铁道运输与经济2024，Vol.46Issue(8)：113-125,13.DOI:10.16668/j.cnki.issn.1003-1421.2024.08.12
考虑碳排放及时间满意度的高铁快运"站到门"配送优化模型及算法研究
Optimization Model and Algorithm for Station to Door Distribution of High Speed Railway Express Considering Carbon Emissions and Time Satisfaction
摘要
Abstract
Under the background of carbon peaking and carbon neutrality goals,there are high distribution costs,low service capability and difficulty in solving the terminal distribution in the"station-to-door"distribution stage of high speed railway(HSR)express.To solve these problems,combining with the characteristics of HSR express business,the Sigmoid drop index was introduced.The"station-to-door"distribution time satisfaction function was obtained through function transformation.The customer importance was determined by the first freight rate of different product types of HSR express.The multi-objective optimization model of the"station-to-door"distribution of HSR express was constructed with the goal of service satisfaction,transportation economic cost,and carbon emission.A fast non-dominated sorting multi-objective whale optimization algorithm(FNSMWOA)was proposed and designed to address the shortcomings of the whale optimization algorithm(WOA)that easily falls into local optimization solution.The algorithm was utilized to solve the problem.The effectiveness of the proposed model and algorithm was verified by an example.The results show that the FNSMWOA has performed better in both global search ability and local search ability.The Pareto solution frontier solution was obtained by experiments,with the Pareto optimal solution scheme considering different optimization purposes,which provides a new idea for the optimization of the"station-to-door"distribution of HSR express.关键词
高铁快运/碳排放/时间满意度/"站到门"/多目标优化/鲸鱼优化算法Key words
High Speed Railway Express/Carbon Emission/Time Satisfaction/"Station to Door"/Multi-objective Optimization/Whale Optimization Algorithm分类
交通工程引用本文复制引用
田金松,李德仓..考虑碳排放及时间满意度的高铁快运"站到门"配送优化模型及算法研究[J].铁道运输与经济,2024,46(8):113-125,13.基金项目
国家自然科学基金项目(72061021) （72061021）
