ABSTRACT

This paper investigates the optimization of Reservation-based Autonomous Car Sharing (RACS) systems, aiming at minimizing the total vehicle travel time and customer waiting time. Thus, the RACS system and its routing are formulated with a consideration for system efficiency and passengers’ concerns. A meta-heuristic Tabu search method is investigated as a solution approach, in combination with K–Means (KMN–Tabu) or K–Medoids (KMD–Tabu) clustering algorithms. The proposed solution algorithms are tested in two different networks of varying complexity, and the performance of the algorithms is evaluated. The evaluation results show that the TS method is more suitable for small-scale problems, while KMD–Tabu is suitable for large-scale problems. However, KMN-Tabu has the least computation time, although the solution quality is lower.

摘要

本文研究了基于预订的自动驾驶汽车共享 (RACS) 系统的优化，旨在最大限度地减少车辆总行程时间和客户等待时间。因此，RACS 系统及其路线的制定考虑了系统效率和乘客的关注。结合 K-Means (KMN-Tabu) 或 K-Medoids (KMD-Tabu) 聚类算法，研究了元启发式禁忌搜索方法作为解决方案方法。所提出的解决方案算法在两个不同复杂度的不同网络中进行了测试，并评估了算法的性能。评估结果表明，TS方法更适合小规模问题，而KMD-Tabu方法更适合大规模问题。然而，KMN-Tabu 的计算时间最少，尽管解的质量较低。