We consider a large-scale data center where a fleet of heterogeneous mobile robots and human workers collaborate to handle various installation and maintenance tasks. We focus on the underlying multi-agent task assignment problem which is crucial to optimize the overall system. We formalize the problem as a Markov Decision Process and propose an end-to-end learning approach to solve it. We demonstrate the effectiveness of our approach in simulation with realistic data and in the presence of uncertainty.

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.

In the context of routing in public transit networks, we consider the issue of the customization of walking transfer times, which is incompatible with the preprocessing required by many state-of-the-art algorithms. We propose to extend one of those, the Trip-Based Public Transit Routing algorithm, to take into account at query time user defined transfer speed and maximum transfer duration. The obtained algorithm is optimal for the bicriteria problem of optimizing minimum arrival time and number of transfers. It is tested on two large data sets and the query times are compatible with real-time queries in a production context.

We propose an additional preprocessing step for the Trip-Based Public Transit Routing algorithm, an exact state-of-the art algorithm for bi-criteria min cost path problems in public transit networks. This additional step reduces significantly the preprocessing time, while preserving the correctness and the computation times of the queries. We test our approach on three large scale networks and show that the improved preprocessing is compatible with frequent real-time updates, even on the larger data set. The experiments also indicate that it is possible, if preprocessing time is an issue, to use the proposed preprocessing step on its own to obtain already a significant reduction of the query times compared to the no pruning scenario.