Today green supply chain is considered all around the world and supplier selection has been changed regarding these green and carbon emission criteria, so green supplier selection has been a major problem in this area. In this study we use fuzzy time function to assist managers in green supplier selection under uncertainty and ambiguity. This function will consider derivation from the goal during the time and by using it, and we will be able to have the best supplier in every period after having some modification in legal limitations for green supplier selection criteria. We use a fuzzy TOPSIS to have better initial weighting in TODIM, a discrete multicriteria method based on prospect theory in uncertainty (known as TODIM in Portuguese) decision making method. The results indicated that our proposed approach can easily and effectively accommodate criteria with gains and loss functions during time and also by using this method we will have a more reasonable predict of our suppliers ranking in future and that will help us in future investment in these suppliers. Finally it has been shown in car industries in Iran. 1. Introduction In recent years, the European Union (EU) has established various environmental policies, including the RoHS (Restricted Use of Hazardous Substances in Electronics and Electrical Equipment) as well as WEEE (Waste Electronics and Electrical Equipment) directives. So far, environmental management has evolved to include boundary-spanning activities in the upstream and the downstream supply chains. Sirvastava defined green supply chain management (GSCM) as a combination of environmental and supply chain management activities, including product design, material selection, manufacturing process, final product delivery, and end-of-life product management. With GSCM, firms can select from a wide variety of suppliers and leverage resources throughout the firm to eliminate the environmental impact of supply chain activities, Tseng [1]. Firms typically expect their suppliers to surpass environmental compliance and to develop efficient and green product design. In addition, suppliers are expected to assess the life cycle of a product. Although the qualitative criteria are littered with subjective perception because the GSCM evolution criteria tend to be subjective, qualitative, or described with linguistic information. Thus, it is extremely difficult for the decision makers to express their preference using exact numerical values, Zhang et al. [2], so it is important to use linguistic number to distinguish between these criteria.
References
[1]
M. L. Tseng, “An assessment of cause and effect decision-making model for firm environmental knowledge management capacities in uncertainty,” Environmental Monitoring and Assessment, vol. 161, no. 1–4, pp. 549–564, 2010.
[2]
S. Zhang, S. Liu, and R. Zhai, “An extended GRA method for MCDM with interval-valued triangular fuzzy assessments and unknown weights,” Computers and Industrial Engineering, vol. 61, no. 4, pp. 1336–1341, 2011.
[3]
M. L. Tseng, K. H. Tan, R. J. Lina, and Y. Gengb, “Multicriteria analysis of green supply chain management using interval-valued fuzzy TODIM,” Knowledge-Based Systems, 2012.
[4]
M. Izadikhah, “Group decision making process for supplier selection with TOPSIS method under interval-valued intuitionistic fuzzy numbers,” Advances in Fuzzy Systems, vol. 2012, Article ID 407942, 14 pages, 2012.
[5]
L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.
[6]
H. J. Zimmermann, Fuzzy Set Theory and Its Application, International Thomson Publishing, Norwell, Mass, USA, 2001.
[7]
J. C. A. de Figueiredo and A. Perkusich, “Faults and timing analysis in real-time distributed systems: a fuzzy time Petri-net-based approach,” Fuzzy Sets and Systems, vol. 83, no. 2, pp. 143–168, 1996.
[8]
J. Yoneyama, “Robust stability and stabilization for uncertain Takagi-Sugeno fuzzy time-delay systems,” Fuzzy Sets and Systems, vol. 158, no. 2, pp. 115–134, 2007.
[9]
C. L. Hwang and K. Yoon, Multiple Attributes Decision Making Methods and Applications, Springer, Berlin, 1981.
[10]
M. Ekmek？io？lu, T. Kaya, and C. Kahraman, “Fuzzy multicriteria disposal method and site selection for municipal solid waste,” Waste Management, vol. 30, no. 8-9, pp. 1729–1736, 2010.
[11]
S. J. Chen and C. L. Hwang, “Fuzzy multi attribute decision making,” vol. 375 of lecture notes in economics and mathematical system, Springer, New York, 1992.
[12]
C. Chen, “Extensions of the TOPSIS for group decision-making under fuzzy environment,” Fuzzy Sets and Systems, vol. 114, no. 1, pp. 1–9, 2000.
[13]
T. Chu, “Selecting plant location via a fuzzy TOPSIS approach,” International Journal of Advanced Manufacturing Technology, vol. 20, no. 11, pp. 859–864, 2002.
[14]
T. Chu and Y. Lin, “Improved extensions of the TOPSIS for group decision making under fuzzy environment,” Journal of Information and Optimization Sciences, vol. 23273, 286 pages, 2002.
[15]
G. R. Jahanshahloo, F. H. Lotfi, and M. Izadikhah, “Extension of the TOPSIS method for decision-making problems with fuzzy data,” Applied Mathematics and Computation, vol. 181, no. 2, pp. 1544–1551, 2006.
[16]
T. C. Chu and Y. C. Lin, “An interval arithmetic based fuzzy TOPSIS model,” Expert Systems with Applications, vol. 36, no. 8, pp. 10870–10876, 2009.
[17]
D. Yong, “Plant location selection based on fuzzy TOPSIS,” International Journal of Advanced Manufacturing Technology, vol. 28, no. 7-8, pp. 839–844, 2006.
[18]
C. T. Chen, C. T. Lin, and S. F. Huang, “A fuzzy approach for supplier evaluation and selection in supply chain management,” International Journal of Production Economics, vol. 102, no. 2, pp. 289–301, 2006.
[19]
C. Kahraman, S. ？evik, N. Y. Ates, and M. Gülbay, “Fuzzy multi-criteria evaluation of industrial robotic systems,” Computers and Industrial Engineering, vol. 52, no. 4, pp. 414–433, 2007.
[20]
A. C. Kutlu and M. Ekmek？io？lu, “Fuzzy failure modes and effects analysis by using fuzzy TOPSIS integrated with fuzzy AHP,” in Proceedings of the 1st International Symposium on Computing in Science and Engineering, pp. 706–713, 2010.
[21]
T. Kaya and C. Kahraman, “Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology,” Expert Systems with Applications, vol. 38, no. 6, pp. 6577–6585, 2011.
[22]
A. C. Kutlu and M. Ekmek？io？lu, “Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP,” Expert Systems with Applications, vol. 39, no. 1, pp. 61–67, 2012.
[23]
L. F. A. M. Gomes and L. A. D. Rangel, “An application of the TODIM method to the multicriteria rental evaluation of residential properties,” European Journal of Operational Research, vol. 193, no. 1, pp. 204–211, 2009.
