Artikel

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

Sprache
Englisch

Erschienen in
Journal: Journal of Industrial Engineering International ; ISSN: 2251-712X ; Volume: 8 ; Year: 2012 ; Pages: 1-10 ; Heidelberg: Springer

Klassifikation
Management
Thema
flexible manufacturing system
production planning
loading
hybrid algorithm optimization

Ereignis
Geistige Schöpfung
(wer)
Kumar, Vijay M.
Murthy, ANN
Chandrashekara, K.
Ereignis
Veröffentlichung
(wer)
Springer
(wo)
Heidelberg
(wann)
2012

DOI
doi:10.1186/2251-712X-8-3
Handle
Letzte Aktualisierung
10.03.2025, 11:46 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

  • Kumar, Vijay M.
  • Murthy, ANN
  • Chandrashekara, K.
  • Springer

Entstanden

  • 2012

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