Data Mining for Multicriteria Single Facility Location Problems

Data Mining for Multicriteria Single Facility Location Problems

Seda Tolun, Halit Alper Tayalı
Copyright: © 2016 |Pages: 24
DOI: 10.4018/978-1-5225-0075-9.ch007
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This chapter focuses on available data analysis and data mining techniques to find the optimal location of the Multicriteria Single Facility Location Problem (MSFLP) at diverse business settings. Solving for the optimal of an MSFLP, there exists numerous multicriteria decision analysis techniques. Mainstream models are mentioned in this chapter, while presenting a general classification of the MSFLP and its framework. Besides, topics from machine learning with respect to decision analysis are covered: Unsupervised Principal Components Analysis ranking (PCA-rank) and supervised Support Vector Machines ranking (SVM-rank). This chapter proposes a data mining perspective for the multicriteria single facility location problem and proposes a new approach to the facility location problem with the combination of the PCA-rank and ranking SVMs.
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Business research literature refers to various techniques to solve facility location selection problems and related models such as factor-rating, transportation method of linear programming, center of gravity method, median method, volume-cost analysis, and Ardalan method (Chase et al., 2001; Ozcakar, 2015). An internet-based service provided by NEOS Server (“network-enabled optimization solver”) offers valuable resource at their website (“NEOS Guide”, 2015) about solvers for numerical optimization and also puts forward a broad classification of the optimization problems. While these solution approaches are mainstream; state-of-the-art techniques such as simulation, heuristics, and geographical information systems (GIS) are also used to determine the optimal facility locations (Ballou, 2004).

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