Toward expert simulation systems in job shop scheduling
AFIPS Conference Proceedings; vol. 55 1986 National Computer Conference
The shifting bottleneck procedure for job shop scheduling
Management Science
Simulation optimization using simultaneous replications and event time dilation
Proceedings of the 29th conference on Winter simulation
Recursive simulation to aid models of decision making
Proceedings of the 32nd conference on Winter simulation
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Simulation-based optimization: practical introduction to simulation optimization
Proceedings of the 35th conference on Winter simulation: driving innovation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Using simulation based approach to improve on the mean cycle time performance of dispatching rules
WSC '05 Proceedings of the 37th conference on Winter simulation
The improved sweep metaheuristic for simulation optimization and application to job shop scheduling
Proceedings of the 40th Conference on Winter Simulation
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We describe a metaheuristic algorithm for simulation optimization. Traditionally, discrete event simulation optimization is carried out by multiple simulation runs executed sequentially. At the end of each simulation run, the run is evaluated (using model output - black box approach) by an objective function. If we carry out simulation runs simultaneously, then we can evaluate (using model internal data - white box approach) different simulation runs during their execution before the end is reached. Thus, we can eliminate the inferior runs early and allow only the most promising runs to continue to the end. We explore this parallel competition of simulation models on a single processor computer. Applications of the algorithm to traveling salesman and job shop scheduling problems are presented. In conclusion, our results suggest that the algorithm is a suitable approach for solving some combinatorial problems, and it represents a promising "nonsequential" avenue for simulation optimization.