Contractor Selection Using Integrated Goal Programming and Fuzzy ELECTRE

Contractor Selection Using Integrated Goal Programming and Fuzzy ELECTRE

Ahmad Jafarnejad Chaghooshi (Department of Industrial Management, University of Tehran, Tehran, Iran), Ehsan Khanmohammadi (Department of Industrial Management, University of Tehran, Tehran, Iran), Maryam Faghei (Department of Industrial Management, University of Tehran, Tehran, Iran) and Amir Karimi (Department of Industrial Management, University of Tehran, Tehran, Iran)
Copyright: © 2014 |Pages: 22
DOI: 10.4018/ijsds.2014070104
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Abstract

Outsourcing plays an important role in the success of organizations. One of the risks associated with outsourcing is inappropriate contractor selection which significantly influences the implementation of projects in terms of time, quality, and cost. In this study, we applied multi-criteria decision-making techniques in order to determine the best contractor using criteria such as reputation, offered price, and technical capacity. This study is primarily aimed at identifying important criteria of contractor selection, determining the significance of the criteria, and designing a framework for selection of the most appropriate contractor. Important criteria for selecting contractors were extracted from the literature and experts' views were collected using questionnaire. Accordingly, six criteria were selected and their weights were determined by the application of goal programming. Finally, contractors were ranked and the best contractor was selected using fuzzy ELECTRE technique with trapezoidal fuzzy numbers.
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2. Literature Review

2.1. Contractor Selection Methods

Most real-world problems have different, conflicting and multiple measurement criteria. In terms of decision-making, different and contradictory qualitative factors are evaluated and suitable solutions are selected among several alternatives. The multi-attribute analysis (MAA), multi-attribute utility theory (MAUT), analytic hierarchy process (AHP), analytic network process (ANP), the technique for order preference by similarity to ideal solution (TOPSIS), PROMETHEE, fuzzy logic, matrix approach, case-based reasoning (CBR), cluster analysis (CA), and graph theory are amongst the most frequently used approaches in the contractor selection literature (Abudayyeh et al., 2007; Araz et al., 2007; Boran et al., 2009; Cheng and Li, 2004; Darvish et al., 2009; El-Sawalhi et al., 2007; Fong & Choi, 2000; Hatush & Skitmore, 1998; Holt, 1996, 1998; Holt et al., 1994; Juan, 2009; Lin et al., 2010; Mahdi et al., 2002; Ng, 2001; Plebankiewicz, 2012; Topcu, 2004; Wong et al., 2003; Zavadskas & Vilutienė, 2006).

For instance, Araz et al. (2007) developed an outsourcer evaluation and management system for a textile company by the use of fuzzy goal programming and PROMETHEE. Considering the interdependent influences specified in their model, Cheng and Li (2004) proposed an ANP model for contractor selection. In their research study, Darvish et al. (2009) showed how the graph theory and matrix methods may be served as a decision analysis tool for contractor selection. In addition, Fong and Choi (2000) applied the AHP in a contractor selection problem which would help construction clients to identify contractors with the best potential to deliver satisfactory outcomes in a final contractor selection process. Jajimoggala et al. (2011) presented an integrated approach for maintenance policy selection using fuzzy ANP and goal programming based on fuzzy preemptive priority. Sodenkamp (2012) proposed a multi-criteria decision analysis method to facilitate making supplier selection decisions by the distributed groups of experts and improving quality of the order allocation decisions. Golara et al. (2012) introduced a dynamic mixed integer linear programming model for the design and optimization of closed-loop supply chain network capable of recovering glass containers. Guchhait (2012) developed an inventory control problem under two-level trade-credit policy with fuzzy inventory costs. Fuzzy sets were for the first time used to build a contractor selection model by Nguyen (1985) taking into consideration criteria of cost, presentation of bid information and past experience.

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