An Evolutionary Stochastic-Local-Search Framework For One-Dimensional Cutting-Stock Problems

Georgios Chasparis, Michael Rossbory, Verena Haunschmid, Christian Lettner
2018 Zenodo  
We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem. Cutting-stock problems are encountered often in industrial environments and the ability to address them efficiently usually results in large economic benefits. Traditionally linear-programming-based techniques have been utilized to address such problems, however their flexibility might be limited when nonlinear constraints and objective
more » ... ns are introduced. To this end, this paper proposes an evolutionary SLS algorithm for addressing one-dimensional cutting-stock problems. The contribution lies in the introduction of a flexible structural framework of the optimization that may accommodate a large family of diversification strategies including a novel parallel pattern appropriate for SLS algorithms (not necessarily restricted to cutting-stock problems). We finally demonstrate through experiments in a real-world manufacturing problem the benefit in cost reduction of the considered diversification strategies.
doi:10.5281/zenodo.1186635 fatcat:q3r2xe7gxrhb7asgia2rnpjjpm