Optimal Integer Solutions to Industrial Cutting-Stock Problems: Part 2, Benchmark Results
INFORMS Journal on Computing
Improving Cutting-Stock Plans with Multi-objective Genetic Algorithms
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Strips minimization in two-dimensional cutting stock of circular items
Computers and Operations Research
An Inexact Bundle Approach to Cutting-Stock Problems
INFORMS Journal on Computing
An Exact Algorithm for the Two-Dimensional Strip-Packing Problem
Operations Research
On the one-dimensional stock cutting problem in the paper tube industry
Journal of Scheduling
A recursive branch-and-bound algorithm for constrained homogenous T-shape cutting patterns
Mathematical and Computer Modelling: An International Journal
Cutting stock with no three parts per pattern: Work-in-process and pattern minimization
Discrete Optimization
Solution approaches for the cutting stock problem with setup cost
Computers and Operations Research
Heuristic for the rectangular strip packing problem with rotation of items
Computers and Operations Research
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The primary objective in cutting and packing problems is trim loss or material input minimization (in stock cutting) or value maximization (in knapsack-type problems). However, in real-life production we usually have many other objectives (costs) and constraints. Probably the most complex auxiliary criteria in one-dimensional stock cutting are the number of different cutting patterns (setups) and the maximum number of open stacks during the cutting process. There are applications where the number of stacks is restricted to two. We design a sequential heuristic to minimize material input and show its high effectiveness for this purpose. Then we extend it to restrict the number of open stacks to any given limit. Then, the heuristic is simplified and integrated into a setup-minimization approach in order to combine setup and open-stacks minimization. To get a smaller number of open stacks, we may split up the problem into several parts of smaller sizes. Different solutions are evaluated in relation to the multiple objectives using the Pareto criterion.