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Solving the Multi-Objective Flexible Job-Shop Scheduling Problem with Alternative Recipes for a Chemical Production Process
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Author(s)
Department/unit(s)
Publication details
| Title of host publication | Applications of Evolutionary Computation - 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings |
|---|---|
| Date | Accepted/In press - 5 Jan 2019 |
| Date | E-pub ahead of print - 30 Mar 2019 |
| Date | Published (current) - 24 Apr 2019 |
| Pages | 33-48 |
| Number of pages | 16 |
| Editors | Paul Kaufmann, Pedro A. Castillo |
| Original language | English |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11454 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
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.
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.
- Multi-objective genetic algorithms, Multi-objective job-shop scheduling, Process manufacturing optimisation
Research areas
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