Modeling and Optimization of a Spatial Detection System

Abstract

Oil and gas companies are drilling and developing fields in the Arctic Ocean, which is an environment with ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize locations and quantities for the docking stations and the AUVs, as well as the AUV scheduling policies, to maximize security of the platform. We model the system using a multistage stochastic facility location problem to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and two efficient online scheduling heuristics provide lower bounds and upper bounds for the multistage model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to two-dimensional radial detection.