求解带用户满意度的多目标实时车辆路径问题的改进伊藤算法

Improved ITO Algorithm for Multiobjective Real-time Vehicle Routing Problem with Customers' Satisfaction

基于对标准车辆路径问题的分析,本文构建了一种包括交通因素、客户需求动态改变、用户满意度的多目标动态车辆路径问题模型.针对伊藤算法在求解离散组合优化问题时效率较低、收敛性较差等缺陷,本文以具有通用性的伊藤算法为框架,参考蚁群算法,设计了伊藤-蚂蚁优化算法,并采用正交实验的方法,分析了改进算法参数的设置问题.为了验证改进算法的有效性,文章对标准测试数据集中的数据进行了测试.最后,将标准测试数据改编成符合带用户满意度的多目标实时车辆路径问题模型的测试数据,并用改进算法进行求解.实验结果表明,本文提出的问题模型和改进算法是可行的、有效的.

Based on the analysis of the standard vehicle routing problem,we proposed a multiobjective real-time vehicle rout-ing problem model,referred as MR-VRPCS.The MR-VRPCS considers the traffic factors,customer demand dynamic change and customers’satisfaction.As we know,the ITO algorithm has low efficiency and poor convergence performance on the discrete com-binatorial optimization problems.Therefore,we apply the universal framework of ITO and introduce the Ant Colony Optimization al-gorithm,which has got depth studied in vehicle routing problem,to design the ITO-Ant Optimization algorithm.We analyze the IAO algorithm’s parameter setting problem by the method of orthogonal experiment.Finally,we use the Solomon benchmark test data to prove the effectiveness of the IAO algorithm,and adjust the standard test data to the MR-VRPCS model’s,and resolve it with IAO. The experimental results show the feasibility and effectiveness of the proposed model and algorithm.