文献类型：期刊文献

英文题名：Scheduling Optimization of Offshore Oil Spill Cleaning Materials Considering Multiple Accident Sites and Multiple Oil Types

作者：Li, Kai[1,2,3];Yu, Hongliang[1,2];Xu, Yiqun[1,2];Luo, Xiaoqing[4]

机构：[1]Jimei Univ, Sch Marine Engn, Xiamen 361021, Peoples R China;[2]Fujian Prov Key Lab Ship & Ocean Engn, Xiamen 361021, Peoples R China;[3]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524055, Peoples R China;[4]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang 524088, Peoples R China

年份：2022

卷号：14

期号：16

外文期刊名：SUSTAINABILITY

收录：SSCI(收录号：WOS:000845344500001)、SCI-EXPANDED(收录号：WOS:000845344500001)、、Scopus(收录号：2-s2.0-85137701899)、WOS

基金：This study was supported by Fujian Provincial Science and Technology Planning Project (2020H0018; 2021H0020) and the Zhanjiang City Science and Technology Development Special Fund Competitive Allocation Project (NO.2021A05034).

语种：英文

外文关键词：emergency management; offshore oil spill; decontamination material scheduling; improved genetic algorithm; time window; multiobjective optimization

外文摘要：Coastal governments have been preventing and controlling pollution in the marine environment by enhancing the construction of hardware and software facilities. The dispatch of offshore oil spill cleaning materials must be upgraded and optimized to cope with repeated offshore oil leak incidents while simultaneously improving cleaning efficiency and the ability to resist oil spill hazards. Accordingly, we set up a multiobjective optimization model with time window constraints to solve the scheduling optimization problem of offshore oil spill accidents with multiple locations and oil types. This model integrates the minimal sum of fixed costs, fuel consumption costs, maximum load violation costs, and time window penalty costs to solve the scheduling optimization problem of an offshore oil spill accident. An improved genetic algorithm is designed to solve the proposed mathematical model effectively and to make a scientific decontaminated decision-scheduling scheme. The practicality of the model and algorithm is validated by using a specific instance, demonstrating that the suggested method can effectively solve the schedule optimization problem for cleaning materials.