摘要
共享单车系统所面临的主要问题是用户需求与单车供给的不匹配,实施有效的重平衡操作需要权衡成本与服务水平这两个相互矛盾的目标。在“双碳”目标的背景下,考虑了由电动车和燃油车组成的混合车队以及燃油车限行因素,建立了以重平衡总成本最小和未满足重平衡需求量最小为优化目标的多目标混合整数规划模型,并提出了一种改进多目标粒子群算法来求解该问题。不同规模测试例的仿真结果表明,该算法在收敛性、多样性和分布性方面都优于NSGA-Ⅱ和标准多目标粒子群算法,能够得到更高质量的帕累托最优解集。管理者可以依据其自身偏好,从中选择合理的重平衡方案。
The main problem of the bike-sharing system is the mismatch between user demand and bike supply.An effective rebalancing operation requires a trade-off between the conflicting objectives of cost and service level.In the context of the'dual carbon',a multi-objective mixed integer programming model with the goals of minimizing the total cost and minimizing the unmet rebalancing demand was developed,considering the mixed fleets of electric vehicles and fuel vehicles as well as the traffic restrictions to fuel vehicles.Then an improved multi-objective particle swarm optimization was proposed to solve the problem.Simulation results of cases with different sizes show that the algorithm outperforms NSGA-Ⅱand the standard multi-objective particle swarm optimization in terms of convergence,diversity and distribution,and can obtain a higher quality Pareto solution set.Managers can choose their reasonable solutions according to their own preferences.
作者
贾永基
赵丽娜
JIA Yongji;ZHAO Lina(Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China)
出处
《工业工程与管理》
CSCD
北大核心
2023年第5期1-9,共9页
Industrial Engineering and Management
基金
上海市哲学社会科学规划基金资助项目(2018BGL018)
中央高校基本科研专项资金资助项目(2232018H-07)。
关键词
共享单车重平衡
多目标优化
混合车队
交通限行
多目标粒子群算法
bike-sharing rebalancing
multi-objective optimization
mixed fleets
traffic restrictions
multi-objective particle swarm optimization
