By the same authors

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

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Publication details

Title of host publicationApplications of Evolutionary Computation - 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings
DateAccepted/In press - 5 Jan 2019
DateE-pub ahead of print - 30 Mar 2019
DatePublished (current) - 24 Apr 2019
Number of pages16
EditorsPaul Kaufmann, Pedro A. Castillo
Original languageEnglish

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


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.

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    Research areas

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

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