An Ant Colony Optimization Algorithm for the One-Dimensional Cutting Stock Problem with Multiple Stock Lengths

Abstract

The cutting stock problem (CSP) with multiple stock lengths is the NP-hard combinatorial optimization problem. In recent years, the CSP is researched by applying evolutionary approaches which includes genetic algorithm, evolutionary programming, et al. In the paper, an ant colony optimization (ACO) algorithm for one-dimensional cutting stock problems with multiple stock lengths (MCSP) is presented, and mutation operation is imported into the ACO in order to avoid the phenomenon of precocity and stagnation emerging. Based on the analysis of the algorithm, the ACO for MCSP has the same time complexity as CSP. Through experiments study, the outcome shows that, compared with other algorithm, the algorithm takes a great improvement in the convergent speed and result optimization.