The optimization of tourists shunt based on genetic algorithm

被引:0
|
作者
Ma, Ruimin [1 ,2 ]
Yao, Lifei [1 ,3 ]
Jin, Maozhu [1 ]
Ren, Peiyu [1 ]
机构
[1] Business School, Sichuan University, Chengdu
[2] Civil and Environmental Engineering, University of Washington (Seattle), Seattle, 98195, WA
[3] School of Community Resources and Development, Arizona State University, Phoenix, 85004, AZ
基金
中国国家自然科学基金;
关键词
Genetic algorithms; Open shop; Scenic area; Tourists shunt;
D O I
10.1166/jctn.2015.4662
中图分类号
学科分类号
摘要
Tourism brings huge economic benefits to the scenic area and local residents. At the same time, it also brings some interference to the natural and ecological environment. Especially in the tourism peak, too many tourists travel a scenic spot at a same time, which caused great damage to ecological environment and base infrastructure of some scenic areas. Besides, tourist satisfaction has been affected due to the overcrowding at the same time, therefore it is very necessary that making a reasonable distribution of tourists. In this paper, we established the corresponding mathematical model and the objective functions were to maximize the spatial-temporal utility of scenic area and minimize the congestion of scenic sites. This paper innovatively transformed the problem of tourists visiting scenic area into the open shop scheduling with release and setup time problem (OSSRSTP), and designed a genetic algorithm to optimize the tourists shunt scheme through coding to the scenic spots and transportation resources in the scenic area. Finally, a numerical example is used to illustrate that the optimization effect is remarkable. Copyright © 2015 American Scientific Publishers All rights reserved.
引用
收藏
页码:6244 / 6251
页数:7
相关论文
共 50 条
  • [21] A genetic Algorithm Based on Optimization for Doubly Fed Induction Generator
    Guediri, A.
    Touil, S.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2022, 35 (01):
  • [22] Learning to be selective in genetic-algorithm-based design optimization
    Rasheed, K
    Hirsh, H
    AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 1999, 13 (03): : 157 - 169
  • [23] Quadratic approximation based hybrid genetic algorithm for function optimization
    Deep, Kusum
    Das, Kedar Nath
    APPLIED MATHEMATICS AND COMPUTATION, 2008, 203 (01) : 86 - 98
  • [24] A new penalty based genetic algorithm for constrained optimization problems
    Hu, YB
    Wang, YP
    Guo, FY
    Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 3025 - 3029
  • [25] The establishment of energy consumption optimization model based on genetic algorithm
    Yang, Xiaohong
    Guo, Shuxu
    Yang, HongTao
    2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION AND LOGISTICS, VOLS 1-6, 2008, : 1426 - +
  • [26] Optimization Algorithm Based On Genetic Support Vector Machine Model
    Li, Lan
    Ma, Shaobin
    Zhang, Yun
    2014 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID 2014), VOL 1, 2014, : 307 - 310
  • [27] Path Optimization of Gluing Robot Based on Improved Genetic Algorithm
    Zhang, Yuhang
    Song, Ziling
    Yuan, Jing
    Deng, Zhiyun
    Du, Han
    Li, Lidan
    IEEE ACCESS, 2021, 9 : 124873 - 124886
  • [28] Multidisciplinary electronic package design and optimization methodology based on genetic algorithm
    Suwa, Tohru
    Hadim, Hamid
    IEEE TRANSACTIONS ON ADVANCED PACKAGING, 2007, 30 (03): : 402 - 410
  • [29] An image fusion method with sparse representation based on genetic algorithm optimization
    Zhao X.-J.
    Li Y.-Z.
    Lei S.-Y.
    Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications, 2016, 39 (02): : 73 - 76and87
  • [30] Optimization of Storage and Retrieval Strategies in Warehousing Based on Enhanced Genetic Algorithm
    He, Pengfei
    Zhao, Zhimin
    Zhang, Ying
    Fan, Pengfei
    IEEE ACCESS, 2024, 12 : 105703 - 105715