Foundational Courses Department, Shandong University of Science and Technology, Taian 271000, China.
Comput Intell Neurosci. 2022 Apr 30;2022:7898871. doi: 10.1155/2022/7898871. eCollection 2022.
In order to study multimedia urban road path optimization based on genetic algorithm, a dynamic path optimization based on genetic algorithm is proposed. Firstly, for the current situation of traffic congestion, time constraints are strictly considered based on the traditional hard time window logistics distribution vehicle scheduling problem model. Then, the mathematical model is established, and the optimal solution is solved by the combination of decomposition coordination algorithm and genetic algorithm. We divide multiple customers into different customer groups and determine the service object order of each express car in each customer group, so as to obtain the most valuable scheduling scheme. Finally, in the process of solving the model, the relevant and reliable distribution basis for enterprise distribution is collected, including customer geographical coordinates, demand, delivery time window, unit cost required for loading and unloading, loading and unloading time, and penalty cost to be borne by distribution enterprises after early arrival and late arrival. Using the improved genetic algorithm, the optimal solution of each objective function is actually obtained in about 140 generations, which is faster than that before the improvement. Using the genetic algorithm based on sequence coding, a hybrid genetic algorithm is constructed to solve the model problem. Through the comparative analysis of experimental data, it is known that the algorithm has good performance, is a feasible algorithm to solve the VSP problem with time window, and can quickly obtain the vehicle routing scheduling scheme with reference value.
为了研究基于遗传算法的多媒体城市道路路径优化，提出了一种基于遗传算法的动态路径优化方法。首先，针对当前交通拥堵的现状，在传统的硬时间窗物流配送车辆调度问题模型基础上严格考虑时间约束。然后，建立数学模型，并通过分解协调算法和遗传算法的组合来求解最优解。我们将多个客户分为不同的客户群，并确定每个客户群中每个快递车的服务对象顺序，从而获得最有价值的调度方案。最后，在模型求解过程中，收集了企业配送的相关可靠配送基础数据，包括客户地理位置坐标、需求、交货时间窗口、装卸所需单位成本、装卸时间以及配送企业提前到达和迟到时需要承担的罚款成本。通过改进遗传算法，实际上在大约 140 代中获得了每个目标函数的最优解，比改进前更快。使用基于序列编码的遗传算法，构建了一种混合遗传算法来解决模型问题。通过实验数据的对比分析，可知该算法具有良好的性能，是一种解决带时间窗 VSP 问题的可行算法，能够快速获得具有参考价值的车辆路径调度方案。
