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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbaspour Onari M, Yousefi S, Jahangoshai Rezaee M (2020) Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm. Artif Intell Rev. https://doi.org/10.1007/s10462-020-09883-w
Aboutorab H, Saberi M, Asadabadi MR, Hussain O, Chang E (2018) ZBWM: the Z-number extension of best worst method and its application for supplier development. Expert Syst Appl 107:115–125. https://doi.org/10.1016/j.eswa.2018.04.015
Alinezad A, Seif A, Esfandiari N (2013) Supplier evaluation and selection with QFD and FAHP in a pharmaceutical company. Int J Adv Manuf Technol 68(1–4):355–364. https://doi.org/10.1007/s00170-013-4733-3
Alizadeh A, Yousefi S (2019) An integrated Taguchi loss function–fuzzy cognitive map–MCGP with utility function approach for supplier selection problem. Neural Comput Appl 31(11):7595–7614. https://doi.org/10.1007/s00521-018-3591-1
Alizadeh S, Ghazanfari M (2009) Learning FCM by chaotic simulated annealing. Chaos Solitons Fractals 41(3):1182–1190. https://doi.org/10.1016/j.chaos.2008.04.058
Alizadeh S, Ghazanfari M, Jafari M, Hooshmand S (2007) Learning FCM by tabu search. Int J Comput Sci 2(2):142–149
Anezakis V-D, Dermetzis K, Iliadis L, Spartalis S (2016) Fuzzy cognitive maps for long-term prognosis of the evolution of atmospheric pollution, based on climate change scenarios: the case of Athens. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 9875 LNCS. Springer, pp 175–186. https://doi.org/10.1007/978-3-319-45243-2_16
Araz C, Ozkarahan I (2007) Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. Int J Prod Econ 106(2):585–606. https://doi.org/10.1016/j.ijpe.2006.08.008
Axelrod R (1976) The cognitive mapping approach to decision making. In: Structure of decision, p 221–250
Azadeh A, Ghaderi SF, Pashapour S, Keramati A, Malek MR, Esmizadeh M (2017) A unique fuzzy multivariate modeling approach for performance optimization of maintenance workshops with cognitive factors. Int J Adv Manuf Technol 90(1–4):499–525. https://doi.org/10.1007/s00170-016-9208-x
Bakhtavar E, Yousefi S (2018) Assessment of workplace accident risks in underground collieries by integrating a multi-goal cause-and-effect analysis method with MCDM sensitivity analysis. Stoch Environ Res Risk Assess 32(12):3317–3332. https://doi.org/10.1007/s00477-018-1618-x
Banaeian N, Mobli H, Fahimnia B, Nielsen IE, Omid M (2018) Green supplier selection using fuzzy group decision making methods: a case study from the agri-food industry. Comput Oper Res 89:337–347. https://doi.org/10.1016/j.cor.2016.02.015
Bevilacqua M, Ciarapica FE, Marcucci G, Mazzuto G (2018) Conceptual model for analysing domino effect among concepts affecting supply chain resilience. Supply Chain Forum 19(4):282–299. https://doi.org/10.1080/16258312.2018.1537504
Bourgani E, Stylios CD, Manis G, Georgopoulos V. C (2014) Time dependent fuzzy cognitive maps for medical diagnosis. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 8445 LNCS. Springer, pp 544–554. https://doi.org/10.1007/978-3-319-07064-3_47
Chandramohan A, Rao MVC, Senthil Arumugam M (2006) Two new and useful defuzzification methods based on root mean square value. Soft Comput 10(11):1047–1059. https://doi.org/10.1007/s00500-005-0042-6
Chen YJ (2011) Structured methodology for supplier selection and evaluation in a supply chain. Inf Sci 181(9):1651–1670. https://doi.org/10.1016/j.ins.2010.07.026
Choi TY, Hartley JL (1996) An exploration of supplier selection practices across the supply chain. J Oper Manag 14(4):333–343. https://doi.org/10.1016/S0272-6963(96)00091-5
