Metaheuristic scheduling of multiple picking agents for warehouse management

Purpose – This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time). Design/methodology/approach – The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic. Findings – The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the tabu scheduler produce solutions that are roughly of the same quality but exhibit considerable differences in computational time. Research limitations/implications – The proposed methodology is applicable only to the static scheduling problem where all inputs are known beforehand. Furthermore, of the possible delays during picking, only loading queues are explicitly addressed (although this is justifiable, given that these delays are dominant in the problem). Practical implications – The proposed approach can significantly increase through‐put and productivity in picking systems that utilize multiple intelligent agents (human pickers included), e.g. in warehouses/distribution centers. Originality/value – The paper addresses a practical scheduling problem with a high degree of complexity, i.e. scheduler explicitly deals with delays while trying to minimize makespan (generally, delays are ignored in the literature to simplify things). In the tabu implementation, an LS procedure is introduced in the metaheuristic loop that enhances the search process by minimizing non‐productive time of picking agents (travel time and delays).