Retrospective optimization of mixed-integer stochastic systems using dynamic simplex linear interpolation

Honggang Wang

European Journal of Operational Research, 2012, vol. 217, issue 1, 141-148

Abstract: We propose a family of retrospective optimization (RO) algorithms for optimizing stochastic systems with both integer and continuous decision variables. The algorithms are continuous search procedures embedded in a RO framework using dynamic simplex interpolation (RODSI). By decreasing dimensions (corresponding to the continuous variables) of simplex, the retrospective solutions become closer to an optimizer of the objective function. We present convergence results of RODSI algorithms for stochastic “convex” systems. Numerical results show that a simple implementation of RODSI algorithms significantly outperforms some random search algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Keywords: Simulation optimization; Stochastic systems; Mixed-integer optimization; Sample-path approximation (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:217:y:2012:i:1:p:141-148

DOI: 10.1016/j.ejor.2011.08.020

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