A Proposed Decision Support System for Location Selection using Fuzzy Quality Function Deployment

Location selection is a multi-criteria decision making (MCDM) problem that includes both tangible and intangible factors (Kahraman et al., 2003). Location problems involve the determination of the location of one or more new facilities in one or several potential sites. Obviously, the number of sites should be at least equal to the number of new facilities being located (Heragu, 2006). The majority of research approaches for location selection focus on heuristics (Berman et al., 2001) and mathematical programming, such as integer programming (Melkote & Daskin, 2001), dynamic programming (Canel et al., 2001), and nonlinear programming (Nanthavanij & Yenradee, 1999). However, the mathematical programming cannot consider qualitative criteria. Moreover, the decision makers do not contribute in decision making process, and the roll of experience is ignored. Another problem is appeared when there are a lot of alternatives. In this condition, the decision makers prefer to select a group of alternatives, called pre-qualified alternatives. Then, decision makers will select the best alternative from the new group. A pre-qualification stage decreases time and costs of the decision making process. In addition, the characteristics of products affect on the location of factories. For instance, the location of a factory that manufactures heavy products should be different from ordinary factories. However in previous researches, the relationship between location criteria and product criteria is ignored. In this chapter, we propose a new model for location selection consisting of two phases. In the first phase (i.e., pre-qualification stage), we propose a novel fuzzy based decision making model to take into account both qualitative and quantitative criteria. In the second phase (i.e., final selection), quality function deployment (QFD) is applied to select the best location to establish a factory. Not only QFD is a planning tool used in quality control, but also it is a useful tool in decision making. QFD enables us to consider the relationship between product criteria and location criteria to select the best location. This feature distinguishes QFD from other decision making tools. Moreover, the fuzzy sets theory, linguistic variables, and triangular fuzzy numbers are utilized. A major contribution of the fuzzy sets theory is its capability of representing vague data. In other words, it can