Swarm intelligence
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Knowledge based Simulation and Optimization in Manufacturing Organisation and Logistics
Proceedings of the 12th European Simulation Multiconference on Simulation - Past, Present and Future
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Allocation of simulation runs for simulation optimization
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
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The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a "best possible" parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. This paper presents a fast converging procedure as a combination of heuristic approaches, namely Particle Swarm Optimization and Genetic Algorithm, within a material flow simulation to close this gap. Our integrated implementation allows automated, distributed simulation runs for practical, complex production systems. First results show the proof of concept with a reference model and demonstrate the benefits of combinatorial and parallel processing.