Clustered tabu search optimization for reservation-based shared autonomous vehicles

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.

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    • © 2020 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
  • Authors:
    • Su, Shun
    • Chaniotakis, Emmanouil
    • Narayanan, Santhanakrishnan
    • Jiang, Hai
    • Antoniou, Constantinos
  • Publication Date: 2022-2

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  • English

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  • Accession Number: 01839937
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 24 2022 5:26PM