Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 59, Issue 12, Pages 3572-3590Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.1897174
Keywords
Tool indexing; genetic algorithm; non-machining time; multi-objective optimisation; SPEA2; mathematical model
Categories
Funding
- Knowledge Foundation (KKS), Sweden, through the Profile project, Virtual Factories-Knowledge-Driven Optimisation (VF-KDO)
- Knowledge Foundation (KKS), Sweden, through the Prospket project, Predictive Modelling for Data Intensive industrial Processes and Systems (PreMoDIPS) (Stiftelsen for Kunskaps-och Kompetensutveckling) [HSK2019/20]
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This study addresses the multi-objective optimisation tool-indexing problem by considering changes that must be made to current industrial settings as an additional objective, in addition to minimising tool-indexing time. A novel mathematical model and approach are proposed, and a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism is suggested to achieve a uniform distribution of solutions. Results show a significant reduction in non-machining time and tradeoff solutions for efficient tool management.
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.
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