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Gavalas, D., Konstantopoulos, C. and Pantzious, G. (2016) Design and Management of Vehicle Sharing Systems: A Survey of Algorithmic Approaches. In: Obaidat, M.S. and Nicopolitidis, P., Eds., Smart Cities and Homes, Morgan Kaufmann, San Francisco, 261-289.
https://doi.org/10.1016/B978-0-12-803454-5.00013-4

has been cited by the following article:

• TITLE: A Self-Adaptive Quantum Genetic Algorithm for Network Flow Vehicle Scheduling Problem

  AUTHORS: Yiming Yan, Aimei Xiao

  KEYWORDS: Network Coding, Quantum Genetic Algorithm, Multicast Networks, Optimization

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.7, July 28, 2021

  ABSTRACT: Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.