Evaluation of supplier performance of high-speed train based on multi-stage multi-criteria decision-making method

Abstract In the process of developing the industry of high-speed train manufacturing (HSTM), a key problem is to evaluate the performance of suppliers by considering the special characteristics of suppliers. To address this problem, a multi-stage multi-criteria decision-making (MCDM) method is proposed based on the evidential reasoning approach. A criteria framework of the supplier performance evaluation (SPE) problem is constructed first. To describe quantitative and qualitative assessments with uncertainty, belief distributions are adopted to model the problem. Then, the rule-based information transformation technique is introduced to unify quantitative data and qualitative information. Suppose that SPE is performed at multiple stages and a decision maker may prefer the suppliers with the performance that is continuously increasing at these stages. On this assumption, stage weights are determined objectively by considering the dynamic nature of SPE at multiple stages. A real SPE problem in the HSTM enterprise located in Changzhou, Jiangsu Province, China is investigated to verify the validity and applicability of the proposed multi-stage MCDM method.

[1]  Shi-Jin Feng,et al.  In situ experimental study on high speed train induced ground vibrations with the ballast-less track , 2017 .

[2]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[3]  W. C. Benton,et al.  A systematic assessment of supplier selection literature – State-of-the-art and future scope , 2016 .

[4]  Yuh-Jen Chen,et al.  Structured methodology for supplier selection and evaluation in a supply chain , 2011, Inf. Sci..

[5]  Xifeng Liang,et al.  A new method to measure the aerodynamic drag of high-speed trains passing through tunnels , 2017 .

[6]  Francesco Corman,et al.  A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system , 2017 .

[7]  Witold Pedrycz,et al.  An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment , 2017, Eur. J. Oper. Res..

[8]  W. C. Benton,et al.  Vendor selection criteria and methods , 1991 .

[9]  T. C. Wong,et al.  Evaluation of user satisfaction using evidential reasoning-based methodology , 2014, Neurocomputing.

[10]  Jurgita Antucheviciene,et al.  A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations , 2017, Comput. Ind. Eng..

[11]  Kannan Govindan,et al.  Multi criteria decision making approaches for green supplier evaluation and selection: a literature review , 2015 .

[12]  Dong-Ling Xu,et al.  An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis , 2012, Ann. Oper. Res..

[13]  Cathal Heavey,et al.  A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain , 2017, Eur. J. Oper. Res..

[14]  Chandra Prakash Garg,et al.  An integrated framework for sustainable supplier selection and evaluation in supply chains , 2017 .

[15]  James Nga-Kwok Liu,et al.  Application of decision-making techniques in supplier selection: A systematic review of literature , 2013, Expert Syst. Appl..

[16]  Enrique Herrera-Viedma,et al.  Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence , 2018, IEEE Transactions on Fuzzy Systems.

[17]  Li Kun,et al.  Research on classification of suppliers of high-end equipment manufacturing enterprises based on the degree of interdependence , 2013, 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings.

[18]  F. Chiclana,et al.  Strategic weight manipulation in multiple attribute decision making , 2018 .

[19]  Yong Deng,et al.  An evidential approach to physical protection system design , 2014 .

[20]  Millie Pant,et al.  Integrating DEA with DE and MODE for sustainable supplier selection , 2017, J. Comput. Sci..

[21]  Narges Banaeian,et al.  Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry , 2018, Comput. Oper. Res..

[22]  Dragan Simic,et al.  50 years of fuzzy set theory and models for supplier assessment and selection: A literature review , 2017, J. Appl. Log..

[23]  Anjali Awasthi,et al.  A fuzzy multicriteria approach for evaluating environmental performance of suppliers , 2010 .

[24]  Prasanta Kumar Dey,et al.  A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments , 2015 .

[25]  Jing Xiao,et al.  Managing consensus and weights in iterative multiple-attribute group decision making , 2016, Appl. Soft Comput..

