A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
A new evolutionary approach to cutting stock problems with and without contiguity
Computers and Operations Research
Genetic Algorithms for Cutting Stock Problems: With and Without Contiguity
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
A stabilized branch-and-price-and-cut algorithm for the multiple length cutting stock problem
Computers and Operations Research
Heuristics for the one-dimensional cutting stock problem with limited multiple stock lengths
Computers and Operations Research
A metaheuristic approach to the urban transit routing problem
Journal of Heuristics
Biased random-key genetic algorithms for combinatorial optimization
Journal of Heuristics
One-dimensional cutting stock problems and solution procedures
Mathematical and Computer Modelling: An International Journal
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This work presents a hybrid approach based on the use of genetic algorithms to solve efficiently the problem of cutting structural beams arising in a local metalwork company. The problem belongs to the class of one-dimensional multiple stock sizes cutting stock problem, namely 1-dimensional multiple stock sizes cutting stock problem. The proposed approach handles overproduction and underproduction of beams and embodies the reusability of remnants in the optimization process. Along with genetic algorithms, the approach incorporates other novel refinement algorithms that are based on different search and clustering strategies. Moreover, a new encoding with a variable number of genes is developed for cutting patterns in order to make possible the application of genetic operators. The approach is experimentally tested on a set of instances similar to those of the local metalwork company. In particular, comparative results show that the proposed approach substantially improves the performance of previous heuristics.