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Data Mining for Multicriteria Single Facility Location Problems

Data Mining for Multicriteria Single Facility Location Problems

Seda Tolun, Halit Alper Tayalı
Copyright: © 2020 |Pages: 24
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch063
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MLA

Tolun, Seda, and Halit Alper Tayalı. "Data Mining for Multicriteria Single Facility Location Problems." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1248-1271. https://doi.org/10.4018/978-1-7998-2460-2.ch063

APA

Tolun, S. & Tayalı, H. A. (2020). Data Mining for Multicriteria Single Facility Location Problems. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1248-1271). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch063

Chicago

Tolun, Seda, and Halit Alper Tayalı. "Data Mining for Multicriteria Single Facility Location Problems." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1248-1271. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch063

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Abstract

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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