14,737 research outputs found
Dynamic Multi-Vehicle Routing with Multiple Classes of Demands
In this paper we study a dynamic vehicle routing problem in which there are
multiple vehicles and multiple classes of demands. Demands of each class arrive
in the environment randomly over time and require a random amount of on-site
service that is characteristic of the class. To service a demand, one of the
vehicles must travel to the demand location and remain there for the required
on-site service time. The quality of service provided to each class is given by
the expected delay between the arrival of a demand in the class, and that
demand's service completion. The goal is to design a routing policy for the
service vehicles which minimizes a convex combination of the delays for each
class. First, we provide a lower bound on the achievable values of the convex
combination of delays. Then, we propose a novel routing policy and analyze its
performance under heavy load conditions (i.e., when the fraction of time the
service vehicles spend performing on-site service approaches one). The policy
performs within a constant factor of the lower bound (and thus the optimal),
where the constant depends only on the number of classes, and is independent of
the number of vehicles, the arrival rates of demands, the on-site service
times, and the convex combination coefficients.Comment: Extended version of paper presented in American Control Conference
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On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
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
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