Title
A Strip Packing Solving Method Using An Incremental Move Based On Maximal Holes
Abstract
When handling 2D packing problems, numerous incomplete and complete algorithms maintain a so-called bottom-left (BL) property: no rectangle placed in a container can be moved more left or bottom. While it is easy to make a rectangle BL when it is added in a container, it is more expensive to maintain all the placed pieces BL when a rectangle is removed. This prevents researchers from designing incremental moves for metaheuristics or efficient complete optimization algorithms. This paper investigates the possibility of violating the BL property. Instead, we propose to maintain the set of maximal holes, which allows incremental additions and removals of rectangles.To validate our alternative approach, we have designed an incremental move,maintaining maximal holes, for the strip packing problem, a variant of the famous 2D bin-packing. We have also implemented a metaheuristic,with no user-defined parameter, using this move and standard greedy heuristics. We have finally designed two variants of this incomplete method. In the first variant,a better first layout is provided by a hyperheuristic proposed by some of the authors. In the second variant, a fast repacking procedure recovering the BL property is occasionally called during the local search.Experimental results show that the approach is competitive with the best known incomplete algorithms.
Year
DOI
Venue
2008
10.1142/S0218213008004205
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Keywords
Field
DocType
Local search, 2D packing, bin packing, strip packing
Mathematical optimization,Packing problems,Computer science,Rectangle,Algorithm,Heuristics,Optimization algorithm,Local search (optimization),Bin packing problem,Strip packing,Metaheuristic
Journal
Volume
Issue
ISSN
17
5
0218-2130
Citations 
PageRank 
References 
2
0.36
9
Authors
4
Name
Order
Citations
PageRank
Bertrand Neveu125323.18
Gilles Trombettoni225121.03
Ignacio Araya310412.29
María Cristina Riff420023.91