Traumatic ulnar nerve dislocation: an unusual entity

Ulnar nerve dislocation is defined as abnormal movement of the ulnar nerve at the elbow joint. This dislocation has been reported in 16% of asymptomatic arms. However, posttraumatic ulnar nerve subluxation remains a rare clinical entity. We present a unique case of posttraumatic ulnar nerve dislocation and describe clinical characteristics and etiology of this injury and how it was managed

Online Large-Scale Taxi Assignment: Optimization and Learning

We propose a solution method for online vehicle routing, which integrates a machine learning routine to improve tours’ quality. Our optimization model is based on the Bertsimas et al. (2019) re-optimization approach. Two separate routines are developed. The first one uses a neural network to produce realistic pick-up times for the customers to serve. The second one relies on Q-learning in addition to random walks for the construction of the backbone graph corresponding to the instance problem of each time step. The second routine gives improved results compared to the original approach

On the impact of spatio-temporal granularity of traffic conditions on the quality of pickup and delivery optimal tours

International audienceOptimizing the duration of delivery tours is a crucial issue in urban logistics. In most cases, travel times between locations are considered as constant for the whole optimization horizon. Making these travel times time-dependent is particularly relevant in real urban traffic environments as traffic conditions and thus travel speeds vary according to the time of the day.In this paper, we review the literature on time-dependent routing problems, with a specific focus on benchmarks and performance criteria used to experimentally evaluate the interest of exploiting time-dependent data, showing the lack of studies on the impact of spatio-temporal features of the benchmark on solutions. Hence, we introduce a new benchmark produced from a realistic traffic flow micro-simulation of Lyon city, allowing us to consider different levels of spatial granularity (i.e., number of sensors used to measure traffic conditions) and temporal granularity (i.e., frequency of measures). Finally, we experimentally evaluate the impact of the spatio-temporal granularity on the quality of solutions for different classical problems, including the traveling salesman problem, the pickup and delivery problem, and the dial-a-ride problem

Autonomous Ride-Sharing Service Using Graph Embedding and Dial-a-Ride Problem: Application to the Last-Mile Transit in Lyon City

Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features. In this study, we propose a holistic solving approach to this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering the exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of “Vallée de la Chimie” in Lyon city, France. Instances involving more than 50 transport requests and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with lower vehicle kilometers traveled compared to the constrained K-means-based reduction. Reductions in terms of GHG emissions are estimated to be around 75% less than the private vehicle mode in our applied service. A sensitivity analysis is also provided

Pilotage Dynamique de Transport Sanitaire: Apprentissage et Optimisation

International audienceNous proposons deux méthodes de résolution pour le transport dynamique, i.e. arrivage des requêtes en temps réel, suivant une synergie entre apprentissage automatique et optimisation. La méthode d'optimisation de base est une ré-optimisation selon [Bertsimas et al., 2019]. La première amélioration utilise un réseau de neurones pour exploiter le contexte des instances à résoudre et produire des temps de ramassage de points réalistes. Tandis que la deuxième amélioration fait usage de l'apprentissage par renforcement en plus de marches aléatoire pour la construction du graphe support pour l'instance du problème correspondant à chaque pas de temps. Les benchmarks considérés dans le projet sont ceux de [Bertsimas et al., 2019] pour le transport urbain et de [Skiredj, 2021] pour le transport sanitaire

Online Large-Scale Taxi Assignment: Optimization and Learning

International audienceWe propose a solution method for online vehicle routing, which integrates a machine learning routine to improve tours’ quality. Our optimization model is based on the Bertsimas et al. (2019) re-optimization approach. Two separate routines are developed. The first one uses a neural network to produce realistic pick-up times for the customers to serve. The second one relies on Q-learning in addition to random walks for the construction of the backbone graph corresponding to the instance problem of each time step. The second routine gives improved results compared to the original approach