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.

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained contradictory results that suggest that, at least in this work, combining the strengths of different hyper-heuristic models may not be a suitable approach.

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained unexpected results.