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Multi-objective job shop scheduling using i-NSGA-III

Version 2 2024-05-30, 15:14
Version 1 2023-02-20, 03:45
conference contribution
posted on 2024-05-30, 15:14 authored by Burhan KhanBurhan Khan, Samer HanounSamer Hanoun, Michael JohnstoneMichael Johnstone, Chee Peng Lim, Douglas CreightonDouglas Creighton, S Nahavandi
The complexity of job shop scheduling problems is related to many factors, such as a large number of jobs, the number of objectives and constraints. Evolutionary algorithms are a natural fit to search for the optimum schedules in complex job shop scheduling problems with multiple objectives. This paper extends the authors' i-NSGA-In algorithm to tackle a manufacturing job shop scheduling problem with multiple objectives. One of the complex objectives is to pair jobs with similar properties to increase the overall cost savings. The genetic operators in i-NSGA-III are replaced with novel problem-specific crossover and mutation operators. The proposed approach is validated by comparing against the enumeration technique for problems with 5 to 10 jobs. Unlike the enumeration technique, the proposed methodology shows competence in terms of computation time and ability to schedule a large number of jobs with a high number of objectives. Further comparisons with NSGA-III demonstrate the superiority of i-NSGA-III for problems with 30, 40, and 50 jobs.

History

Pagination

1-5

Location

Vancouver, Canada

Start date

2018-04-24

End date

2018-04-26

ISSN

1944-7620

ISBN-13

9781538636640

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

SysCon 2018 : Proceedings of the 12th Annual IEEE International Systems Conference

Event

IEEE Systems Council. Conference (12th : 2018 : Vancouver, Canada)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Systems Council Conference