A Stochastic, Dual-Criterion, Simulation-Optimization Algorithm for Generating Alternatives

  • Julian Scott Yeomans Schulich School of Business York University
Keywords: Dual-criterion Objectives, Population-based algorithms, Modelling-to-generate-alternatives, Simulation-Optimization, Metaheuristics

Abstract

Complex stochastic engineering problems are frequently inundated with incompatible performance requirements and inconsistent performance specifications that can be difficult to identify when supporting decision models must be constructed. Consequently, it is often advantageous to create a set of dissimilar options that afford distinctive approaches to the problem. These alternatives should satisfy the required system performance criteria and yet be maximally different from each other in their decision spaces. The approach for creating such maximally different solution sets is referred to as modelling-to-generate-alternatives (MGA). This paper describes a dual-criterion stochastic MGA procedure that can generate sets of maximally different alternatives for any simulation-optimization approach that employs a population-based search algorithm. This stochastic algorithmic approach is both computationally efficient and simultaneously produces the prescribed number of maximally different solution alternatives in a single computational run of the procedure.

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Author Biography

Julian Scott Yeomans , Schulich School of Business York University

OMIS Area, Schulich School of Business York University, 4700 Keele Street Toronto, ON, M3J 1P3 Canada

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Published
2019-07-02
How to Cite
Yeomans , J. S. (2019). A Stochastic, Dual-Criterion, Simulation-Optimization Algorithm for Generating Alternatives. IJRDO - Journal of Computer Science Engineering (ISSN: 2456-1843), 5(6), 01-10. Retrieved from https://ijrdo.org/index.php/cse/article/view/2987