Metaheuristics for the 3D bin packing problem in the steel industry

This work presents heuristic and metaheuristic approaches for addressing the real-world steel cutting problem of a retail steel distributor as a cutting & packing problem. It consists of the cutting of large steel blocks in order to obtain smaller pieces ordered by clients. The problem was formulated as a 3-dimensional residual bin packing problem for minimization of scrap generation, with guillotine cutting constraint and chips scrap generation. A tabu search and bestfit decreasing (BFD) approaches are proposed and their performance compared to an heuristic and ant colony optimization (ACO) algorithms. It's shown that the tabu search and best-fit decreasing algorithm are able to reduce the generated scrap by up to 52% in comparison with the heuristic in [1]. The orders to suppliers were also reduced by up to 35%. The analysis of the results of the different approaches provide insight onto the most important factors in the problem's scrap minimization.