How can we assign vehicles to requests in real time, the instant a call arrives or a vehicle becomes free? We model this asynchronous dispatching problem as a semi-Markov decision process and solve it by combining discrete-event simulation with deep reinforcement learning, validated on real New York City taxi data.
This paper addresses the asynchronous dynamic vehicle dispatching problem (DVDP), in which vehicle assignments must be made in real time as requests arise or vehicles become available. Unlike traditional synchronous dispatching, the asynchronous DVDP …
We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational …
We propose a formulation of the stochastic cutting stock problem as a discounted infinite-horizon Markov decision process. At each decision epoch, given current inventory of items, an agent chooses in which patterns to cut objects in stock in …