Storage Assignment Using Nested Annealing and Hamming Distances

Abstract

The assignment of products to storage locations significantly impacts the efficiency of warehouse operations. We propose a multi-phase optimizer for a Storage Location Assignment Problem (SLAP) where solution quality is based on a distance estimate of future-forecasted order picking. Candidate assignments are first sampled using a Markov Chain accept/reject method. Future-forecasted pick-rounds are then modified according to the candidate assignments and solved as Traveling Salesman Problems (TSP). The model is graph-based and generalizes to any obstacle layout in 2D. Due to the intractability of the SLAP, methods are proposed to speed up search for strong solution candidates. These include usage of fast function approximation to find potentially strong samples, as well as restarts from local minima. Results show that these methods improve performance and that total travel distance can be reduced by as much as 30% within 8 hours of CPU-time. We share a public repository with SLAP instances and corresponding benchmark results on the generalizable TSPLIB format.