Artikel

Hybrid multimode resource-constrained maintenance project scheduling problem

Organizing maintenance tasks is a specific and important field of project and production management. Well-planned, properly scheduled, and effectively communicated maintenance tasks result in the accomplishment of more work that is performed more efficiently at a lower cost. In maintenance and production management, agile and lean approaches are becoming more frequently used, where preventive/predictive maintenance (PPM) requires flexible project plans to decide which system components (or equipment) should be maintained to achieve the target system reliability. Maintenance projects have been treated as fixed sequences of corrective/preventive tasks, while the agile/lean approach allows for and usually requires prioritizing tasks and reorganizing the project structure. The fixed structure of maintenance projects and traditional maintenance project scheduling algorithms are not considered representative of these agile properties. The paper models the preventive resource-constrained maintenance project scheduling problem and aims at optimizing its maintainability. A matrix-based algorithm is proposed to apply to both system and project structures that include both traditional and agile/lean project management approaches.

Sprache
Englisch

Erschienen in
Journal: Operations Research Perspectives ; ISSN: 2214-7160 ; Volume: 6 ; Year: 2019 ; Pages: 1-13 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Agile/lean approach
Flexibility
Maintenance management
Project management
Scheduling

Ereignis
Geistige Schöpfung
(wer)
Kosztyán, Zsolt T.
Pribojszki-Németh, Anikó
Szalkai, István
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2019

DOI
doi:10.1016/j.orp.2019.100129
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Kosztyán, Zsolt T.
  • Pribojszki-Németh, Anikó
  • Szalkai, István
  • Elsevier

Entstanden

  • 2019

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