Genetic optimization of order scheduling with multiple uncertainties

Z. X. Guo, Wai Keung Wong, S. Y.S. Leung, J. T. Fan, S. F. Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

46 Citations (Scopus)

Abstract

In this paper, the order scheduling problem at the factory level, aiming at scheduling the production processes of each production order to different assembly lines is investigated. Various uncertainties, including uncertain processing time, uncertain orders and uncertain arrival times, are considered and described as random variables. A mathematical model for this order scheduling problem is presented with the objectives of maximizing the total satisfaction level of all orders and minimizing their total throughput time. Uncertain completion time and beginning time of production process are derived firstly by using probability theory. A genetic algorithm, in which the representation with variable length of sub-chromosome is presented, is developed to generate the optimal order scheduling solution. Experiments are conducted to validate the proposed algorithm by using real-world production data. The experimental results show the effectiveness of the proposed algorithm.
Original languageEnglish
Pages (from-to)1788-1801
Number of pages14
JournalExpert Systems with Applications
Volume35
Issue number4
DOIs
Publication statusPublished - 1 Nov 2008

Keywords

  • Genetic algorithms
  • Order scheduling
  • Probability theory
  • Uncertain processing time

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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