Cutting Tools Assignment and Control Using Neutrosophic Case-Based Reasoning and Best Worst Method

Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study propose...

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Published in:Advances in Operations Research Vol. 2022; pp. 1 - 11
Main Authors: Kasie, Fentahun Moges, Bright, Glen
Format: Journal Article
Language:English
Published: New York Hindawi 29.10.2022
John Wiley & Sons, Inc
Subjects:
Artificial intelligence
Cost control
Cutting tools
Decision making
Decision support systems
Genetic algorithms
Heuristic
Knowledge
Metal cutting
Methods
Multiple criterion
Operations research
Optimization
Optimization techniques
Process planning
Reasoning
Simulation
Simulation methods
ISSN:1687-9147, 1687-9155
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Abstract Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.
AbstractList Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.
Audience Academic
Author Kasie, Fentahun Moges
Bright, Glen
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Copyright Copyright © 2022 Fentahun Moges Kasie and Glen Bright.
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SubjectTerms Artificial intelligence
Cost control
Cutting tools
Decision making
Decision support systems
Genetic algorithms
Heuristic
Knowledge
Metal cutting
Methods
Multiple criterion
Operations research
Optimization
Optimization techniques
Process planning
Reasoning
Simulation
Simulation methods
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