A hierarchical hybrid dispatch framework coupled with simulation–optimization for Arctic low-ice oil spill emergency response

To address the challenges of rapidly evolving environmental states, conflicting multiple objectives, and decentralized coordination of resources across response centers in Arctic oil-spill emergencies under low-ice conditions, a hierarchically hybrid scheduling framework coupled with a closed-loop simulation–optimization scheme is proposed. Within this framework, the three decision tiers are encoded as coupled variable blocks within a single mixed-variable NSGA-III chromosome: the upper tier determines resource call-ups, the middle tier generates ship–equipment–voyage schedules, and the lower tier adjusts on-site operations according to real-time sea-ice conditions, sea-surface temperature, surface currents, and oil-spill status. To maintain a clear research boundary, no independent high-resolution hydrodynamic model is developed; instead, a reduced-order advection–diffusion module, which is replaceable by external trajectory models, is employed to incorporate wind-driven and current-driven transport, enabling rolling-horizon updates of oil-spill centroids, impact zones, and ecologically sensitive exposures. The proposed framework is tested against a low-ice scenario in the Barents Sea, wherein the three-objective model—encompassing response time, total cost, and ecological risk—is solved via an enhanced constraint-handling NSGA-III. The balanced solution yields a response time of 25.1 h, a total cost of CNY 7.15 million, an ecological risk of 382 t oil equivalent, a recovery rate of 84.6%, and a composite performance loss index, P(X), of 0.31. Under a 5% sea-ice concentration, this solution reduces P(X) by 63.1%, 65.9%, and 59.2% compared with static deterministic planning, single-objective optimization, and open-loop simulation–optimization, respectively, while simultaneously improving the recovery rate by 13.3–19.8 percentage points; relative to ablation configurations without dynamic iterative correction and without hierarchical progression, P(X) is lowered by 43.6% and 35.4%, respectively. Case-study results demonstrate that the proposed framework is capable of generating interpretable resource-allocation and voyage schedules while maintaining robust composite performance across 0%, 5%, and 10% sea-ice-concentration scenarios.