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

被引：5

作者：

Dziurzanski, Piotr ^{[1
]}

Zhao, Shuai ^{[1
]}

Swan, Jerry ^{[1
]}

Indrusiak, Leandro Soares ^{[1
]}

Scholze, Sebastian ^{[2
]}

Krone, Karl ^{[3
]}

机构：

[1] Univ York, Dept Comp Sci, Deramore Lane, York YO10 5GH, N Yorkshire, England

[2] Inst Angew Systemtech Bremen GmbH, Wiener Str 1, D-28359 Bremen, Germany

[3] OAS AG, Caroline Herschel Str 1, D-28359 Bremen, Germany

来源：

APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2019 | 2019年 / 11454卷

基金：

欧盟地平线“2020”;

关键词：

Multi-objective job-shop scheduling; Process manufacturing optimisation; Multi-objective genetic algorithms; EVOLUTIONARY ALGORITHM; GENETIC ALGORITHM;

D O I：

10.1007/978-3-030-16692-2_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：33 / 48

页数：16

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