Department of Industrial Engineering, Federal University of Rio de Janeiro, Moniz Aragão, 360, Cidade Universitária, Rio de Janeiro 21941-594, Rio de Janeiro, Brazil.
Department of Industrial Engineering, Federal University of Rio de Janeiro, Moniz Aragão, 360, Cidade Universitária, Rio de Janeiro 21941-594, Rio de Janeiro, Brazil.
Mar Pollut Bull. 2024 Oct;207:116829. doi: 10.1016/j.marpolbul.2024.116829. Epub 2024 Aug 18.
In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.
在海上油气项目发生溢油事故时，必须在关键时间内通过被称为溢油应急响应船（OSRV）的船只将围堵和分散设备送到受影响的区域。在这里，我们开发了一种优化工具，该工具与溢油轨迹模拟模型集成在一起，包括确定性和随机两种选择，以支持在 OSRV 操作的战略规划期间进行决策。该工具是使用 GNOME 为溢油模拟和 GUROBI 来解决优化模型而在 Python 中构建的。该工具应用于巴西的一个案例研究，并提供了相关建议。在研究贡献方面，我们证明了溢油模拟和数学建模在 OSRV 战略运营规划方面的集成是可行的，我们用一种创新的策略探索了问题的随机性，并展示了框架在实际操作中的灵活性和易于适用性。
