Applying a multi-criteria group decision-making method in a probabilistic environment for supplier selection (Case study: Urban railway in Iran)

Subject Areas : Design of Experiment

Azam Modares ¹ , Nasser Motahari Farimani ² , Vahideh Bafandegan Emroozi ³

1 -
2 -
3 -

Received: 2022-01-21 Accepted : 2023-02-19 Published : 2023-06-01

Keywords: VIKOR, Group decision-making, Supplier selection, Bayesian BWM, aggregating of DMs opinions,

Abstract :

Supplier selection of urban train signaling equipment is one of the main problems in urban railways due to the serious impact of this equipment on the safety of travelers. The supplier's selection is a multi-criteria decision-making (MCDM) problem, in which the preferences over criteria are highly dependent on the opinions of decision-makers (DMs). The focus of the present study is to provide an appropriate model of multi-criteria group decision making in a probabilistic setting to effectively combine DMs' judgments. For this purpose, the Bayesian hierarchical model with the best-worst method (BWM) is used to determine the weights of criteria and the Vise Keriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is used to prioritize suppliers. Bayesian BWM aggregates the opinions of all DMs at once, instead of averaging the individual opinions of the DMs that underlie MCDM methods. In this method, the probability of preference of one criterion over one another is calculated using Markov-chain Monte Carlo (MCMC), in addition to the weight of criteria, so that the confidence between pairs of criteria is revealed and ranking of criteria become more certain. Given that the average obtained confidence levels are 0.95, the validity of the Bayesian BWM results is confirmed. For different values of the VIKOR parameter, the third supplier will have the highest rating and the fifth supplier will have the lowest rating. The introduced multi-criteria decision model in this research will help the decision-makers of the urban railway company and other organizations with several suppliers to be able to select the best supplier by considering the relevant criteria.

References:

