Descent search approaches applied to the minimization of open stacks

•We consider the Minimization of Open Stacks Problem (MOSP).•We propose a new local search procedure for the MOSP.•We propose an implementation of the Nested Variable Neighborhood Descent metaheuristic.•We propose an implementation of a Nested Steepest Descent method.•Experiments compared the proposed methods to the state-of-the-art methods using five datasets.

In this paper, new algorithms are proposed for solving the minimization of open stacks, an industrial cutting pattern sequencing problem. In the considered context, the objective is to minimize the use of intermediate storage, as well as the unnecessary handling of manufactured products. We introduce a new local search method, specifically tailored for this NP-hard problem, which has wide practical applications. In order to further explore the solution space, we use this new local search as a component in two descent search methods associated with grouping strategies: variable neighborhood descent and steepest descent. Computational experiments were conducted involving 595 benchmark instances from five different sets through which the contributions of the proposed methods were compared with those of the state-of-the-art methods. The results demonstrate that the proposed algorithms are competitive and robust, as high quality solutions were consistently generated in a reasonable running time.