A Multiple Attribute Utility Theory Approach to Ranking and Selection

Abstract

Managers of large industrial projects often measure performance by multiple attributes. For example, our paper is motivated by the simulation of a large industrial project called a land seismic survey, in which project performance is based on duration, cost, and resource utilization. To address these types of problems, we develop a ranking and selection procedure for making comparisons of systems (e.g., project configurations) that have multiple performance measures. The procedure combines multiple attribute utility theory with statistical ranking and selection to select the best configuration from a set of possible configurations using the indifference-zone approach. We apply our procedure to results generated by the simulator for a land seismic survey that has six performance measures, and describe a particular type of sensitivity analysis that can be used as a robustness check.Simulation, Ranking and Selection, Multiple Attribute Utility Theory

A Fuzzy Multiple Attribute Utility Model for Intelligent Building Assessment

“…This approach is known as multiattribute utility theory (MAUT) (Beliakov, Warren 2001). A MAUT analysis of alternatives explicitly identifies the measures that are used to evaluate the alternatives, and helps to identify those alternatives that perform well on a majority of these measures, with a special emphasis on the measures that are considered to be relatively more important (Butler et al 2001).…”

“…Jiménez et al (2003) described a decision support system based on an additive or multiplicative multiattribute utility model for identifying the optimal strategy. Butler et al (2001) described the application and detailed of how they used the simulator, MAUT, and statistical ranking and selection to select the best project configuration of possible configurations. Sohn et al (2001) proposed a method to aggregate multi-stakeholder opinions and assimilate the public opinions during the course of the decision making process.…”

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“…Many methodologies can be found in the literature to address ranking problems (Butler et al 2001;Hochbaum and Levin 2006). In some of them, different rankings from the same set of features are compared by means of correlation indexes such as Kendall's tau or Spearman's rho correlation coefficients (Glover et al 2011;Lapata 2006).…”

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