An approach to ranking and selection for multiple performance measures

Multiobjective simulation-based methodologies for medical decision making.

“…In our simulation modeling, we utilize an approach, similar to Morrice et al (Morrice 1998(Morrice , 1999, to this problem: convert multiple performance measures to a scalar performance measure using multiple attribute utility (MAU) theory (Keeney and Raiffa 1992). MAU theory can be used with or without costing approach when good cost data are not available or when cost is not suitable as a measure of performance.…”

“…In our model, we follow the same combination technique of Morrice et al (1998Morrice et al ( ,1999 to link R&S procedure with MAU theory.…”

“…In our simulation model, we used the MAU to unify multi tests (multiperformance measures) into one scale (Morrice, 1998). Thus we utilized a RankingSelection procedure of Rinott, (1978), to select the system with the largest expected performance measures (Bechhofer 1995, Kim-Nelson 2001, Nelson-Miler 1995.…”

“…Similar to the solution achieved by Morrice et al (1998 and, in industry, our solution does not require complications like a covariance matrix, which was used by Gupta (1979).…”

“…And its goal is to select the best project configuration from a set of K configurations when project performance is measured over multiple performance measures. (Morrice 1998) 6. There is another formulation; the Bayesian approach: Instead of providing a A good procedure is one that delivers the desired the probability of correct selection (PCS) efficiently (with minimal simulated data) and is robust to its underlying assumptions.…”