Clerc M, Kennedy J (2002) The particle swarm—explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73. https://doi.org/10.1109/4235.985692
Dabbagh R, Yousefi S (2019) A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis. J Saf Res 71:111–123. https://doi.org/10.1016/j.jsr.2019.09.021
Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. Presence Teleoper Virtual Environ 3(2):173–189. https://doi.org/10.1162/pres.1994.3.2.173
de Castro Vivas R, Sant’Anna AMO, Esquerre KPSO, Freires FGM (2020) Integrated method combining analytical and mathematical models for the evaluation and optimization of sustainable supply chains: a Brazilian case study. Comput Ind Eng 139:105670. https://doi.org/10.1016/j.cie.2019.01.044
Froelich W, Juszczuk P (2009) Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps—a comparative study. Stud Comput Intell 252:153–174. https://doi.org/10.1007/978-3-642-04170-9_7
Groumpos PP (2010) Fuzzy cognitive maps: basic theories and their application to complex systems. Stud Fuzziness Soft Comput 247:1–22. https://doi.org/10.1007/978-3-642-03220-2_1
Ho W, Xu X, Dey PK (2010) Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. Eur J Oper Res 202(1):16–24. https://doi.org/10.1016/j.ejor.2009.05.009
Hoyle D (2005) Automotive quality systems handbook: ISO/TS 16949, 2002nd edn. Elsevier, Amsterdam
Irani Z, Sharif A, Kamal MM, Love PED (2014) Visualising a knowledge mapping of information systems investment evaluation. Expert Syst Appl 41(1):105–125. https://doi.org/10.1016/j.eswa.2013.07.015
Irani Z, Kamal MM, Sharif A, Love PED (2017) Enabling sustainable energy futures: factors influencing green supply chain collaboration. Prod Plann Control 28(6–8):684–705. https://doi.org/10.1080/09537287.2017.1309710
Jahangoshai Rezaee M, Shokry M (2017) Game theory versus multi-objective model for evaluating multi-level structure by using data envelopment analysis. Int J Manag Sci Eng Manag 12(4):245–255. https://doi.org/10.1080/17509653.2016.1249425
Jahangoshai Rezaee M, Yousefi S (2018) An intelligent decision making approach for identifying and analyzing airport risks. J Air Transp Manag 68:14–27. https://doi.org/10.1016/j.jairtraman.2017.06.013
Jahangoshai Rezaee M, Yousefi S, Hayati J (2019) Root barriers management in development of renewable energy resources in Iran: an interpretative structural modeling approach. Energy Policy 129:292–306. https://doi.org/10.1016/j.enpol.2019.02.030
Jahangoshai Rezaee M, Moini A, Haji-Ali Asgari F (2012a) Unified performance evaluation of health centers with integrated model of data envelopment analysis and bargaining game. J Med Syst 36:3805–3815. https://doi.org/10.1007/s10916-012-9853-z
Jahangoshai Rezaee M, Moini A, Makui A (2012b) Operational and non-operational performance evaluation of thermal power plants in Iran: a game theory approach. Energy 38(1):96–103. https://doi.org/10.1016/j.energy.2011.12.030
Jahangoshai Rezaee M, Yousefi S, Valipour M, Dehdar MM (2018) Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis. Comput Ind Eng 123:325–337. https://doi.org/10.1016/j.cie.2018.07.012
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, vol 4. IEEE, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kim MC, Kim CO, Hong SR, Kwon IH (2008) Forward-backward analysis of RFID-enabled supply chain using fuzzy cognitive map and genetic algorithm. Expert Syst Appl 35(3):1166–1176. https://doi.org/10.1016/j.eswa.2007.08.015
Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75. https://doi.org/10.1016/S0020-7373(86)80040-2
