Name
Abstract | ||
|---|---|---|
Simulation optimization is rapidly becoming a mainstream tool for simulation practitioners, as several simulation packages include add-on optimization tools. In this paper we are concentrating on an automated optimization approach that is based on adapting model parameters in order to handle uncertainty that arises from stochastic elements of the process under study. We particularly investigate the use of global search methods in this context, as these methods allow the optimization strategy to escape from sub-optimal (i.e., local) solutions and, in that sense, they improve the efficiency of the simulation optimization process. The paper compares several global search methods and demonstrates the successful application of the Particle Swarm Optimizer to simulation modeling optimization and design of a steelworks plant, a representative example of the stochastic and unpredictable behavior of a complex discrete event simulation model. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.5555/1030453.1030747 | Winter Simulation Conference |
Keywords | Field | DocType |
simulation package,simulation optimization,simulation optimization process,simulation practitioner,automated optimization approach,complex discrete event simulation,simulation optimisation,optimization strategy,general methodology,model parameter,global search strategy,global search method,add-on optimization tool,discrete event simulation,simulation model | Continuous optimization,Probabilistic-based design optimization,Mathematical optimization,Stochastic optimization,Global optimization,Simulation,Discrete optimization,Computer science,Multi-swarm optimization,Engineering optimization,Metaheuristic | Conference |
ISBN | Citations | PageRank |
0-7803-7615-3 | 1 | 0.48 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George D. Magoulas | 1 | 826 | 81.73 |
| Tillal Eldabi | 2 | 315 | 32.51 |
| Ray J. Paul | 3 | 144 | 17.39 |