Probability, random processes, and estimation theory for engineers
Probability, random processes, and estimation theory for engineers
Mathematics of Operations Research
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
The vehicle routing problem
The coverage problem in a wireless sensor network
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Integrated coverage and connectivity configuration in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Co-Grid: an efficient coverage maintenance protocol for distributed sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
Improved algorithms for orienteering and related problems
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric
IEEE Transactions on Robotics
Dynamic Vehicle Routing with Priority Classes of Stochastic Demands
SIAM Journal on Control and Optimization
Online traveling salesman problem with deadline and advanced information
Computers and Industrial Engineering
Computers and Operations Research
Localization with sparse acoustic sensor network using UAVs as information-seeking data mules
ACM Transactions on Sensor Networks (TOSN)
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In this paper, we study the problem of designing motion strategies for a team of mobile agents, required to fulfill request for on-site service in a given planar region. In our model, each service request is generated by a spatio-temporal stochastic process; once a service request has been generated, it remains active for a certain deterministic amount of time, and then expires. An active service request is fulfilled when one of the mobile agents visits the location of the request. Specific problems we investigate are the following: what is the minimum number of mobile agents needed to ensure that a certain fraction of service requests is fulfilled before expiration? What strategy should they use to ensure that this objective is attained? This problem can be viewed as the stochastic and dynamic version of the well-known vehicle routing problem with time windows. We also extend our analysis to the case in which the time service requests remain active is itself a random variable, describing customer impatience. The customers' impatience is only known to the mobile agents via prior statistics. In this case, it is desired to minimize the fraction of service requests missed because of impatience. Finally, we show how the routing strategies presented in the paper can be executed in a distributed fashion.