考虑碳排放的海洋垃圾收集船路径鲁棒优化  

Robust optimization of path for marine debris collection ships considering carbon emissions

作　　者：刘超毅 段刚[1] 李清悦 LIU Chaoyi;DUAN Gang;LI Qingyue(School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)

机构地区：[1]兰州交通大学交通运输学院,甘肃兰州730070

出　　处：《上海海事大学学报》2024年第3期31-39,74,共10页Journal of Shanghai Maritime University

摘　　要：针对海洋垃圾收集船路径优化问题中垃圾质量具有不确定性的特点,构建船舶路径鲁棒优化模型。该模型以总成本最低为目标,其中总成本包括运输成本和固定成本,同时考虑船舶载质量和时间窗等约束条件。通过鲁棒等价变化和对偶变化将不确定性模型转化为确定性混合整数规划模型,并设计一种节约里程算法、邻域搜索算法与模拟退火算法相结合的混合遗传算法进行求解。结果表明,所提出的模型能较好地抵抗垃圾质量不确定性的影响。所设计的混合遗传算法比传统遗传算法和模拟退火算法求得的总成本分别减少了5.94%和8.70%。对垃圾质量的不确定参数和碳税进行灵敏度分析,为决策者制定合适的碳排放政策提供参考。Aiming at the uncertainty of marine debris mass in debris collection ship path optimization issue,a ship path robust optimization model is constructed.The model aims to minimize the total cost,which includes transportation cost and fixed cost,meanwhile taking into account constraints such as ship load mass and time window.The uncertainty model is transformed into a deterministic mixed integer programming model through robust equivalence and duality changes,and a hybrid genetic algorithm is designed to solve the model,which combines the mileage saving algorithm,the neighborhood search algorithm and the simulated annealing algorithm.The results show that the proposed model can resist the influence of debris mass uncertainty better.Compared with the traditional genetic algorithm and the simulated annealing algorithm,the total cost of the designed hybrid genetic algorithm decreases by 5.94%and 8.70%,respectively.The sensitivity of uncertainty parameters related to debris mass and carbon tax is analyzed,which provides a reference for decision-makers to formulate appropriate carbon emission policies.

关 键 词：水路运输 船舶路径 遗传算法 碳排放 鲁棒优化 

分 类 号：F550.72[经济管理—产业经济]