Solving the Multi-Objective Flexible Job-Shop Scheduling Problem with Alternative Recipes for a Chemical Production Process

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

Abstract

This paper considers a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes. We propose a novel associated chromosome encoding and customise the classic MOEA/D multi-objective genetic algorithm with new genetic operators. The applicability of the proposed approach is evaluated experimentally and showed to outperform typical multi-objective genetic algorithms. The problem variant is motivated by real-world manufacturing in a chemical plant and is applicable to other plants that manufacture goods using alternative recipes.
Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings
EditorsPaul Kaufmann, Pedro A. Castillo
PublisherSpringer
Pages33-48
Number of pages16
ISBN (Print)9783030166915
DOIs
Publication statusPublished - 24 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11454 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Keywords

  • Multi-objective genetic algorithms
  • Multi-objective job-shop scheduling
  • Process manufacturing optimisation

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