A genetic algorithm for solving classical job shop scheduling problems

Abdullatif Algabasi, Alireza Maheri

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

Abstract

This paper presents a new genetic algorithm (GA) for solving job shop scheduling (JSS) problem, in which a two-dimensional chromosome is used to represent individuals. Classical JSS problem is formulated in a more realistic and general way in which the expected arrival time for each job and machine availability for each machine are also taken into account. This formulation also allows solving special cases of returning tasks, in which a job can have more than one task allocated for a specific machine. Through a case study, the performance of the developed GA in solving generalised classic JSS problem is evaluated and presented.
Original languageEnglish
Title of host publicationInternational Conference on Advances in Mechanical Engineering (ICAME'15)
Pages1-6
Number of pages6
Publication statusPublished - 1 May 2015

Keywords

  • classical job shop scheduling
  • generalised job shop scheduling
  • JSS
  • JSSP
  • genetic algorithm

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  • Cite this

    Algabasi, A., & Maheri, A. (2015). A genetic algorithm for solving classical job shop scheduling problems. In International Conference on Advances in Mechanical Engineering (ICAME'15) (pp. 1-6) http://nrl.northumbria.ac.uk/22520/1/JSS_Final.pdf