Variable neighbourhood search for the variable sized bin packing problem

Authors:
Vera Hemmelmayr;Verena Schmid;Christian Blum
Affiliations:
Department of Business Administration, Universität Wien, Vienna, Austria;Department of Business Administration, Universität Wien, Vienna, Austria;ALBCOM Research Group, Universitat Politècnica de Catalunya, Barcelona, Spain
Venue:
Computers and Operations Research
Year:
2012

Citing 9
Cited 0

Variable sized bin packing

SIAM Journal on Computing
An efficient approximation scheme for variable-sized bin packing

SIAM Journal on Computing
Variable neighborhood search

Computers and Operations Research
Variable-Sized Bin Packing: Tight Absolute Worst-Case Performance Ratios for Four Approximation Algorithms

SIAM Journal on Computing
Solving the variable size bin packing problem with discretized formulations

Computers and Operations Research
Heuristics for the variable sized bin-packing problem

Computers and Operations Research
Hybrid algorithms for the variable sized bin packing problem

HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
Efficient lower bounds and heuristics for the variable cost and size bin packing problem

Computers and Operations Research
Relaxations and exact solution of the variable sized bin packing problem

Computational Optimization and Applications

Quantified Score

Hi-index	0.03

Visualization

Abstract

The variable sized bin packing problem is a generalisation of the one-dimensional bin packing problem. Given is a set of weighted items, which must be packed into a minimum-cost set of bins of variable sizes and costs. This problem has practical applications, for example, in packing, transportation planning, and cutting. In this work we propose a variable neighbourhood search metaheuristic for tackling the variable sized bin packing problem. The presented algorithm can be seen as a hybrid metaheuristic, because it makes use of lower bounding techniques and dynamic programming in various algorithmic components. An extensive experimentation on a diverse set of problem instances shows that the proposed algorithm is very competitive with current state-of-the-art approaches.