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The key to competitive success in most industries has moved beyond the confines of any single organization. In today’s business environment, competitiveness is heavily influenced by the ability of multiple organizations in a supply chain to synchronize and integrate their business activities (Jitpaiboon, GU, & Patel, 2015). According to the council of supply chain management professionals (CSCMP), supply chain management emphasizes the coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers, and integrating supply and demand management within and across organizations (Zhou & Ji, 2015). In manufacturing industries, the component parts and raw materials can equal up to 70% of the product cost. In such circumstances, the purchasing department has a key role in cost reduction, and supplier selection is one of the most important functions of purchasing management (Jadidi, Hong, Firouzi, Yusuff, & Zulkifli, 2008). Therefore, supplier selection is a fundamental issue of supply chain are that heavily contributes to the overall supply chain performance (Izadikhah, 2012) and it is a hard problem since supplier selection is typically a Multi Criteria Group Decision Making problem (Boran, Genc, Kurt, & Akay, 2009, and Izadikhah, 2012). In other words, a group of people, whether public or private, makes most decisions in an organization. This change of how to focus on the problem, i.e. the focus moves from that of on decision maker to that of a group of people, introduces the important issue of how base to aggregate the decision makers’ preference structure. So that, the problem of aggregating the individual pre-orders of decision makers in a single collective pre-order has been the target of several studies in the literature on group decision making. Historically, the first paper, which tackled this problem, was by Borda and by Condorcet. In the last decade, various studies have been under taken (Alencar, Almedia, & Morais, 2010). In other side of this coin, in general, Group Decision Making (GDM) problem can be defined as a decision problem with several alternatives and decision makers (DMs) that try to obtain the best solution(s) taking into account their opinions or preferences. As an important branch of GDM problems, Multiple Attribute (also often called Criteria) Group Decision Making (MAGDM) is commonly encountered in the real world and plays a key role especially in engineering and economy fields. The definition of MAGDM is described specifically as follow, multi DMs make judgments or evaluations by virtue of respective knowledge, experience and preference for a decision space (i.e. a finite set of alternatives), under multi attributes to rank all the alternatives or give evaluation information of each alternative. Then, decision results from each DM are aggregated to form an overall ranking result for all the alternatives (Pang, & Liang, 2012).
Some studies have reported that TOPSIS is a popular and widely used technique in dealing with Multi Attribute Decision Making (MADM) problem (i.e. Lin, Lee, Chang, & Ting, (2008), Huang & Li, (2012)). TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), developed by Hwang and Yoon in 1981, is a simple ranking method in conception and application. The standard TOPSIS method attempts to choose alternatives that simtaneously have the shortest distance from the positive ideal solution and the farthest distance from the negative-ideal solution. The positive ideal solution maximizes the benefit criteria and minimizes the cost criteria, whereas the negative-ideal solution maximizes the cost criteria and minimizes the benefit criteria. TOPSIS makes full use of attribute information, provides a cardinal ranking of alternatives, and does not require attribute preferences to be independent. To apply this technique, attribute values must be numeric, monotonically increasing or decreasing, and have commensurable units (Behzadian, Otaghsara, Yazdani, & Ignatious, 2012).