Optimization of Threshold Control Parameters via Simulation-Based Methods
This chapter presents simulation-based methods to optimize threshold control parameters in stochastic supply chain systems. We first describe the key components of the discrete-event simulation, then provide a generic simulation model to evaluate the performance of a given threshold control policy in stochastic supply chain systems. Two simulation-based meta-heuristics, genetic algorithms and simulated annealing, are presented with the explanation of their key elements. These two meta-heuristics are then applied to the stochastic supply chain with assembly operations in Chap. 6 and optimize the threshold control policies in Chap. 11. To tackle the computational complexity of the search-based optimization methods in stochastic systems, the ordinal optimization technique is introduced. We present an ordinal optimization-based elite genetic algorithm to achieve the balance between the solution quality and the computational effort.
KeywordsSupply Chain Simulated Annealing Supply Chain System Offspring Population Sample Cost
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