Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics
Abstract
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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BibTeX
@article{vela2026hyperheuristics,
title = {{Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics}},
author = {Alonso Vela and Gerardo Humberto Valencia-Rivera and Jorge Cruz-Duarte and José Carlos Ortiz-Bayliss and Ivan Amaya},
journal = {IEEE Access},
year = {2026},
month = sep,
doi = {10.1109/access.2025.3532201},
url = {https://hal.science/hal-05758671v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics
AU - Alonso Vela
AU - Gerardo Humberto Valencia-Rivera
AU - Jorge Cruz-Duarte
AU - José Carlos Ortiz-Bayliss
AU - Ivan Amaya
PY - 2026
DA - 2026/09/22
JO - IEEE Access
DO - 10.1109/access.2025.3532201
UR - https://hal.science/hal-05758671v1
AB - 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.
SN - 2169-3536
SN - 2169-3536
ER -