[26]  Gül E. Okudan Kremer,et al.  A regional information-based multi-attribute and multi-objective decision-making approach for sustainable supplier selection and order allocation , 2018, Journal of Cleaner Production.

[27]  Prasanta Kumar Dey,et al.  Strategic supplier performance evaluation: a case-based action research of a UK manufacturing organisation , 2015 .

[28]  E. Xie,et al.  Performance feedback and supplier selection: A perspective from the behavioral theory of the firm , 2017 .

[29]  Paolo Toth,et al.  Timetable Optimization for High-Speed Trains at Chinese Railways , 2016, Electron. Notes Discret. Math..

[30]  Jian-Bo Yang,et al.  Research project evaluation and selection: an evidential reasoning rule-based method for aggregating peer review information with reliabilities , 2015, Scientometrics.

[31]  Zeshui Xu,et al.  A Dynamic Weight Determination Approach Based on the Intuitionistic Fuzzy Bayesian Network and Its Application to Emergency Decision Making , 2018, IEEE Transactions on Fuzzy Systems.

[32]  Sirawadee Arunyanart,et al.  Supplier selection towards uncertain and unavailable information: An extension of TOPSIS method , 2018 .

[33]  Frédéric Dobruszkes,et al.  Mind the services! High-speed rail cities bypassed by high-speed trains , 2017 .

[34]  W. V. D. Valk,et al.  Purchasing pension advisory services in Sweden – An interpretive investigation into service conceptions and supplier selection , 2017 .

[35]  Xiaowei Xu,et al.  Multi-criteria decision making approaches for supplier evaluation and selection: A literature review , 2010, Eur. J. Oper. Res..

[36]  Sheng-Lin Chang,et al.  Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets ☆ , 2009 .

[37]  Ali Osman Kusakci,et al.  A hybrid type-2 fuzzy based supplier performance evaluation methodology: The Turkish Airlines technic case , 2017, Appl. Soft Comput..

[38]  Fabio Gagliardi Cozman,et al.  Sequential decision making with partially ordered preferences , 2011, Artif. Intell..

[39]  A. Manello,et al.  The influence of reputation on supplier selection: An empirical study of the European automotive industry , 2019, Journal of Purchasing and Supply Management.

[40]  Nursel Öztürk,et al.  Supplier selection and performance evaluation in just-in-time production environments , 2011, Expert Syst. Appl..

[41]  Weijie Chen,et al.  An integrated method for supplier selection from the perspective of risk aversion , 2017, Appl. Soft Comput..

[42]  Francisco Rodrigues Lima Junior,et al.  A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection , 2014, Appl. Soft Comput..

[43]  Witold Pedrycz,et al.  Building consensus in group decision making with an allocation of information granularity , 2014, Fuzzy Sets Syst..

[44]  Yong Deng,et al.  A new fuzzy dempster MCDM method and its application in supplier selection , 2011, Expert Syst. Appl..

[45]  G. De Roeck,et al.  Dynamic response of a train–bridge system under collision loads and running safety evaluation of high-speed trains , 2014 .

[46]  Jian-Bo Yang,et al.  Environmental impact assessment using the evidential reasoning approach , 2006, Eur. J. Oper. Res..

[47]  Prasenjit Chatterjee,et al.  Integrated QFD-MCDM framework for green supplier selection , 2017 .

[48]  Xuan Li,et al.  Weapon System Capability Assessment under uncertainty based on the evidential reasoning approach , 2011, Expert Syst. Appl..

[49]  Zeshui Xu,et al.  Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans , 2018, International journal of environmental research and public health.

[50]  Xinyang Deng,et al.  Supplier selection using AHP methodology extended by D numbers , 2014, Expert Syst. Appl..

[51]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[52]  Jian-Bo Yang,et al.  On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[53]  Joseph Sarkis,et al.  Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework , 2015 .

[54]  Lori Tavasszy,et al.  Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP , 2014, Expert Syst. Appl..