Artificial Intelligence for Warehouse Picking Optimization - An NP-Hard Problem

This master thesis work demonstrates an approach to processing warehouse management system log-dump datasets and architectural CAD floor maps for graphical network representation of large warehouse environments. This representation enables use of stochastic search and linear programming algorithms (Simulated Annealing and Concorde TSP Solver) for the purpose of warehouse pick-run routing optimization (a case of the NP-Hard Travelling Salesperson Problem). By comparing over 20 000 historic routes with optimized routes for the same pick-run instances, it is shown that optimized routing reduces distance travelled by warehouse forklift pickers at an Ahlsell Warehouse by approximately 15%, and that solving these optimal routes can be achieved, for practical purposes, in near-instantaneous time