Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
One-dimensional cutting stock problems and solution procedures
Mathematical and Computer Modelling: An International Journal
A two-objective mathematical model without cutting patterns for one-dimensional assortment problems
Journal of Computational and Applied Mathematics
Efficient algorithms for real-life instances of the variable size bin packing problem
Computers and Operations Research
Shadow price based genetic algorithms for the cutting stock problem
International Journal of Artificial Intelligence and Soft Computing
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This paper deals with the classical one-dimensional integer cutting stock problem, which consists of cutting a set of available stock lengths in order to produce smaller ordered items. This process is carried out in order to optimize a given objective function (e.g., minimizing waste). Our study deals with a case in which there are several stock lengths available in limited quantities. Moreover, we have focused on problems of low demand. Some heuristic methods are proposed in order to obtain an integer solution and compared with others. The heuristic methods are empirically analyzed by solving a set of randomly generated instances and a set of instances from the literature. Concerning the latter, most of the optimal solutions of these instances are known, therefore it was possible to compare the solutions. The proposed methods presented very small objective function value gaps.