A dynamic pickup and delivery problem in mobile networks under information constraints,

Abstract-This paper considers a network in which a set of vehicles is responsible for picking up and delivering messages that arrive according to a Poisson process. Message pickup and delivery locations are uniformly distributed in a convex region. The vehicles are required to pickup and deliver the messages so that the average delay is minimized. It is required that the vehicle that picks up a message must be the one to deliver it. This problem is called the dynamic pickup and delivery problem (DPDP) and has applications in the context of autonomous vehicles and wireless ad hoc networks. The control policies considered are separable into two parts: an assignment policy used by a centralized controller to assign arriving messages to the vehicles for service and a service policy used by each vehicle to determine the service routes through its assigned messages. Lower bounds are provided on the delay achievable by separable control policies that depend on the information constraints in place. It is proved that the optimal average delay scaling can be reduced when message destination information is available to the centralized controller in addition to the message source information. The paper also provides policies that achieve these scaling bounds, proving that these bounds are tight. Index Terms-Dial-a-ride problem (DARP), dynamic pickup and delivery problem (DPDP), dynamic traveling repair-person problem (DTRP), less-than-truckload (LTL), unmanned aerial vehicle (UAV)

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page

Dynamic routing model and solution methods for fleet management with mobile technologies

Author name used in this publication: K. L. ChoyAuthor name used in this publication: Wenzhong Shi2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Job Selection in a Network of Autonomous UAVs for Delivery of Goods

This article analyzes two classes of job selection policies that control how a network of autonomous aerial vehicles delivers goods from depots to customers. Customer requests (jobs) occur according to a spatio-temporal stochastic process not known by the system. If job selection uses a policy in which the first job (FJ) is served first, the system may collapse to instability by removing just one vehicle. Policies that serve the nearest job (NJ) first show such threshold behavior only in some settings and can be implemented in a distributed manner. The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision-making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality. In particular, we compute a lower bound for the infrastructure expenditure required to achieve a certain expected delivery time. The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid-term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles