Aggregated state dynamic programming for a multiobjective two-dimensional bin packing problem

This paper studies a real-life multi-objective two-dimensional single-bin-size bin-packing problem arising in industry. A packing pattern is defined by one bin, a set of items packed into the bin and the packing positions of these items. A number of bins can be placed with the same packing pattern....

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of production research Ročník 50; číslo 15; s. 4316 - 4325
Hlavní autoři: Liu, Ya, Chu, Chengbin, Yu, Yugang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis Group 01.08.2012
Taylor & Francis
Taylor & Francis LLC
Témata:
Applied sciences
Bins
Computation
cutting stock problems
Decision theory. Utility theory
Decomposition
Dynamic programming
Exact sciences and technology
Flows in networks. Combinatorial problems
Heuristic
Mathematical programming
Operational research and scientific management
Operational research. Management science
Optimization
Optimization techniques
Packing problem
Stemming
Studies
Two dimensional
Hypergraph
OR
cutting stock problems
Cutting stock problem
Heuristic method
Multiobjective programming
Bin packing problem
Dynamic programming
Combinatorial optimization
ISSN:0020-7543, 1366-588X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper studies a real-life multi-objective two-dimensional single-bin-size bin-packing problem arising in industry. A packing pattern is defined by one bin, a set of items packed into the bin and the packing positions of these items. A number of bins can be placed with the same packing pattern. The objective is not only to minimise the number of bins used, as in traditional bin-packing problems, but also to minimise the number of packing patterns. Based on our previous study of a heuristic stemming from dynamic programming by aggregating states to avoid the exponential increase in the number of states, we further develop this heuristic by decomposing a pattern with a number of bins at each step. Computational results show that this heuristic provides satisfactory results with a gap generally less than 20% with respect to the optimum.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.622309