A Novel Approach for Modeling Order Picking Paths

Journal Paper
S.G. Ozden, A. E. Smith, and K. R. Gue
Naval Research Logistics
Publication year: 2020

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.

Solving large batches of traveling salesman problems with parallel and distributed computing

Journal Paper
S.G. Ozden, A. E. Smith, and K. R. Gue
Computers & Operations Research, Volume 85, Issue C, Pages 87-96, 2017
Publication year: 2017

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.