Developing a simulated annealing algorithm for the cutting stock problem
Computers and Industrial Engineering
Exact Solution of the Two-Dimensional Finite Bon Packing Problem
Management Science
Heuristic and Metaheuristic Approaches for a Class of Two-Dimensional Bin Packing Problems
INFORMS Journal on Computing
An Exact Approach to the Strip-Packing Problem
INFORMS Journal on Computing
A new heuristic recursive algorithm for the strip rectangular packing problem
Computers and Operations Research
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
A new constraint programming approach for the orthogonal packing problem
Computers and 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
Journal of Artificial Intelligence Research
An evolutionary squeaky wheel optimization approach to personnel scheduling
IEEE Transactions on Evolutionary Computation
Arc-flow model for the two-dimensional guillotine cutting stock problem
Computers and Operations Research
An efficient deterministic heuristic for two-dimensional rectangular packing
Computers and Operations Research
Evolutionary squeaky wheel optimization: A new framework for analysis
Evolutionary Computation
A simple randomized algorithm for two-dimensional strip packing
Computers and Operations Research
Heuristics for the strip packing problem with unloading constraints
Computers and Operations Research
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Expert Systems with Applications: An International Journal
An effective shaking procedure for 2D and 3D strip packing problems
Computers and Operations Research
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The two-dimensional strip packing problem occurs in industries such as metal, wood, glass, paper, and textiles. The problem involves cutting shapes from a larger stock sheet or roll of material, while minimising waste. This is a well studied problem for which many heuristic methodologies are available in the literature, ranging from the basic 'one-pass' best-fit heuristic, to the state of the art Reactive GRASP and SVC(SubKP) iterative procedures. The contribution of this paper is to present a much simpler but equally competitive iterative packing methodology based on squeaky wheel optimisation. After each complete packing (iteration), a penalty is applied to pieces that directly decreased the solution quality. These penalties inform the packing in the next iteration, so that the offending pieces are packed earlier. This methodology is deterministic and very easy to implement, and can obtain some best results on benchmark instances from the literature.