Aboutorab, H., Saberi, M., 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.
Ahadi, M., Mahpour, A., & Taraghi, V. (2018). A Combined Fuzzy Logic and Analytical Hierarchy Process Method for Optimal Selection and Locating of Pedestrian Crosswalks. journal of optimization in industrial engineering, 11(2), 79-89. doi:10.22094/JOIE.2017.458.0
Alinezhad, A., & Seif, A. (2020). Application of Fuzzy Analytical Hierarchy Process and Quality Function Deployment Techniques for Supplier's Assessment. journal of optimization in industrial engineering, 13(2), 279-289. doi:10.22094/JOIE.2020.27.1
Bafandegan Emroozi, V., & Fakoor, A. (2023). A new approach to human error assessment in financial service based on the modified CREAM and DANP. Journal of Industrial and Systems Engineering.
Bafandegan Emroozi, V., Modares, A., & Mohemi, Z. (2022). Presenting a model for diagnosing the implementation of total quality management based on performance expansion model (Case: Simorgh Rail Transportation Company). Road. doi:10.22034/ROAD.2022.319629.2007
Bai, C., & Sarkis, J. (2014). Determining and applying sustainable supplier key performance indicators. Supply Chain Management: An International Journal, 19(3), 275-291.
Banaeian, N., Moblia, H., & Fahimnia, B. (2016). Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry. Computers & Operations Research.
Blagojective, B., Srdjevic, B., Srdjevic, Z., & Zoranovic, T. (2016). Heuristic aggregation of individual judgment in AHP group decision making using simulated annealing algorithm. Information Science, 330, 260-273.
Burgess, K., Singh, P., & Koroglu, R. (2006). Supply chain management: A structured literature review and implications for future research. International Journal of Operations & Production Management, 26(7), 703-729.
Büyüközkan, G., & Çifçi, G. (2011). A novel fuzzy multi-criteria decision framework for sus- tainable supplier selection with incomplete information. Comput. Ind, 62(2), 164-174.
Cakravastia, A., & Takahashi, K. (2004). Integrated model for supplier selection and negotiation in a make-to-order environment. International Journal of Production Research, 42(21), 4457-4474.
Ecer, F., & Pamucar, D. (2020). Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model. Journal of Cleaner Production, 266.
Forman, E., & Peniwati, K. (1998). Aggregation ing individual judgments and priorities with the analytic hierarchy process. European journal of operation al research, 108(1), 165-169.
Gilks, W. (1995). Markov Chain Monte Carlo in Practice. London, UK: Informa UK Limited.
Goldstein, D. (2011). Doing Bayesian Data Analysis: A Tutorial with R and BUGS, John K. Kruschke. Academic Press, Elsevier. Journal of Economic Psychology, 32(5), 724-725.
Goren, H. (2018). A decision framework for sustainable supplier selection and order allocation with lost sales. J. Clean. Prod, 183, 1156-1169.
Hosseini, M., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68-87.
Hsu, C., & Hu, A. (2009). Applying hazardous substance management to supplier se- lection using analytic network process. J. Clean Prod, 17(2), 255-264.
Hsu, C.-W., & Hu, A. H. (2007). Application of analytic network process on supplier selection to hazardous substance management in green supply chain management. Proceed- ings of the IEEE International Conference on Industrial Engineering and Engineering Managemen.
Kazemitash, N., Fazlollahtabar, H., & Abbaspour, M. (2021). Rough Best-Worst Method for Supplier Selection in Biofuel Companies based on Green criteria. Operational Research in Engineering Sciences: Theory and Applications, 4(2), 1-12.
Kumar Kar, A. (2014). Revisiting the supplier selection problem: An integrated approach for group decision support. Expert Systems with Applications, 41, 2762-2771.
Kumar, A., Jain, V., & Kumar, S. (2014). A comprehensive environment friendly approach for supplierselection. Omega, 42, 109–123.
Lei, F., Wei, G., & Gao, H. (2020). TOPSIS Method for Developing Supplier Selection with Probabilistic Linguistic Information. Int. J. Fuzzy Syst, 22, 749–759.
Li, J., & Wang, J. (2017). An Extended QUALIFLEX Method Under Probability Hesitant Fuzzy Environment for Selecting Green Suppliers. Int. J. Fuzzy Syst, 19, 1866-1879.
Liu, A., Xiao, Y., Ji, X., Wang, K., & Tsai, S. (2018). A Novel Two-Stage Integrated Model for Supplier Selection of Green Fresh Product. Sustainability, 10(7), 2371.
Liu, H., Quan, M., Li, Z., & Wang, Z. (2019). A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment. Information Sciences, 486, 254-270.
Liu, Y., Eckert, C., Yannou-Le Bris, G., & Petit, G. (2019). A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain. Comput. Ind. Eng, 127, 196-212.
Lo, H., Liou, J., Huang, C., & Chuang, Y. (2020). A new soft computing approach for analyzing the influential relationships of critical infrastructures. Int. J. Crit. Infrastruct. Prot, 28(100336).
Luthra, S., Govindan, K., Kannan, D., Kumar Mangla, S., & Prakash Garg, C. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of Cleaner Production, 140(3), 1686-1698.
doi:10.1016/j.jclepro.2016.09.078
Modares, A., Bafandegan Emroozi, V., & Mohemmi, Z. (2021). ‏Evaluate and control the factors affecting the equipment reliability with the approach Dynamic systems simulation, Case study: Ghaen Cement Factory. Journal of Quality Engineering and Management, 11(2), 89-106.
Modares, A., Motahari Farimani , N., & Bafandegan Emroozi, V. (2022). A vendor-managed inventory model based on optimal retailers selection and reliability of supply chain. Journal of Industrial and Management Optimization, 19(5), 3075-3106. doi:10.3934/jimo.2022078
Modares, A., Motahari Farimani, N., & Bafandegan Emroozi, V. (2022). A new model to design the suppliers portfolio in newsvendor problem based on product reliability. Journal of Industrial and Management optimization. doi:10.3934/jimo.2022124
Modares, A., Motahari Farimani, N., & Bafandegan Emroozi, V. (2022). Developing a Newsvendor Model based on the Relative Competence of Suppliers and Probable Group Decision-making. Industrial Management Journal, 14(1), 115-142. doi:10.22059/IMJ.2022.331988.1007872
Mohammadi, M., & Rezaei, J. (2020). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96(102075).
Morais, D. C., & Ahmeid, A. T. (2012). Group decision making on water resources based on analysis of individuals ranking. Omega, 40(1), 42-52.
Motahari Farimani, N., Ghanbarzade, J., & Modares, A. (2022). A New Approach for Pricing Based on Passengers’ Satisfaction. Transportation Journal, 61(2), 123-150. doi:10.5325/transportationj.61.2.0123
Nepal, B., & Yadav, O. (2015). Bayesian belief network-based framework for sourcing risk analysis during supplier selection. International Journal of Production Researcch, 53(20), 6114-6135.
Noci, G. (1997). Designing ‘green’ vendor rating systems for the assessment of a supplier’s environmental performance. Eur. J. Purchasing Supply Manage, 3(2), 103-114.
Opricovic, S., & Tzeng, G. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178, 514–529.
Opricovic, S., & Tzeng, G. (2004). Compromise solution by MCDM methods: acomparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res, 156, 445-455.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
Roostaee, R., Izadikhah, M., Hosseinzadeh Lotfi, F., & Rostamy-Malkhalifeh, M. (2012). A Multi-Criteria Intuitionistic Fuzzy Group Decision Making Method for Supplier Selection with VIKOR Method. International Journal of Fuzzy System Applications, 2(1), 1-17.
doi:DOI:10.4018/IJFSA.2012010101
Roozkhosh, P., & Kazemi, M. (2022). Application of Internet of Things in Green Supply Chain and Investigating the Effective Factors for Selecting a Green Supplier: A Case Study: Mashhad Rubber Factory. Supply Chain Management Journal, 24(75), 61-73.
Roozkhosh, P., & Motahari Farimani, N. (2022). Designing a new model for the hub location-allocation problem with considering tardiness time and cost uncertainty. International Journal of Management Science and Engineering Management, 1-15.
Roozkhosh, P., Pooya, A., & Agarwal, R. (2022). Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach. Operations Management Research, 1-21.
Sanayei, A., Mousavi, S., & Yazdankhah, A. (2010). Group decision-making process for supplier selection with Vikor under fuzzy environment. Expert Syst Appl, 37(1), 24-30.
Sayadi, M. K., Heydari, M., & Shahanaghi, K. (2009). Extension of VIKOR method for decision making problem with interval numbers. Applied Mathematical Modelling, 33(5), 2257-2262.
doi: 10.1016/j.apm.2008.06.002
Shahhoseini, A., & Yousefinejad Attari, M. (2018). Hybrid Techniques of Multi-Criteria Decision-Making for Location of Automated Teller Machines (ATMs): Shahr Bank Branches in Tehran, 1st District Municipality. Journal of optimization in industrial engineering, 11(2), 139-148. doi:10.22094/JOIE.2017.706.1445
Wan, S., Xu, G., & Dong, J. (2017). Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment. Inf. Sci, 385-386, 19-38.
Wang, X., & Cai, J. (2017). A group decision-making model based on distance-based VIKOR with incomplete heterogeneous information and its application to emergency supplier selection. Cybernetic, 46, 501-529.
Zhang, D., Zhang, J., & Lai, K.-K. (2009). An novel approach to supplier selection based on vague sets group decision. Expert Systems with Applications, 36, 9557-9563.