Koulouriotis DE, Diakoulakis IE, Emiris DM (2001) Anamorphosis of fuzzy cognitive maps for operation in ambiguous and multi-stimulus real world environments. In: 10th IEEE international conference on fuzzy systems (Cat. No. 01CH37297), vol. 3. IEEE, pp 1156–1159
Kyriakarakos G, Patlitzianas K, Damasiotis M, Papastefanakis D (2014) A fuzzy cognitive maps decision support system for renewables local planning. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2014.07.009
Lambertini L (2011) Game theory in the social sciences: a reader-friendly guide. Routledge, London
Lamba K, Singh SP, Mishra N (2019) Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in Big Data environment. Comput Ind Eng 128:1052–1062. https://doi.org/10.1016/j.cie.2018.04.028
Li Y, Abtahi AR, Seyedan M (2019) Supply chain performance evaluation using fuzzy network data envelopment analysis: a case study in automotive industry. Ann Oper Res 275(2):461–484. https://doi.org/10.1007/s10479-018-3027-4
Liu T, Deng Y, Chan F (2018) Evidential supplier selection based on DEMATEL and game theory. Int J Fuzzy Syst 20(4):1321–1333. https://doi.org/10.1007/s40815-017-0400-4
Lu LYY, Wu CH, Kuo TC (2007) Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis. Int J Prod Res 45(18–19):4317–4331. https://doi.org/10.1080/00207540701472694
Luo X, Wei X, Zhang J (2009) Game-based learning model using fuzzy cognitive map. In: Proceedings of the first ACM international workshop on Multimedia technologies for distance learning, pp 67–76
Mulazzani L, Manrique R, Malorgio G (2017) The role of strategic behaviour in ecosystem service modelling: integrating bayesian networks with game theory. Ecol Econ. https://doi.org/10.1016/j.ecolecon.2017.04.022
Nash JF (1950) The bargaining problem. Econometrica 18(2):155. https://doi.org/10.2307/1907266
Nikas A, Doukas H (2016) Developing robust climate policies: a fuzzy cognitive map approach. In: International series in operations research and management science, vol 241. Springer New York LLC, pp 239–263. https://doi.org/10.1007/978-3-319-33121-8_11
Nikas A, Ntanos E, Doukas H (2019) A semi-quantitative modelling application for assessing energy efficiency strategies. Appl Soft Comput J 76:140–155. https://doi.org/10.1016/j.asoc.2018.12.015
Osborne MJ, Rubinstein A (1994) A course in game theory. MIT press, Cambridge
Özgen D, Önüt S, Gülsün B, Tuzkaya UR, Tuzkaya G (2008) A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Inf Sci 178(2):485–500. https://doi.org/10.1016/j.ins.2007.08.002
Pal O, Gupta AK, Garg RK (2013) Supplier selection criteria and methods in supply chains: a review. Int J Soc Manag Econ Bus Eng 7(10):1403–1409
Papageorgiou EI (2013) Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms, vol 54. Springer, Berrlin
Papageorgiou EI, Groumpos PP (2005a) A new hybrid method using evolutionary algorithms to train fuzzy cognitive maps. Appl Soft Comput J 5(4):409–431. https://doi.org/10.1016/j.asoc.2004.08.008
Papageorgiou EI, Groumpos PP (2005b) A weight adaptation method for fuzzy cognitive map learning. Soft Comput 9(11):846–857. https://doi.org/10.1007/s00500-004-0426-z
Papageorgiou EI, Poczęta K (2017) A two-stage model for time series prediction based on fuzzy cognitive maps and neural networks. Neurocomputing 232:113–121. https://doi.org/10.1016/j.neucom.2016.10.072
Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–249. https://doi.org/10.1016/j.ijar.2004.01.001
Papageorgiou EI, Parsopoulos KE, Stylios CS, Groumpos PP, Vrahatis MN (2005) Fuzzy cognitive maps learning using particle swarm optimization. J Intell Inf Syst 25(1):95–121. https://doi.org/10.1007/s10844-005-0864-9
