Operations research: deterministic optimization models
Operations research: deterministic optimization models
A Near-Optimal Solution to a Two-Dimensional Cutting Stock Problem
Mathematics of Operations Research
A Review of the Application ofMeta-Heuristic Algorithms to 2D Strip Packing Problems
Artificial Intelligence Review
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Evaluation of algorithms for one-dimensional cutting
Computers and Operations Research
Heuristic and Metaheuristic Approaches for a Class of Two-Dimensional Bin Packing Problems
INFORMS Journal on Computing
Recent advances on two-dimensional bin packing problems
Discrete Applied Mathematics
An Exact Approach to the Strip-Packing Problem
INFORMS Journal on Computing
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
Online strip packing with modifiable boxes
Operations Research Letters
Risk aware overbooking for commercial grids
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
Hyperheuristic encoding scheme for multi-objective guillotine cutting problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A multi-objective approach for the 2D guillotine cutting stock problem
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
New fast heuristics for the 2d strip packing problem with guillotine constraint
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
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An overview and comparison is provided of a number of heuristics from the literature for the two-dimensional strip packing problem in which rectangles have to be packed without rotation. Heuristics producing only guillotine packings are considered. A new heuristic is also introduced and a number of modifications are suggested to the existing heuristics. The resulting heuristics (known and new) are then compared statistically with respect to a large set of known benchmarks at a 5% level of significance.