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An Evolutionary Stochastic-Local-Search Framework For One-Dimensional Cutting-Stock Problems
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
doi:10.5281/zenodo.1186635
fatcat:q3r2xe7gxrhb7asgia2rnpjjpm