Even though order picking is the most costly operation in a warehouse, current design practices have used the same principles (straight rows with parallel pick aisles and perpendicular cross aisles) to reduce travel distances between pick locations for more than sixty years. We present an open-source computational software system for facilitating the design of warehouse layouts to near-optimality considering average walking distance of the picker as the objective function. This software is particularly novel because a wide variety of traditional and innovative designs are automatically generated and evaluated. For the warehouse design parameters we consider the rectangular aspect ratio of the floor plan, the number and location of cross aisles, the number and location of pick aisles, and the location of a single input/output location. The main components of the design system are importing pick list profile data, creating the warehouse layout design as a network, product allocation (slotting) of SKUs through the warehouse, routing of pickers on a sample of orders using an exact routing algorithm, and design optimization using a meta-heuristic. We provide both mathematical and computational descriptions of the algorithms used by the software system, describe the types of problems that can be solved, and summarize our computational experience. This software is open source available on a GitHub website under an MIT license.
We introduce the visibility graph as an alternative way to estimate the length of a route traveled by order pickers in a warehouse. Heretofore it has been assumed that workers travel along a network of travel paths corresponding to centers of aisles, including along the right angles formed where picking aisles join cross aisles. A visibility graph forms travel paths that correspond to more direct and, we believe, more appropriate “travel by sight.”We compare distance estimations of the visibility graph and the aisle-centers method analytically for a common traditional warehouse design. We conduct a range of computational experiments for both traditional and fishbone warehouse layouts to assess the impact of this change in distance metric. Distance estimations using aisle-centers calculates a length of a picking tour on average 10–20% longer compared to distance estimations based on the visibility graph. The visibility graph metric also has implications for warehouse design: when comparing three traditional layouts, the distance model using a visibility graph resulted in choosing a different best layout in 13.3% of the cases.
In this paper, we describe and compare serial, parallel, and distributed solver implementations for large batches of Traveling Salesman Problems using the Lin-Kernighan Heuristic (LKH) and the Concorde exact TSP Solver. Parallel and distributed solver implementations are useful when many medium to large size TSP instances must be solved simultaneously. These implementations are found to be straightforward and highly efficient compared to serial implementations. Our results indicate that parallel computing using hyper-threading for solving 150- and 200-city TSPs can increase the overall utilization of computer resources up to 25 percent compared to single thread computing. The resulting speed-up/physical core ratios are as much as ten times better than a parallel and concurrent version of the LKH heuristic using SPC3 in the literature. We illustrate our approach with an application in the design of order picking warehouses.