A new iterative method for solving the joint dynamic storage location assignment, order batching and picker routing problem in manual picker-to-parts warehouses

•Jointly solves storage location assignment (SA), order batching (OB) and picker routing (PR).•Uses particle swarm optimization and evolutionary computation to solve OB and PR.•Develops a dynamic storage location assignment algorithm to find promising relocations.•Proposes an estimation procedure and OB/PR method to calculate future travel distance reduction. Short and reliable delivery lead times are crucial for many buying decisions, making efficiently operated order picking systems a critical contributor to a company’s competitiveness. To pick orders fast and with minimal effort, three planning problems need to be solved, namely the assignment of items to storage locations, the consolidation of orders in batches and the routing of the order pickers through the warehouse. Even though the problems are strongly interdependent, they have so far largely been solved separately, leading to losses in efficiency. Recent research has shown that a joint solution can lead to significant performance improvements as compared to individual solutions. Up to now, no method is available that solves all three problems jointly. This work contributes to closing this research gap by proposing an iterative heuristic method that solves the problems jointly and that takes account of future dynamics in customer demand and their influence on the three planning problems. The performance of the method is illustrated in numerical experiments. The results of our studies indicate that the method may lead to significant savings in travel distance.