Simulation and optimization in production planning: a case study
Decision Support Systems
Simulation optimization using simulated annealing
Computers and Industrial Engineering
Stochastic optimization applied to a manufacturing system operation problem
WSC '95 Proceedings of the 27th conference on Winter simulation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
A sequential-design metamodeling strategy for simulation optimization
Computers and Operations Research
Simulation-based optimization: practical introduction to simulation optimization
Proceedings of the 35th conference on Winter simulation: driving innovation
ACM SIGSIM Simulation Digest
New advances and applications for marrying simulation and optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
Gradient-based simulation optimization
Proceedings of the 38th conference on Winter simulation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Distributed evolutionary algorithms for simulation optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The global optimization of complex systems such as industrial systems often necessitates the use of computer simulation. In this paper, we suggest the use of reinforcement learning (RL) algorithms and artificial neural networks for the optimization of simulation models. Several types of variables are taken into account in order to find global optimum values. After a first evaluation through mathematical functions with known optima, the benefits of our approach are illustrated through the example of an inventory control problem frequently found in manufacturing systems. Single-item and multi-item inventory cases are considered. The efficiency of the proposed procedure is compared against a commercial tool.