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....

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Bibliographic Details
Published in:International journal of production research Vol. 50; no. 15; pp. 4316 - 4325
Main Authors: Liu, Ya, Chu, Chengbin, Yu, Yugang
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis Group 01.08.2012
Taylor & Francis
Taylor & Francis LLC
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2011.622309