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Research on Hybrid Scheduling of Shared Bikes Based on MLP-GA Method

Author

Listed:
  • Chuanxiang Ren

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Hui Xu

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Changchang Yin

    (College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Liye Zhang

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Chunxu Chai

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Qiu Meng

    (College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Fangfang Fu

    (College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

Bike-sharing systems with convenience and flexibility have been appearing more and more in cities and become a necessary tool of travel for people. However, the distribution of bikes is highly unbalanced due to the changes in user demand, which leads to the unfavorable situation of “no bikes available” or “too many bikes” at some bike stations. For this reason, this paper proposes a hybrid scheduling method, which combines truck-based scheduling (TBS) and user-based scheduling (UBS). Firstly, a hybrid scheduling model (HBS) combining TBS and UBS is established. Secondly, a method combining multilayer perceptron and genetic algorithm (MLP-GA) is proposed to solve the model. Thirdly, the HBS model is simulated and analyzed by the example. The results show that the MLP-GA method converges, has a faster running time than the genetic algorithm and can obtain solutions with lower total cost and shorter optimal truck path. Further analysis shows that HBS is more implementable in practice and can shorten the optimal truck path and reduce the scheduling total cost while allowing users to use the shared bike in an affordable way, thus realizing the efficient operation of the shared bike system. Finally, a sensitivity analysis of the reward coefficients is performed. This shows that as the reward coefficient increases, the cost of HBS generally shows an increasing trend when the reward coefficient is small, reaches a maximum value when the reward coefficient is 0.6, and decreases slightly thereafter.

Suggested Citation

  • Chuanxiang Ren & Hui Xu & Changchang Yin & Liye Zhang & Chunxu Chai & Qiu Meng & Fangfang Fu, 2023. "Research on Hybrid Scheduling of Shared Bikes Based on MLP-GA Method," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16634-:d:1295665
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    References listed on IDEAS

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    1. Bulhões, Teobaldo & Subramanian, Anand & Erdoğan, Güneş & Laporte, Gilbert, 2018. "The static bike relocation problem with multiple vehicles and visits," European Journal of Operational Research, Elsevier, vol. 264(2), pages 508-523.
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    3. Shaheen, Susan & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future," Institute of Transportation Studies, Working Paper Series qt79v822k5, Institute of Transportation Studies, UC Davis.
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    5. Shaheen, Susan A & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6qg8q6ft, Institute of Transportation Studies, UC Berkeley.
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