TOPSIS in Business Analytics

TOPSIS in Business Analytics

William P. Fox (Naval Postgraduate School, USA)
Copyright: © 2014 |Pages: 11
DOI: 10.4018/978-1-4666-5202-6.ch226
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Background

TOPSIS was the result of work done by Yoon and Hwang (1980). TOPSIS has been used in a wide spectrum of comparisons of alternatives including: item selection from among alternatives, ranking leaders or entities, remote sensing in regions, data mining, and supply chain operations. TOPSIS is chosen over other methods because it orders the feasible alternatives according to their closeness to an ideal solution (Malezewski, 1996).

Napier (1992) provided some analysis of the use of TOPSIS for the department of defense in industrial base planning and item selection. For years the military used TOPSIS to rank order the systems’ request from all the branches within the service for the annual budget review process (Fox, 2012) as well as being taught again in as part of decision analysis. Current work is being done to show the ability of TOPSIS to rank order nodes of a dark or social network across all the metrics of social network analysis (Fox, 2012; Fox & Everton, 2013).

In manufacturing analysis, Wang et al. (2008) proposed two methods to improve TOPSIS for multi-response optimization using Taguchi’s loss function. Ozturk and Batuk (2011) used TOPSIS for spatial decisions and then linked to geographical information systems (GIS) operations for flood vulnerability. Olson and Wu (2005, 2006) have shown how TOPSIS may be used for data mining and analysis in credit card score data. Olson (2006) presented a comparison of weights (centroid weights, equal weights, and weights by linear regression) in TOPSIS models using baseball data where their conclusion is that accurate weights in TOPSIS are crucial to success.

Key Terms in this Chapter

Decision Weights (Eigenvectors): These are the subjective decision weights that are either provided by the decision maker or computed from the pair-wise comparison matrix as eigenvectors to the maximum eigenvalue.

TOPSIS: Technique for order preference by similarity to ideal solution.

Decision Matrix: This is the m x n matrix of the m alternatives by n attributes.

Analytical Hierarchy Process: (AHP) is a technique created by Saaty using a 9 point scale to rank alternatives in a decision process and is useful to get decision maker weights for use in TOPSIS.

S+: This represents the distance of the computed value to the ideal solution.

S-: This represents the distance of the computed value to the negative ideal solution.

Normalization Process: The normalization process for TOPSIS differs from others process in that TOPSIS considered distances.

Ideal Solution: Although assume unachievable the ideal and negative ideal solution are used to compute the ratios of distances from the ideal and negative ideal solution.

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