{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Alonso Vela"
    },
    {
      "name": "Gerardo Humberto Valencia-Rivera"
    },
    {
      "name": "Jorge Cruz-Duarte"
    },
    {
      "name": "José Carlos Ortiz-Bayliss"
    },
    {
      "name": "Ivan Amaya"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2qkmz81dysgvg.bib",
  "cited_by": 9,
  "doi": "10.1109/access.2025.3532201",
  "fulltext_url": null,
  "id": "2qkmz81dysgvg",
  "issns": [
    "2169-3536",
    "2169-3536"
  ],
  "landing_url": "https://hal.science/hal-05758671v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
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    ],
    [
      "hal",
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    [
      "openalex",
      "2209|Scheduling and Optimization Algorithms"
    ]
  ],
  "openalex": "W4406610557",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": "IEEE",
  "ris": "https://research.draconitic.com/paper/2qkmz81dysgvg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "hal-05758671",
      "source": "hal",
      "url": "https://hal.science/hal-05758671v1"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "hal:1.math.math-oc",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics",
  "title_text": "Hyper-Heuristics and Scheduling Problems: Strategies, Application Areas, and Performance Metrics",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/2qkmz81dysgvg",
  "venue": "IEEE Access",
  "versions": []
}