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Optimization of Stochastic Discrete Event Simulation Models

Author Peter Buchholz



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DagSemProc.09261.23.pdf
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Peter Buchholz

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Peter Buchholz. Optimization of Stochastic Discrete Event Simulation Models. In Models and Algorithms for Optimization in Logistics. Dagstuhl Seminar Proceedings, Volume 9261, pp. 1-6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2009)
https://doi.org/10.4230/DagSemProc.09261.23

Abstract

Many systems in logistics can be adequately modeled using stochastic discrete event simulation models. Often these models are used to find a good or optimal configuration of the system. This implies that optimization algorithms have to be coupled with the models. Optimization of stochastic simulation models is a challenging research topic since the approaches should be efficient, reliable and should provide some guarantee to find at least in the limiting case with a runtime going to infinite the optimal solution with a probability converging to 1. The talk gives an overview on the state of the art in simulation optimization. It shows that hybrid algorithms combining global and local optimization methods are currently the best class of optimization approaches in the area and it outlines the need for the development of software tools including available algorithms.
Keywords
  • Stochastic discrete event simulation
  • optimization
  • hybrid algorithms

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