• ISSN 0258-2724
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Volume 58 Issue 1
Jan.  2023
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Journal of Southwest Jiaotong University  > 2023  >  58(1): 74-82.
JIANG Yangsheng, LI Yan, LI Hao, HU Lu, TANG Youhua. Optimization for Joint Relocation of Carsharing Based on Modular Simulation[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 74-82. doi: 10.3969/j.issn.0258-2724.20210083
Citation: JIANG Yangsheng, LI Yan, LI Hao, HU Lu, TANG Youhua. Optimization for Joint Relocation of Carsharing Based on Modular Simulation[J]. Journal of Southwest Jiaotong University, 2023, 58(1): 74-82. doi: 10.3969/j.issn.0258-2724.20210083
shu

Optimization for Joint Relocation of Carsharing Based on Modular Simulation

doi: 10.3969/j.issn.0258-2724.20210083
  • 1.

    School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China

  • 2.

    National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu 610031, China

  • Received Date: 02 Feb 2021
  • Rev Recd Date: 18 Aug 2021
    • Available Online: 15 Oct 2022
  • Publish Date: 13 Sep 2021
    Abstract

  • It is difficult for operators to effectively solve the profitable difficulty caused by the imbalanced distribution of shared vehicles when considering staff-based and customer-based relocation alone. Thus, based on the traditional space-time network, the impact of time-varying road congestion and trip demands on the operation is considered. Based on C# language and O2DES (object-oriented discrete event simulation) framework, an efficient carsharing system model composed of modular station and road segment models is built. Moreover, a simulation-optimization model that jointly determines vehicle inventory thresholds and trip pricing is proposed to maximize the daily net revenue of operators. In order to solve the global optimization problem in a random environment, an elitist genetic algorithm (EGA) with optimal computing budget allocation (OCBA) is designed. Finally, a case study in Chengdu with five sites is conducted to demonstrate the efficiency of the proposed simulation-optimization model. The results show that with the same fleet size, the optimal design can increase the average daily net revenue by 10.37%−162.30% compared with customer-based relocation (fixed pricing); the optimized scheme can increase the profit by 15.34% compared with separate staff-based relocation.

     

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