A genetic algorithm for a 2D industrial packing problem
Computers and Industrial Engineering
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
Reactive GRASP for the strip-packing problem
Computers and Operations Research
The Bottomn-Left Bin-Packing Heuristic: An Efficient Implementation
IEEE Transactions on Computers
A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
INFORMS Journal on Computing
Computers and Operations Research
A squeaky wheel optimisation methodology for two-dimensional strip packing
Computers and Operations Research
A hybrid genetic algorithm for packing in 3d with deepest bottom left with fill method
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
A simple randomized algorithm for two-dimensional strip packing
Computers and Operations Research
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Strip packing intends to minimise the height required for placing a set of rectangular items into a strip with infinite height. The present paper introduces a fast, yet effective shaking algorithm for the two- and three-dimensional strip packing problems, which are both NP-hard. The proposed heuristic procedure starts from an ordered item list, from which it alternates between forward and backward construction phases. The algorithm builds upon the common (deepest) bottom-left-fill algorithm, but shows significantly better results. Improvements on the solution quality of more than 9% can be observed. Moreover, applying the shaking procedure as a post processing algorithm to existing high performance heuristics also leads to improvements.