Mathematical programming algorithms for bin packing problems with item fragmentation

In this paper we consider a class of bin packing problems from the literature having the following distinctive feature: items may be fragmented at a price. Problems of this kind arise in diverse application fields like logistics and telecommunications, and have already been extensively tackled from...

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Vydané v:Computers & operations research Ročník 46; s. 1 - 11
Hlavní autori: Casazza, Marco, Ceselli, Alberto
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Ltd 01.06.2014
Elsevier
Pergamon Press Inc
Predmet:
Algorithms
Applied sciences
Approximation
Bin packing
Branch-and-price
Exact sciences and technology
Flows in networks. Combinatorial problems
Fragmentation
Heuristic
Item fragmentation
Logistics
Mathematical analysis
Mathematical models
Mathematical programming
Operational research and scientific management
Operational research. Management science
Optimization
Optimization algorithms
Packing problem
Studies
Bin packing
Branch-and-price
Item fragmentation
Mathematical programming
Branch and bound method
Bin packing problem
Telecommunication
Combinatorial optimization
Modeling
Fragmentation
Column generation
Heuristic method
Logistics
ISSN:0305-0548, 1873-765X, 0305-0548
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Popis
Shrnutí:In this paper we consider a class of bin packing problems from the literature having the following distinctive feature: items may be fragmented at a price. Problems of this kind arise in diverse application fields like logistics and telecommunications, and have already been extensively tackled from an approximation point of view. We focus on the case in which splitting produces no overhead, a fixed number of bins is given and the number of fragments or fragmentations needs to be minimized. We first investigate the theoretical properties of the problem. Then we elaborate on them to devise mathematical programming models and algorithms, yielding both exact optimization algorithms and effective heuristics. An extensive experimental campaign proves that our approach is very effective, and highlights which features make an instance computationally harder to solve.
Bibliografia:SourceType-Scholarly Journals-1
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2013.12.008