An Integrated Model for Master Scheduling, Lot Sizing and Capacity Requirements Planning

Although material requirements planning (M.R.P.) allows managers to better plan hierarchical production and inventory systems, much is still left to the planner's intuition and experience in devising realistic master production schedules, selecting appropriate lot sizes for components, and chan...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:The Journal of the Operational Research Society Ročník 35; číslo 5; s. 389 - 399
Hlavní autoři: Bahl, Harish C., Ritzman, Larry P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 01.05.1984
Pergamon Press
Palgrave Macmillan UK
Taylor & Francis Ltd
Témata:
Algorithms
Business and Management
capacity requirements planning
Carrying costs
computer aided analysis
Heuristic
Heuristics
integrated model
Linear programming
Linear transformations
Management
master scheduling
Material requirements planning
materials planning and control
mathematical programming
modeling
Nonlinear programming
Operations research
Operations Research/Decision Theory
Optimal solutions
Overtime costs
Planning
Production capacity
Production planning
Production scheduling
Scheduling
Theoretical Paper
Theoretical Papers
Total costs
capacity requirements planning
master scheduling
integrated model
material requirements planning
mathematical programming
ISSN:0160-5682, 1476-9360
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Although material requirements planning (M.R.P.) allows managers to better plan hierarchical production and inventory systems, much is still left to the planner's intuition and experience in devising realistic master production schedules, selecting appropriate lot sizes for components, and changing capacity levels. Decisions are made sequentially, rather than simultaneously, with no real assurance of satisfactory performance. This research proposes an integrated model for facilitating these decisions. A mixed-integer nonlinear programming model is formulated such that it can be solved by a heuristic procedure. Several versions of this procedure are tested in a research design controlling for nine variables on shop and cost structure. Preliminary research results show that the procedure gives good solutions to the situations represented by the experimental variables. Computational results are also encouraging.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
content type line 23
ISSN:0160-5682
1476-9360
DOI:10.1057/jors.1984.79