Exact algorithms for routing electric autonomous mobile robots in intralogistics

Abstract

In intralogistics and manufacturing, autonomous mobile robots (AMRs) are usually electrically powered and recharged by battery swapping or induction. We investigate AMR route planning in these settings by studying different variants of the electric vehicle routing problem with due dates (EVRPD). We consider three common recharging strategies: battery swapping, inductive recharging with full recharges, and inductive recharging with partial recharges. Moreover, we consider two different objective functions: the standard objective of minimizing the total distance traveled and the minimization of the total completion times of transport jobs. The latter is of particular interest in intralogistics, where time aspects are of crucial importance and the earliest possible completion of jobs often has priority. In this context, recharging decisions also play an essential role. For solving the EVRPD variants, we propose exact branch-price-and-cut algorithms that rely on ad-hoc labeling algorithms tailored to the respective variants. We perform an extensive computational study to generate managerial insights on the AMR route planning problem and to assess the performance of our solution approach. The experiments are based on newly introduced instances featuring typical characteristics of AMR applications in intralogistics and manufacturing and on standard benchmark instances from the literature. The detailed analysis of our results reveals that inductive recharging with partial recharges is competitive with battery swapping, while using a full-recharges strategy has considerable drawbacks in an AMR setup.