Reducing conveyor blockages at an automated food logistics warehouse via bounded metaheuristic exploration

Abstract This paper presents a practical case study, conducted with Corporación Alimentaria Guissona S.A. (bonÀrea), on optimizing storage locations in a large-scale, automated warehouse for perishable foods. The objective is to minimize costly blockages on the facility’s conveyors, which disrupt throughput and risk the cold chain. We address this via item redistribution. This is a hard combinatorial optimization problem with a large search space. We model the problem using a derivative-free simulator and explore solutions using local search metaheuristic techniques. Unlike conventional optimization that seeks global optima in an unrestricted search space, our approach is limited by a defined local distance from the original solution. We show different approaches to tackle this set of problem constraints based around local search metaheuristics that ensure bounded exploration of the solution space. Our results demonstrate that a bounded exploration approach can reduce blockages by 45%, providing a practical, low-disruption solution that significantly improves operational efficiency in a real-world, high-volume logistics environment.