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Tourists Initial Optimal Shunt Scheme Using Multi-objective Genetic Algorithm

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Abstract

With the rapid development of tourism, it not only brings great economic benefits, but also causes some problems. Overcrowded visitors reduce tourists satisfaction and bring about negative impact on ecological environment. Designing a reasonable tourists initial shunt scheme can help the management of the scenic area to achieve the goal that balances the economic development and environment protection. In this paper, for the sake of alleviating congestion in scenic area, a tourists optimal shunt scheme is proposed. Instead of defining single optimal objective, the proposed scheme taking two objectives into account, which are minimizing the total load balance degree of the scenic spots and one spot’s load degree. In order to estimate optimal shunt ratios, a multi-objective optimization based on genetic algorithm is used to find the Pareto solution. Then a simulation model is built to investigate and verify the scheme. Finally, a comparison analysis validates the efficiency of the model in mitigating the load of the scenic spots in Jiuzhai Valley.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 71001075 and 61471090), and the Fundamental Research Funds for the Central Universities (Grant Nos. skqy201739 and skzx2017-sb35).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Tao Hu, Maozhu Jin & Peng Ge

  2. National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu, China

    Xia Lei

  3. School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China

    Zhixue Liao

Authors
  1. Tao Hu
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  2. Maozhu Jin
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  3. Xia Lei
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  4. Zhixue Liao
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  5. Peng Ge
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Correspondence to Maozhu Jin.

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Hu, T., Jin, M., Lei, X. et al. Tourists Initial Optimal Shunt Scheme Using Multi-objective Genetic Algorithm. Wireless Pers Commun 102, 3517–3527 (2018). https://doi.org/10.1007/s11277-018-5388-z

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  • DOI: https://doi.org/10.1007/s11277-018-5388-z

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