Artikel

Improved layout structure with complexity measures for the Manufacturer's pallet loading problem (MPLP) using a Block approach

Purpose: The purpose of this paper is to study the Manufacturers pallet-loading problem (MPLP), by loading identical small boxes onto a rectangle pallet to maximise the pallet utilization percentage while reducing the Complexity of loading. Design/methodology/approach: In this research a Block approach is proposed using a Mixed integer linear programming (MILP) model that generates layouts of an improved structure, which is very effective due to its properties in grouping boxes in a certain orientation along the X and Y axis. Also, a novel complexity index is introduced to compare the complexity for different pallet loading, which have the same pallet size but different box arrangements. Findings: The proposed algorithm has been tested against available data-sets in literature and the complexity measure and graphical layout results clearly demonstrate the superiority of the proposed approach compared with literature Manufacturers pallet-loading problem layouts. Originality/value: This study aids real life manufactures operations when less complex operations are essential to reduce the complexity of pallet loading.

Sprache
Englisch

Erschienen in
Journal: Journal of Industrial Engineering and Management (JIEM) ; ISSN: 2013-0953 ; Volume: 14 ; Year: 2021 ; Issue: 2 ; Pages: 231-249 ; Barcelona: OmniaScience

Klassifikation
Management
Thema
manufactures pallet loading problems
cutting and packing problems
mathematical programming
mixed integer optimization

Ereignis
Geistige Schöpfung
(wer)
Aljuhani, Deemah
Papageorgiou, Lazaros
Ereignis
Veröffentlichung
(wer)
OmniaScience
(wo)
Barcelona
(wann)
2021

DOI
doi:10.3926/jiem.3264
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

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

Objekttyp

  • Artikel

Beteiligte

  • Aljuhani, Deemah
  • Papageorgiou, Lazaros
  • OmniaScience

Entstanden

  • 2021

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