Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems
Abstract Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algo...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Collaborative Intelligent Manufacturing |
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| Online Access: | https://doi.org/10.1049/cim2.12120 |
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| author | Jiaxin Fan |
| author_facet | Jiaxin Fan |
| author_sort | Jiaxin Fan |
| collection | DOAJ |
| description | Abstract Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi‐objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ‐constraint, which transforms multi‐objective problems into single‐objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ‐constraint‐based matheuristic methodology (ɛ‐MH) is proposed to apply the idea of ɛ‐constraint to embedded MILP models, so that Pareto fronts obtained by meta‐heuristics can be further improved by solving a set of MILP models. Afterwards, four speed‐up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ‐MH. Finally, several real‐world bi‐objective scheduling problems are discussed to present potential applications for the proposed methodology. |
| format | Article |
| id | doaj-art-874c6d39eb914bb18d2d21522dc7b51b |
| institution | Kabale University |
| issn | 2516-8398 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Collaborative Intelligent Manufacturing |
| spelling | doaj-art-874c6d39eb914bb18d2d21522dc7b51b2024-12-28T04:20:30ZengWileyIET Collaborative Intelligent Manufacturing2516-83982024-12-0164n/an/a10.1049/cim2.12120Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problemsJiaxin Fan0School of Mechanical Engineering and Automation Fuzhou Fuyao Institute for Advanced Study Fuzhou ChinaAbstract Matheuristic is an optimisation methodology that integrates mathematical approaches and heuristics to address intractable combinatorial optimisation problems, where a common framework is to insert mixed integer linear programming (MILP) models as local search functions for evolutionary algorithms. However, since a mathematical programming formulation only tries to find the solution with the best objective value, matheuristics are rarely adopted to multi‐objective scenarios asking for a set of Pareto optimal solutions, for example, vehicle routing problems and production scheduling problems. In this situation, the ɛ‐constraint, which transforms multi‐objective problems into single‐objective formulations by considering selected objectives as constraints, seems to be a promising approach. First, an augmented ɛ‐constraint‐based matheuristic methodology (ɛ‐MH) is proposed to apply the idea of ɛ‐constraint to embedded MILP models, so that Pareto fronts obtained by meta‐heuristics can be further improved by solving a set of MILP models. Afterwards, four speed‐up strategies are developed to alleviate the computational burden resulting from repeatedly solving mathematical formulations, which also imply preferable scenarios for taking advantages of the ɛ‐MH. Finally, several real‐world bi‐objective scheduling problems are discussed to present potential applications for the proposed methodology.https://doi.org/10.1049/cim2.12120flow shop schedulinggenetic algorithmsjob shop schedulingscheduling |
| spellingShingle | Jiaxin Fan Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems IET Collaborative Intelligent Manufacturing flow shop scheduling genetic algorithms job shop scheduling scheduling |
| title | Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems |
| title_full | Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems |
| title_fullStr | Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems |
| title_full_unstemmed | Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems |
| title_short | Augmented ɛ‐constraint‐based matheuristic methodology for Bi‐objective production scheduling problems |
| title_sort | augmented e constraint based matheuristic methodology for bi objective production scheduling problems |
| topic | flow shop scheduling genetic algorithms job shop scheduling scheduling |
| url | https://doi.org/10.1049/cim2.12120 |
| work_keys_str_mv | AT jiaxinfan augmentedɛconstraintbasedmatheuristicmethodologyforbiobjectiveproductionschedulingproblems |