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

Chuanxiang Ren, Hui Xu, Changchang Yin (), Liye Zhang, Chunxu Chai, Qiu Meng and Fangfang Fu
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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

Sustainability, 2023, vol. 15, issue 24, 1-23

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.

Keywords: shared bicycles; hybrid scheduling; MLP-GA method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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