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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10847828/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |