Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions

Abstract:

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Practical performance optimization of a cross-docking center has been rare in the literature so far. The measures representing operation efficiency are average inventory level and transportation cost rate, while average backorder level represents the customer service level. In this paper, a simulation optimization problem is considered and a solution framework has been developed by integrating simulation, genetic algorithm (GA) and smart computing budget allocation (SCBA) to find an optimized solution. Moreover, supply disruptions are considered in the simulation model. This problem has huge search space even for medium-sized problem scenarios. To address this difficulty, the framework employs simulation to estimate the performance measures, GA to search for better design and SCBA to efficiently allocate the simulation budget.

Info:

Periodical:

Advanced Materials Research (Volumes 201-203)

Edited by:

Daoguo Yang, Tianlong Gu, Huaiying Zhou, Jianmin Zeng and Zhengyi Jiang

Pages:

1103-1106

DOI:

10.4028/www.scientific.net/AMR.201-203.1103

Citation:

Y. F. Wu "Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions", Advanced Materials Research, Vols. 201-203, pp. 1103-1106, 2011

Online since:

February 2011

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$35.00

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