Paper Title:
Study on Scheduling Optimization for Flexible Job Shop
  Abstract

A hybrid genetic algorithm based on Pareto was proposed and applied to flexible job shop scheduling problem (FJSP) with multi-objective, and the multi-objective FJSP optimization model was built, where the make-span and the machine utilization rate were concerned. The algorithm embeds Pareto ranking strategy into Pareto competition method. The operation-based encoding and an active scheduling decoding method are employed. In order to promote solution diversity, the niche technology and many kinds of crossover operations are used. Pareto filter saves the optimum individual occurring in the course of evolution, which avoids losing the optimum solutions. Three simulation experiments are carried out to illustrate that the proposed method could solve multi-objective job shop scheduling problem effectively.

  Info
Periodical
Edited by
Zhenyu Du and Bin Liu
Pages
821-825
DOI
10.4028/www.scientific.net/AMM.26-28.821
Citation
X. X. Liu, C. B. Liu, Z. Tao, "Study on Scheduling Optimization for Flexible Job Shop", Applied Mechanics and Materials, Vols. 26-28, pp. 821-825, 2010
Online since
June 2010
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Price
$32.00
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