Paper Title:
Simulation-Based Genetic Algorithm for Cross-Docking Center Operation Optimization under Supply Disruptions
  Abstract

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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