Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics
Abstract
Artificial intelligence (AI) applications are the core challenge for engineering and management science concepts in production and logistics within the next decade. This study analyses the application of AI instances in route planning as a central part of logistics management from an empirical case perspective for retail logistics in Germany. The methods applied encompass fuzzy data envelopment analysis (DEA), slack-based measurement (SBM) fuzzy DEA, and analytic hierarchy process (AHP)-SBM Fuzzy DEA. For the two depots using AI-based routing to the full account, efficiency advantages can be shown in the Fuzzy DEA as well as the SBM fuzzy DEA models. Results further indicate that the methodological approach is adequate for the analysed problem and that the combination with AHP is an interesting addition as, e.g., the perspective of sales managers supersedes that of logistics managers for route planning efficiency - a thought-provoking result pointing at very customeroriented logistics systems.
