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 20(2), pages 225-259, Spring.
    2. Hanato, Shunsuke, 2019. "Simultaneous-offers bargaining with a mediator," Games and Economic Behavior, Elsevier, vol. 117(C), pages 361-379.
    3. Masanori Mitsutsune & Takanori Adachi, 2014. "Estimating noncooperative and cooperative models of bargaining: an empirical comparison," Empirical Economics, Springer, vol. 47(2), pages 669-693, September.
    4. Chen, Jen-Yi & Baddam, Swathi R., 2015. "The effect of unethical behavior and learning on strategic supplier selection," International Journal of Production Economics, Elsevier, vol. 167(C), pages 74-87.
    5. Saša Zorc & Ilia Tsetlin, 2020. "Deadlines, Offer Timing, and the Search for Alternatives," Operations Research, INFORMS, vol. 68(3), pages 927-948, May.
    6. Sherrill Shaffer, 2011. "Strategic risk aversion," Applied Financial Economics, Taylor & Francis Journals, vol. 21(13), pages 949-956.
    7. Yusuke Samejima, 2005. "A Note on Implementation of Bargaining Solutions," Theory and Decision, Springer, vol. 59(3), pages 175-191, November.
    8. Engwerda, J.C., 2012. "Prospects of Tools from Differential Games in the Study Of Macroeconomics of Climate Change," Other publications TiSEM cac36d07-227b-4cf2-83cb-7, Tilburg University, School of Economics and Management.
    9. Stefano Moretti & Fioravante Patrone, 2008. "Transversality of the Shapley value," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-41, July.
    10. Volij, Oscar, 2002. "A remark on bargaining and non-expected utility," Mathematical Social Sciences, Elsevier, vol. 44(1), pages 17-24, September.
    11. Li, Zhong-Ping & Wang, Jian-Jun & Perera, Sandun & Shi, Jim (Junmin), 2022. "Coordination of a supply chain with Nash bargaining fairness concerns," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    12. Osiro, Lauro & Lima-Junior, Francisco R. & Carpinetti, Luiz Cesar R., 2014. "A fuzzy logic approach to supplier evaluation for development," International Journal of Production Economics, Elsevier, vol. 153(C), pages 95-112.
    13. Hu, Tai-Wei & Rocheteau, Guillaume, 2020. "Bargaining under liquidity constraints: Unified strategic foundations of the Nash and Kalai solutions," Journal of Economic Theory, Elsevier, vol. 189(C).
    14. Volij, Oscar & Winter, Eyal, 2002. "On risk aversion and bargaining outcomes," Games and Economic Behavior, Elsevier, vol. 41(1), pages 120-140, October.
    15. Nuri Ozgur DOGAN, 2015. "Analyzing The Supplier Selection Process Of A Lean Manufacturing Firm: A Case Study," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 9(1), pages 1026-1033, November.
    16. Sergiu Hart & Andreu Mas-Colell, 2008. "Cooperative Games in Strategic Form," Discussion Paper Series dp484, The Federmann Center for the Study of Rationality, the Hebrew University, Jerusalem.
    17. Isabel Amigo & Pablo Belzarena & Sandrine Vaton, 2016. "Revenue sharing in network utility maximization problems," Netnomics, Springer, vol. 17(3), pages 255-284, November.
    18. Qing, Qiankai & Deng, Tianhu & Wang, Hongwei, 2017. "Capacity allocation under downstream competition and bargaining," European Journal of Operational Research, Elsevier, vol. 261(1), pages 97-107.
    19. Zhang, H. M. & Ge, Y. E., 2004. "Modeling variable demand equilibrium under second-best road pricing," Transportation Research Part B: Methodological, Elsevier, vol. 38(8), pages 733-749, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:22:y:2022:i:3:d:10.1007_s12351-020-00606-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.