Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics
Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed publications over the last decade. We categorize an...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10847828/ |
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| author | Alonso Vela Gerardo Humberto Valencia-Rivera Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya |
| author_facet | Alonso Vela Gerardo Humberto Valencia-Rivera Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya |
| author_sort | Alonso Vela |
| collection | DOAJ |
| description | Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed publications over the last decade. We categorize and examine the prevailing strategies and configurations of hyper-heuristics, mainly focusing on their application across diverse scheduling scenarios such as job shop, flow shop, timetabling, and project scheduling. Our findings reveal a strong inclination towards selection and perturbative hyper-heuristics, with evolutionary computation emerging as the most commonly employed technique in this context, particularly in job shop and timetabling problems. Despite the robust development in hyper-heuristic methodologies, our analysis indicates an under-representation of multi-objective optimization and a limited use of performance metrics beyond makespan and tardiness. We also identify potential areas for future research, such as expanding hyper-heuristic applications to underexplored industries and exploring less conventional performance metrics. By providing a comprehensive overview of the current landscape and outlining future research directions, we aim to guide and inspire ongoing innovations in scheduling problem research. |
| format | Article |
| id | doaj-art-5c5d669ae9e34e259bb921f167949420 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5c5d669ae9e34e259bb921f1679494202025-01-28T00:01:42ZengIEEEIEEE Access2169-35362025-01-0113149831499710.1109/ACCESS.2025.353220110847828Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance MetricsAlonso Vela0https://orcid.org/0000-0002-9308-5538Gerardo Humberto Valencia-Rivera1https://orcid.org/0000-0002-5470-2441Jorge M. Cruz-Duarte2https://orcid.org/0000-0003-4494-7864Jose Carlos Ortiz-Bayliss3https://orcid.org/0000-0003-3408-2166Ivan Amaya4https://orcid.org/0000-0002-8821-7137School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoCNRS, Inria, CentraleLille, UMR 9189 CRIStAL, University of Lille, Lille, FranceSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoSchool of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, MexicoScheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed publications over the last decade. We categorize and examine the prevailing strategies and configurations of hyper-heuristics, mainly focusing on their application across diverse scheduling scenarios such as job shop, flow shop, timetabling, and project scheduling. Our findings reveal a strong inclination towards selection and perturbative hyper-heuristics, with evolutionary computation emerging as the most commonly employed technique in this context, particularly in job shop and timetabling problems. Despite the robust development in hyper-heuristic methodologies, our analysis indicates an under-representation of multi-objective optimization and a limited use of performance metrics beyond makespan and tardiness. We also identify potential areas for future research, such as expanding hyper-heuristic applications to underexplored industries and exploring less conventional performance metrics. By providing a comprehensive overview of the current landscape and outlining future research directions, we aim to guide and inspire ongoing innovations in scheduling problem research.https://ieeexplore.ieee.org/document/10847828/Combinatorial optimization problemshyper-heuristicsjob shop schedulingscheduling problems |
| spellingShingle | Alonso Vela Gerardo Humberto Valencia-Rivera Jorge M. Cruz-Duarte Jose Carlos Ortiz-Bayliss Ivan Amaya Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics IEEE Access Combinatorial optimization problems hyper-heuristics job shop scheduling scheduling problems |
| title | Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics |
| title_full | Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics |
| title_fullStr | Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics |
| title_full_unstemmed | Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics |
| title_short | Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics |
| title_sort | hyper heuristics and scheduling problems strategies application areas and performance metrics |
| topic | Combinatorial optimization problems hyper-heuristics job shop scheduling scheduling problems |
| url | https://ieeexplore.ieee.org/document/10847828/ |
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