The impact of integrated cluster-based storage allocation on parts-to-picker warehouse performance

Order picking is one of the most demanding activities in many warehouses terms capital and labor. In parts-to-picker systems, automated vehicles or cranes bring parts to a human picker. The storage assignment policy, products locations, influences order efficiency. Commonly used policies, such as full turnover-based class-based storage, only consider frequency at which each product has been requested but ignore information on are ordered jointly, known affinity. Warehouses can use affinity make informed decisions assign multiple correlated same inventory “pod” reduce retrieval time. Existing affinity-based assignments sequentially cluster with high clusters locations. We propose an integrated allocation (ICA) policy minimize time systems based both turnover obtained from historical customer orders. formulate mathematical model that solve small instances develop greedy construction heuristic for solving large instances. ICA total by up 40% compared policies. validated using real warehouse dataset tested against uncertainties demand different travel models.