{
  "abstract": "Assigning nursing students to clinical training wards while satisfying operational, educational, and institutional constraints is a difficult multi-objective combinatorial optimization problem known as the student nurse allocation problem (SNAP). Based on real-world scheduling requirements gathered from expert interviews and university practice, this study develops a mixed-integer programming model for SNAP. The optimization criteria are minimizing the total completion time across wards and minimizing ward switching across courses. Two solution approaches are proposed: the augmented epsilon-constraint (AUGMECON) method for obtaining exact non-dominated solutions in small-size instances and a constructive-heuristic-guided NSGA-II algorithm (CH-NSGA-II) for larger instances. Computational experiments were conducted on generated instances with different sizes and on a real-world case study. In small-size instances, the AUGMECON and CH-NSGA-II were evaluated using computational time and distance-based indicator. For larger generated instances, CH-NSGA-II was compared with a randomized constructive heuristic (RCH) baseline using the normalized hypervolume indicator. The results show that CH-NSGA-II generated non-dominated solutions in shorter computational times than AUGMECON for the tested small-size instances. In the larger instances, CH-NSGA-II achieved higher normalized hypervolume values than RCH across all tested problem groups, indicating improved Pareto-front quality compared with the randomized constructive baseline. In the real-world case, the best reported solution reduced the total completion time across wards by 30.9% compared with the available manual schedule, while satisfying the considered hard constraints. The results suggest that CH-NSGA-II can support clinical placement scheduling in nursing education by providing alternative trade-off solutions for decision makers.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Industrial Engineering and Futures Studies, Faculty of Engineering, University of Isfahan",
    "Department of Nursing, Amol Faculty of Nursing and Midwifery, Mazandaran University of Medical Sciences"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hanieh Shabani Afarani"
    },
    {
      "affs": [
        0
      ],
      "name": "Arezoo Atighehchian"
    },
    {
      "affs": [
        1
      ],
      "name": "Roghieh Nazari"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1c6jmqppxnm19.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-62702-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-62702-0",
  "id": "1c6jmqppxnm19",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-62702-0",
  "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": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1803|Scheduling and Timetabling Solutions"
    ]
  ],
  "openalex": "W7171329481",
  "pdf_url": "https://www.nature.com/articles/s41598-026-62702-0_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/1c6jmqppxnm19.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:53Z",
      "native_id": "59edb646114646628389af080a6e980a",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-62702-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Multi-objective student nurse allocation problem during training using exact and hybrid heuristic–metaheuristic methods",
  "title_text": "Multi-objective student nurse allocation problem during training using exact and hybrid heuristic–metaheuristic methods",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:53Z",
  "url": "https://research.draconitic.com/paper/1c6jmqppxnm19",
  "venue": "Scientific Reports",
  "versions": []
}