Abstract:
This study considers a dynamic customer order scheduling problem in a stochastic setting. Customer orders arrive at the service station dynamically and each consists of m...Show MoreMetadata
Abstract:
This study considers a dynamic customer order scheduling problem in a stochastic setting. Customer orders arrive at the service station dynamically and each consists of multiple product types with random workloads. Each order will be processed by a set of non-identical parallel servers. The objective is to determine the optimal workload assignment policy that minimizes the long-run expected order cycle time. A simulation-based genetic algorithm, named SimGA, is proposed to solve the problem, and a computable lower bound is developed for performance evaluation. Numerical experiments are reported to evaluate the performance of SimGA against two well-known simulation optimization methods.
Published in: 2015 Winter Simulation Conference (WSC)
Date of Conference: 06-09 December 2015
Date Added to IEEE Xplore: 18 February 2016
ISBN Information:
Electronic ISSN: 1558-4305