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Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University. (Produktion och Automatiseringsteknik (PAT), Production and Automation Engineering)ORCID iD: 0000-0001-7534-0382
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och Automatiseringsteknik (PAT), Production and Automation Engineering)ORCID iD: 0000-0001-6280-1848
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University. (Produktion och Automatiseringsteknik (PAT), Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
2021 (English)In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 12, p. 3572-3590Article in journal (Refereed) Published
Abstract [en]

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices – the “tool-indexing problem”. Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation tool indexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices. 

Place, publisher, year, edition, pages
Taylor & Francis Group, 2021. Vol. 59, no 12, p. 3572-3590
Keywords [en]
Tool indexing, genetic algorithm, non-machining time, multi-objective optimisation, SPEA2, mathematical model
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-19535DOI: 10.1080/00207543.2021.1897174ISI: 000628710300001Scopus ID: 2-s2.0-85102698141OAI: oai:DiVA.org:his-19535DiVA, id: diva2:1537187
Projects
VF-KDO
Funder
Knowledge Foundation, HSK2019/20
Note

CC BY-NC-ND 4.0

Published online: 13 Mar 2021

Available from: 2021-03-15 Created: 2021-03-15 Last updated: 2023-02-22Bibliographically approved

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Amouzgar, KavehNourmohammadi, AmirNg, Amos H. C.

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