In this paper, a heuristic method which contributes to the solution of the Daily Activity Chains Optimization problem with the use of Electric Vehicles (DACO-EV) is presented. The DACO-EV is a time-dependent activity-scheduling problem of individual travelers in urban environments. The heuristic method is comprised of a genetic algorithm that considers as its parameters a set of preferences of the travelers regarding their initial activity chains as well as parameters concerning the transportation network and the urban environment. The objective of the algorithm is to calculate the traveler’s optimized activity chains within a single day as they emerge from the improved combinations of the available options for each individual traveler based on their flexibility preferences. Special emphasis is laid on the underlying speed-up techniques of the GA and the mechanisms that account for specific characteristics of EVs, such as consumption according to the EV model and international standards, charging station locations, and the types of charging plugs. From the results of this study, it is proven that the method is suitable for efficiently aiding travelers in the meaningful planning of their daily activity schedules and that the algorithm can serve as a tool for the analysis and derivation of the insights into the transportation network itself.

在本文中，提出了一种启发式方法，该方法有助于解决使用电动汽车 (DACO-EV) 的日常活动链优化问题。DACO-EV 是城市环境中个人旅行者的时间相关活动调度问题。启发式方法由遗传算法组成，该算法将一组旅行者关于其初始活动链的偏好以及关于交通网络和城市环境的参数视为其参数。该算法的目标是计算旅行者在一天内的优化活动链，因为它们是根据每个旅行者的灵活性偏好从改进的可用选项组合中出现的。特别强调了 GA 的基本加速技术和考虑 EV 特定特性的机制，例如根据 EV 模型和国际标准的消耗、充电站位置和充电插头的类型。从这项研究的结果来看，证明该方法适用于有效地帮助旅行者有意义地规划他们的日常活动时间表，并且该算法可以作为分析和推导对交通网络本身的见解的工具.