The one-dimensional cutting stock problem is a linear optimization problem that is categorized as NP-Hard. This problem has a large number of applications in a number of different industries. Though a number of traditional methods have been applied to solve this problem, these methods are not as effective as advanced optimization techniques to find the global optimum of NP-Hard problems. In this paper, a combination of three such advanced methods has been used to solve the Cutting Stock Problem: the firefly algorithm (FFA), the bat algorithm (BA) and the teaching-learning based optimization (TLBO). The results of provided by these algorithms are compared on the basis of the optimality of the solution and for three individual case studies as well as by the convergence of the algorithms. It was found that the teaching-learning based optimization technique performed well in both the optimality as well as the convergence.
Effect of Applying Advanced Optimization Techniques for the One-Dimensional Cutting Stock Problem
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Savsani, V, Savsani, P, & Arya, P. "Effect of Applying Advanced Optimization Techniques for the One-Dimensional Cutting Stock Problem." Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition. Volume 11: Systems, Design, and Complexity. Montreal, Quebec, Canada. November 14–20, 2014. V011T14A020. ASME. https://doi.org/10.1115/IMECE2014-36095
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