A Two-Stage Stochastic Optimization Framework for Environmentally Sensitive Oil Spill Response Resource Allocation in the Arctic
Md Ashiqur Rahman, Mustofa Tanbir Kuhel, Clara Novoa
TL;DR
The paper tackles Arctic oil spill response planning under uncertainty by coupling station siting and dynamic resource allocation within a two-stage stochastic MILP. It integrates spill size uncertainty, environmental sensitivity via NOAA ESI, and Arctic logistics under realistic constraints, solved with Gurobi and evaluated through VSS and EVPI analyses. The study finds a 35.5% improvement over deterministic planning and identifies environmental sensitivity as the dominant driver of effective response, while highlighting the value of robust inter-station transfers and flexible deployments. These insights support risk-informed infrastructure investments and operational resilience for Arctic emergency planners.
Abstract
The risk of oil spills in the Alaskan Arctic has become an urgent environmental and logistical concern as maritime traffic increases under climate driven sea ice retreat. Traditional deterministic response planning models fail to represent key uncertainties, including variable spill magnitudes, changing environmental sensitivity, and infrastructure limitations. This study develops a two-stage stochastic mixed integer linear programming framework that jointly optimizes the location of oil spill response stations and the allocation of heterogeneous resources across multiple probabilistic spill scenarios. The model integrates a weighted objective that combines spill volume, environmental sensitivity index (ESI), response time, and costs for station setup, deployment, and inter station transfer. Separate importance weights for coverage and cost, together with internal ecological weights, allow decision makers to balance ecological protection and operational efficiency. Data was compiled from Alaska Department of Environmental Conservation spill records and National Oceanic and Atmospheric Administration ESI layers and are converted into model ready scenarios through harmonization and sampling. The model is solved with the Gurobi optimizer, and sensitivity analysis is performed over 324 combinations of importance and ecological weights. Results show about a 35.45% percent improvement in response effectiveness over deterministic methods, as confirmed by the value of the stochastic solution, and reveal clear tradeoffs between cost and ecological coverage. The framework provides a data driven decision support tool for Arctic emergency planners that simultaneously accounts for uncertainty, environmental sensitivity, and realistic logistical constraints.