A Repeated Route-then-Schedule Approach to Coordinated Vehicle Platooning: Algorithms, Valid Inequalities and Computation

In “A Repeated Route-then-Schedule Approach to Coordinated Vehicle Platooning: Algorithms, Valid Inequalities and Computation,” Luo and Larson propose a novel repeated route-then-schedule algorithmic framework to efficiently solve a complex vehicle routing and scheduling problem arising in the intelligent transportation system. The goal is to maximize the collective savings of a set of vehicles (especially heavy-duty vehicles) by utilizing the fact that platooning vehicles save energy due to reduced aerodynamic drag. In the algorithm, the original simultaneous route-and-schedule approach is decomposed into the routing stage and scheduling stage with a sophisticated learning-like feedback mechanism to update the presumed fuel cost for each vehicle traversing through each road segment. This leads to an iterative change of objective function in the routing problem and thereby changes the routes that are fed to the scheduling problem. This approach helps identify high-quality solution. The algorithmic framework leads to a very tight formulation of subproblems that can be solved in a timely manner.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

SCHEDULED BASED CLOUD RESOURCE ALLOCATION

The objective of this research The tremendous implementation of cloud computing technology has become a new trend that users can easily utilize high resources through IaaS platform. IaaS is more economical and easier way to have physical resources; in this case Virtual Machines in the cloud, rather than building the infrastructure by their own. To deliver internet services to users such as website, email service or other software applications, a service provider can utilize IaaS platform by leasing virtual infrastructure from a cloud provider and deploy their services on that VMs. However, it becomes a challenge for a service provider to maintain their services due to the increasing number of user requests. They have to maintain resources availability to provide maximum performance to meet their user satisfaction with optimal resources utilization. The approach in this paper will solve this problem by providing service provider a resource monitor module. The module monitors VMs workload based on schedule approach; peak time and off-peak time. According to these two criteria, the service provider can predict and allocate sufficient resources.

An Intelligent Traffic Lights Schedule Approach Using WSNs

We study the problem of traffic lights scheduling in a dynamic three-dimension intersection. In this paper, an intelligent traffic lights schedule approach is proposed to optimize the traffic lights sequence according to traffic data detected and predicted in real-time, e.g. traffic volume, delays and traffic density. Evaluations have been conducted to compare the performance in terms of traffic throughput, and the result depicts that our intelligent approach can obtain better performance.