Among all warehouse operations, the order-picking is the most expensive one and its cost is mainly due to the traveling expenses. In this paper, we study the warehouse routing problems (WRP) so as to reduce the traveling cost, i.e., traveling distance. We formulated the problems and proved that they are NP-Complete. Furthermore, these problems belong to the well-known vehicle routing problems (VRP) . However, under a warehouse's setting, the problem constraints can be different from those of VRP. More important, under two widely used layouts of warehouses, the problems have good structures and the one vehicle routing problem can be tackled by using dynamic programming approach. Based on the one vehicle's situation, heuristics based on a very large scale neighborhood (VLSN) and other me...[ Read more ]

Among all warehouse operations, the order-picking is the most expensive one and its cost is mainly due to the traveling expenses. In this paper, we study the warehouse routing problems (WRP) so as to reduce the traveling cost, i.e., traveling distance. We formulated the problems and proved that they are NP-Complete. Furthermore, these problems belong to the well-known vehicle routing problems (VRP) . However, under a warehouse's setting, the problem constraints can be different from those of VRP. More important, under two widely used layouts of warehouses, the problems have good structures and the one vehicle routing problem can be tackled by using dynamic programming approach. Based on the one vehicle's situation, heuristics based on a very large scale neighborhood (VLSN) and other meta-heuristics were designed to solve multi-vehicle routing problems with constraints. We compared computational results of different methods, including Integer Programming models, relaxations and different heuristic and identified that the VLSN based heuristic is efficient for WRP.