Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency (original) (raw)

Abstract

By proposing the concept of timeline, transform dynamic vehicle scheduling problem into a series of static vehicle scheduling problems. With the objective function of benefit maximization, the cloud preference model of dynamic empty car scheduling is built considering empty car delay time constraint. The non-dominated antibodies are proportionally immune clonal according to their cloud preference, which are defined by their cloud application preferences. It is beneficial to enhance the forecasting accuracy of the immune gene manipulation, and to increase the speed of finding the optimal solution based on the application preference. Experimental results conclusively demonstrate the efficiency and effectiveness of the improving system availability, load balancing deviation and valid time brought by the proposed algorithm in cloud computing environments, conditions and that more close to the reality, empty car scheduling model for specific time was established.

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Acknowledgements

This study is supported by The National Natural Science Foundation of China (61403022); Supported by Beijing Natural Science Foundation (J160003); Supported by the Fundamental Research Funds for the Central Universities (2017JBM030).

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Authors and Affiliations

  1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, China
    Yun Jing, Yingke Liu & Mingkai Bi

Authors

  1. Yun Jing
  2. Yingke Liu
  3. Mingkai Bi

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Correspondence toYun Jing.

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The authors declare that they have no conflict of interest.

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Jing, Y., Liu, Y. & Bi, M. Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency.Cluster Comput 22 (Suppl 1), 545–554 (2019). https://doi.org/10.1007/s10586-017-1292-7

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