Abstract
Multi-attribute decision making (MADM) is commonly used when we are comparing more than two courses of actions or alternatives based upon many selected criteria. In this chapter, we present methodologies to conduct MADM analysis. These methodologies include data envelopment analysis (DEA), simple additive weighting (SAW), analytical hierarchy process (AHP), and the technique of order preference by similarity to ideal solution (TOPSIS). We describe each methodology, provide some strengths and limitations of each, discuss tips for sensitivity analysis, and provide two examples to illustrate each method. Additionally, we provide a carry-through example based upon social network analysis so that we can compare these methods.
Keywords
- Data Envelopment Analysis
- Analytic Hierarchy Process
- Pairwise Comparison Matrix
- Geometric Distance
- Negative Ideal Solution
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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We thank all the reviewers of this and other material used to produce this chapter. These views are the views of the author and not of the Department of Defense, the Department of the Navy, or the Naval Postgraduate School.
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Fox, W.P. (2015). Introduction to Multi-attribute Decision Making in Business Analytics. In: García Márquez, F., Lev, B. (eds) Advanced Business Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-11415-6_4
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DOI: https://doi.org/10.1007/978-3-319-11415-6_4
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