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.

Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods. A key requirement to enable this accelerated improvement cycle is that the simulator is able to span the various sources of complexity that can be found in the real recommendation environment that it simulates. With the emergence of interactive and data-driven methods—e.g., reinforcement learning or online and counterfactual learning-to-rank—that aim to achieve user-related goals beyond the traditional accuracy-centric objectives, adequate simulators are needed. In particular, such simulators must model the various mechanisms that render the recommendation environment dynamic and interactive, e.g., the effect of recommendations on the user or the effect of biased data on subsequent iterations of the recommender system. We therefore propose SARDINE, a flexible and interpretable recommendation simulator that can help accelerate research in interactive and data-driven recommender systems. We demonstrate its usefulness by studying existing methods within nine diverse environments derived from SARDINE, and even uncover novel insights about them.

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.

We study the problem of finding bi-criteria Pareto optimal journeys in public transit networks. We extend the Trip-Based Public Transit Routing (TB) approach [Sascha Witt, 2015] to allow for users to select modes of interest at query time. As a first step, we modify the preprocessing of the TB method for it to be correct for any set of selected modes. Then, we change the bi-criteria earliest arrival time queries, and propose a similar algorithm for latest departure time queries, that can handle the definition of the allowed mode set at query time. Experiments are run on 3 networks of different sizes to evaluate the cost of allowing for mode personalization. They show that although preprocessing times are increased, query times are similar when all modes are allowed and lower when some part of the network is removed by mode selection.

Designing a public urban policy is a demanding process which requires both time and money with no warranty of its efficiency. It involves knowledge about the purpose of urban design, behaviors of users and needs in terms of mobility. We believe that in the near future, decision makers will have to react and more frequently adapt public policies, based on the huge amount of available data, feedbacks from both target users and stakeholders. In this paper, we propose a generic agent-based architecture to model and simulate urban policies, which could facilitate the co-design and assessment of public policies in a specific environment. Two agent-based models are coupled with a micromacro dynamic loop, and they can be adapted either by the system using reinforcement learning, or by the stakeholders using simulation results. A generic formalism is elaborated to represent urban policies, which can be instantiated in a co-design approach between the policymaker and our system. An experimentation is conducted on an urban mobility policy, related to the configuration of parking price system in downtown area. The agent’s behavior and environment are developed to be as realistic as possible, based on a real-world source of modeling. However, our architecture has been designed to be generic, exploiting infrastructure data from any city using available community data (Open Street Map). The scenario of parking pricing shows that the system learns postpolicy behaviors and can propose some adjustments (e.g., specific actions to apply, when and how) to better meet the stakeholders’ objectives (e.g., maximize parking gains). The policy maker can then choose to validate the provided policies, or modified them for additional simulations.