A multi-objective fuzzy genetic algorithm for job-shop scheduling problems

Y. J. Xing, Z. Q. Wang, J. Sun, J. J. Meng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

There are many uncertain factors in job shop scheduling problems. However, those uncertainties are critical for the scheduling procedures. The imprecise processing times are modeled as triangular fuzzy numbers (TFNs) and the due dates are modeled as trapezium fuzzy numbers in this paper. A multi-objective genetic algorithm is proposed to solve fuzzy job shop scheduling problems, in which the objective functions are conflicting. Agreement index (AI) is used to show the satisfaction of client which is defined as value of the area of processing time membership function intersection divided by the area of the due date membership function. The multi-objective function is composed of maximize both the minimum agreement and maximize the average agreement index. Two benchmark problems were used to show the effectiveness of the proposed approach. Experimental results demonstrate that the multiobjective genetic algorithm does not get stuck at a local optimum easily, and it can solve job-shop scheduling problems with fuzzy processing time and fuzzy due date effectively.

Original languageEnglish
Title of host publication2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
PublisherIEEE Computer Society
Pages398-401
Number of pages4
ISBN (Print)1424406056, 9781424406050
DOIs
Publication statusPublished - 2006 Jan 1
Externally publishedYes
Event2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 - Guangzhou, China
Duration: 2006 Oct 32006 Oct 6

Publication series

Name2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
Volume1

Conference

Conference2006 International Conference on Computational Intelligence and Security, ICCIAS 2006
CountryChina
CityGuangzhou
Period06/10/306/10/6

Keywords

  • Fuzzy numbers
  • Genetic algorithms
  • Job shop
  • Scheduling

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering

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  • Cite this

    Xing, Y. J., Wang, Z. Q., Sun, J., & Meng, J. J. (2006). A multi-objective fuzzy genetic algorithm for job-shop scheduling problems. In 2006 International Conference on Computational Intelligence and Security, ICCIAS 2006 (pp. 398-401). [4072115] (2006 International Conference on Computational Intelligence and Security, ICCIAS 2006; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICCIAS.2006.294162