A new cutting-stock heuristic for scheduling production
Computers and Operations Research
Nonorthogonal two-dimensional cutting patterns
Management Science
The discrete two-dimensional assortment problem
Operations Research
A practical solution to a fuzzy two-dimensional cutting stock problem
Fuzzy Sets and Systems
A computational improvement to Wang's two-dimensional cutting stock algorithm
Computers and Industrial Engineering
Genetic algorithm approach to a lumber cutting optimization problem
Cybernetics and Systems
Best-first search methods for constrained two-dimensional cutting stock problems
Operations Research
Interactive procedures in large-scale two-dimensional cutting stock problems
Proceedings of the 6th international congress on Computational and applied mathematics
Developing a simulated annealing algorithm for the cutting stock problem
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Operations Research
Solution for the constrained guillotine cutting problem by simulated annealing
Computers and Operations Research
Branch-and-Price Algorithms for the One-Dimensional Cutting Stock Problem
Computational Optimization and Applications
The cutting stock problem in a hardboard industry: a case study
Computers and Operations Research
A recursive computational procedure for container loading
Proceedings of the 23rd international conference on on Computers and industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Dynamic Stochastic Stock-Cutting Problem
Operations Research
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In most studies on cutting stock problems, it is assumed that the sizes of stock materials are known and the problem is to find the best cutting pattern combinations. However, the solution efficiency of the problem depends strongly on the size of stock materials. In this study, a two-step approach is developed for a 1,5 dimensional assortment problem with multiple objectives. Cutting patterns are derived by implicit enumeration in the first step. The second step is used to determine the optimum sizes of stock materials by applying a genetic algorithm. The object is to find stock material sizes that minimize the total trim loss and also the variety of stock materials. Specialized crossover operator is developed to maintain the feasibility of the chromosomes. A real-life problem with 41 alternative stock materials, 289 order pieces and 1001 patterns is solved.