Vehicle Routing for Resource Management in Time-Phased Deployment of Sensor Networks

Abstract

Time-phased sensor-network deployment refers to the delivery of a set of sensors to their predetermined locations at exact times by a fleet of vehicles. Applications for such network deployments include wilderness search and rescue (WiSAR) and wildfire monitoring, where desirable resource management would imply allowing the vehicles to perform other tasks between deliveries. The goal of this paper is, thus, to formulate and solve a vehicle-routing problem (VRP) for such just-in-time time-phased sensor-network deployments. The proposed optimization method for the modified VRP outlined herein has two primary novelties: 1) the consideration of spare time as the objective function and 2) the use of a targeted local-search (LS) method. The spare-time objective function was formulated to address the uniqueness of the modified routing problem at hand. The targeted LS algorithm, on the other hand, was developed to tangibly improve the efficiency of the search for the optimal values of the chosen objective function. The proposed vehicle-route-planning method was validated via a range of simulated WiSAR scenarios, some of which are included herein. The robustness of the method to variations in problem parameters was also investigated. Note to Practitioners —The resource-management problem addressed in this paper is applicable to scenarios wherein a fleet of vehicles visits a set of locations at predetermined times to provide services while carrying out other tasks in-between. Such time-phased applications include the deployment of sensor networks for wilderness search and rescue or wildfire monitoring, patient transportation services that can handle emergencies, and courier services that can cope with urgent express requests. The primary inputs to the proposed vehicle-routing algorithm are: 1) the physical characteristics of the vehicles (i.e., speed, capacity, and operation time limit) and 2) a service plan (i.e., service locations and corresponding exact service times). The algorithm yields best possible routes (a string of assigned service locations) for all the vehicles, by maximizing spare time between deliveries (within a reasonable computation time). The method also allows for changes in service plan in real time.