A genetic algorithm for a 2D industrial packing problem
Computers and Industrial Engineering
A New Exact Algorithm for General Orthogonal D-Dimensional Knapsack Problems
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
An Exact Approach to the Strip-Packing Problem
INFORMS Journal on Computing
A Combinatorial Characterization of Higher-Dimensional Orthogonal Packing
Mathematics of Operations Research
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
The Bottomn-Left Bin-Packing Heuristic: An Efficient Implementation
IEEE Transactions on Computers
A fast layer-based heuristic for non-guillotine strip packing
Expert Systems with Applications: An International Journal
Genetic algorithms applied to the design of 3D photonic crystals
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Biased random-key genetic algorithms for combinatorial optimization
Journal of Heuristics
Approximation algorithms for multiple strip packing
WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
INFORMS Journal on Computing
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In this paper we have studied the two-dimensional cutting stock problem, in which large number of small rectangles are to be placed in the big container such that the trim loss and height of the layout is minimized. We have proposed a placement approach along with a relevant fitness function to evaluate the overall goodness of the design layout. The computation results validate the solution and the effectiveness of the approach.