Control of mobile networks using dynamic vehicle routing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 141-144).This thesis considers the Dynamic Pickup and Delivery Problem (DPDP), a dynamic multi-stage vehicle routing problem in which each demand requires two spatially separated services: pickup service at its source location and then delivery service at its destination location. The Dynamic Pickup and Delivery Problem arises in many practical applications, including taxi and courier services, manufacturing and inventory routing, emergency services, mobile sensor networks, Unmanned Aerial Vehicle (UAV) routing, and delay tolerant wireless networks. The main contribution of this thesis is the quantification of the delay performance of the Dynamic Pickup and Delivery Problem as a function of the number of vehicles, the total arrival rate of messages, the required message service times, the vehicle velocity, and the network area. Two lower bounds are derived. First, the Universal Lower Bound quantifies the impact of spatially separated service locations and system loading on average delay. The second lower bound is derived by reducing the two-stage Dynamic Pickup and Delivery Problem to the single-stage Dynamic Traveling Repairperson Problem (DTRP). Policies are then presented for which these lower bounds are tight as a function of the system scaling parameters (up to a constant). The impact of information and inter-vehicle relays is also studied. The last part of this thesis examines the application of the Dynamic Pickup and Delivery Problem to mobile multi-agent wireless networks from a physical layer perspective, seeking insights for the control of the network to achieve trade-offs between throughput and delay.by Holly A. Waisanen-Hatipoglu.Ph.D

Modelación En Programación Matemática Y Resolución Del Problema De Localización-Ruteo En Logística Urbana

The implementation of urban distribution centers near to city centers to allow freight consolidation is a widely extended initiative worldwide, seeking to improve traffic congestion and quality of life in downtown, among others. This paper considers the problem of locating urban distribution centers and proposes an exact method, based on integer linear programming for strategic, tactical and operational decision-making. The aim is to solve, in an integer manner, location, sizing and operation (vehicle routing) problems in these logistics platforms. The model is validated using real-data taken from the city of SaintÉtienne, France. Computational experiments are also carried out in order to compare the proposed model with existing procedures from the literature. Results show the efficiency and effectiveness of the proposed model and its applicability in real decision-making for medium sized data sets

Dynamic Vehicle Routing for Robotic Systems

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.United States. Air Force Office of Scientific Research (Award FA 8650-07-2-3744)United States. Army Research Office. Multidisciplinary University Research Initiative (Award W911NF-05-1-0219)National Science Foundation (U.S.) (Award ECCS-0705451)National Science Foundation (U.S.) (Award CMMI-0705453)United States. Army Research Office (Award W911NF-11-1-0092