Optimization of Threshold Control Parameters via Simulation-Based Methods

  • Dong-Ping SongEmail author
Part of the Advances in Industrial Control book series (AIC)


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.


Supply Chain Simulated Annealing Supply Chain System Offspring Population Sample Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of ManagementUniversity of PlymouthPlymouthUK

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