Exact Solution of the Two-Dimensional Finite Bon Packing Problem
Management Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
Exhaustive approaches to 2D rectangular perfect packings
Information Processing Letters
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
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 least wasted first heuristic algorithm for the rectangular packing problem
Computers and Operations Research
A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
INFORMS Journal on Computing
A squeaky wheel optimisation methodology for two-dimensional strip packing
Computers and Operations Research
Computers and Operations Research
A fast layer-based heuristic for non-guillotine strip packing
Expert Systems with Applications: An International Journal
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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Two-dimensional strip packing problem is to pack given rectangular pieces on a strip of stock sheet having fixed width and infinite height. Its aim is to minimize the height of the strip such that non-guillotinable and fix orientation constraints are meet. In this paper, an improved scoring rule is developed and the least waste priority strategy is introduced, and a randomized algorithm is presented for solving this problem. This algorithm is very simple and does not need to set any parameters. Computational results on a wide range of benchmark problem instances show that the proposed algorithm obtains a better or matching performance as compared to the most of the previously published meta-heuristics.