Papageorgiou EI, Stylios C, Groumpos PP (2006) Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int J Hum Comput Stud 64(8):727–743. https://doi.org/10.1016/j.ijhcs.2006.02.009
Papageorgiou EI, Subramanian J, Karmegam A, Papandrianos N (2015) A risk management model for familial breast cancer: a new application using fuzzy cognitive map method. Comput Methods Programs Biomed 122(2):123–135. https://doi.org/10.1016/j.cmpb.2015.07.003
Ramezankhani MJ, Torabi SA, Vahidi F (2018) Supply chain performance measurement and evaluation: a mixed sustainability and resilience approach. Comput Ind Eng 126:531–548. https://doi.org/10.1016/j.cie.2018.09.054
Ren Z (2007) Learning fuzzy cognitive maps by a hybrid method using nonlinear Hebbian learning and extended great deluge algorithm. www.aaai.org
Rezaee MJ, Yousefi S, Babaei M (2017) Multi-stage cognitive map for failures assessment of production processes: an extension in structure and algorithm. Neurocomputing 232:69–82. https://doi.org/10.1016/j.neucom.2016.10.069
Sabouhi F, Pishvaee MS, Jabalameli MS (2018) Resilient supply chain design under operational and disruption risks considering quantity discount: a case study of pharmaceutical supply chain. Comput Ind Eng 126:657–672. https://doi.org/10.1016/j.cie.2018.10.001
Salmeron JL, Rahimi SA, Navali AM, Sadeghpour A (2017) Medical diagnosis of rheumatoid arthritis using data driven PSO–FCM with scarce datasets. Neurocomputing 232:104–112. https://doi.org/10.1016/j.neucom.2016.09.113
Shojaei P, Haeri SAS (2019) Development of supply chain risk management approaches for construction projects: a grounded theory approach. Comput Ind Eng 128:837–850. https://doi.org/10.1016/j.cie.2018.11.045
Singh PK, Nair A (2014) Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach. Clima Change 127(3–4):475–491. https://doi.org/10.1007/s10584-014-1275-0
Stach WJ (2010) Learning and aggregation of Fuzzy Cognitive Maps—an evolutionary approach. https://doi.org/10.7939/R32M6Z
Stylios CD, Georgopoulos VC, Malandraki GA, Chouliara S (2008) Fuzzy cognitive map architectures for medical decision support systems. Appl Soft Comput J 8(3):1243–1251. https://doi.org/10.1016/j.asoc.2007.02.022
van Vliet M, Kok K, Veldkamp T (2010) Linking stakeholders and modellers in scenario studies: the use of Fuzzy cognitive maps as a communication and learning tool. Futures 42(1):1–14. https://doi.org/10.1016/j.futures.2009.08.005
Yousefi S, Mahmoudzadeh H, Jahangoshai Rezaee M (2017) Using supply chain visibility and cost for supplier selection: a mathematical model. Int J Manag Sci Eng Manag 12(3):196–205. https://doi.org/10.1080/17509653.2016.1218307
Yousefi S, Jahangoshai Rezaee M, Solimanpur M (2019) Supplier selection and order allocation using two-stage hybrid supply chain model and game-based order price. Oper Res Int Journal. https://doi.org/10.1007/s12351-019-00456-6
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zare Ravasan A, Mansouri T (2016) A dynamic ERP critical failure factors modelling with FCM throughout project lifecycle phases. Prod Plann Control 27(2):65–82. https://doi.org/10.1080/09537287.2015.1064551
Zhu Y, Zhang W (2008) An integrated framework for learning fuzzy cognitive map using RCGA and NHL algorithm. In 2008 international conference on wireless communications, networking and mobile computing, WiCOM 2008. IEEE, pp 1–5. https://doi.org/10.1109/WiCom.2008.2527
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Abbaspour Onari, M., Jahangoshai Rezaee, M. A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry. Oper Res Int J 22, 2133–2171 (2022). https://doi.org/10.1007/s12351-020-00606-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-020-00606-1