Evolutionary approaches to the design and organization of manufacturing systems
Computers and Industrial Engineering
Application of optimization techniques to parameter set-up in scheduling
Computers in Industry
Simulation-optimization using a reinforcement learning approach
Proceedings of the 40th Conference on Winter Simulation
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Hybrid evolutionary optimization of the operation of pipeless plants
Journal of Heuristics
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The optimization of such complex systems as manufacturing systems often necessitates the use of simulation. In this paper, the use of evolutionary algorithms is suggested for the optimization of simulation models. Several types of variables are taken into account. The reduction of computing cost is achieved through the parallelization of this method, which allows several simulation experiments to be run simultaneously. Emphasis is put on a distributed approach where several computers manage both their own local population of solutions and their own simulation experiments, exchanging solutions using a migration operator. After a first evaluation through a mathematical function with a known optimum, the benefits of this new approach are demonstrated through the example of a transport lot sizing and transporter allocation problem in a manufacturing flow shop system, which is solved using a distributed software implemented on a network of eight Sun workstations