A decimal artificial bee colony with elite strategy for the cutting stock problem with irregular items

This study investigates an irregular cutting stock problem in various industrial applications, including shipbuilding, construction machinery, and automobiles, where a considerable quantity of metal sheets are consumed. The problem involves cutting the single-size stocks to produce a set of demanded items such that the material utilization is maximized, i.e., the waste is minimized. To address the problem, this study employs the double scanline to represent the irregular items, and proposes a decimal artificial bee colony with elite strategy. The algorithm represents solutions with decimal vectors and uses a decoder procedure to map these vectors to solutions of the problem. In addition, a metaheuristic-based hybrid algorithm is developed for further improving the solution quality. To comprehensively assess the performance of the algorithm, two sets of computational tests were conducted. The experimental results demonstrated that the proposed algorithm outperforms competing algorithms by achieving faster convergence than other metaheuristics of the same class and producing better solutions, verifying the algorithm's effectiveness and superiority. The implementation of the algorithm benefits waste reduction for companies in practice.