An Introduction to Multi-Objective Simulation Optimization
Susan R. Hunter(susanhunterpurdue.edu)
Abstract: We provide an introduction to the multi-objective simulation optimization (MOSO) problem aimed at researchers and practitioners who wish to begin working in this nascent and under-developed area. The MOSO problem is a nonlinear multi-objective optimization (MOO) problem in which multiple simultaneous and conflicting objective functions can only be observed with stochastic error. The solution to this problem is called the Pareto set, that is, the set of decision points for which no other feasible point is better on all objectives. We focus on the so-called a posteriori or vector optimization methods that characterize the entire Pareto set as input to the decision-making process. Since these methods enable the decision-maker to articulate solution preferences after the optimization is conducted, their popularity in the MOO literature has increased with the increase in available computing power. Likewise, recent MOSO application papers demonstrate an interest in returning several or all points in the Pareto set to the decision-maker. The recent availability of mature and efficient single-objective simulation optimization algorithms, coupled with ubiquitously available parallel computing power, makes identifying the Pareto set as the solution to a MOSO problem seem like an increasingly realistic goal. However, the methodological and theoretical development of specialized a posteriori MOSO algorithms remains in its infancy. To assist researchers beginning to work in this area, we provide an introduction to the current MOSO literature, review existing MOSO methods, and discuss some of the key open questions that remain in this emerging field.
Keywords: multi-objective simulation optimization; stochastic vector optimization
Category 1: Other Topics (Multi-Criteria Optimization )
Category 2: Other Topics (Optimization of Simulated Systems )
Citation: Under review.
Entry Submitted: 03/13/2017
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