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Research on Location Selection Based on Genetic and Simulated Annealing Algorithm

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Contemporary Research on E-business Technology and Strategy (iCETS 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 332))

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Abstract

When dealing with the problem of location selection, one must optimize multiple objective functions. The combination of genetic algorithms and simulated annealing algorithm can improve the solution efficiency and solve the premature convergence problem caused by the genetic algorithm. Using the logistics distribution system as an example, we established a terminal distribution model according to the characteristics and requirements. Based on the mathematic model and the analysis on influencing factors of the transportation costs, we conducted a study on the location selection of distribution center, and subsequently designed and implemented the corresponding genetic and simulated annealing algorithm, which could reduce delivery cost and optimize distribution models.

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References

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Author information

Authors and Affiliations

  1. School of Computer Software, Tianjin University, China

    Wenyuan Tao

  2. School of Computer Science and Technology, Tianjin University, China

    Jiayue Liu

Authors
  1. Wenyuan Tao
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  2. Jiayue Liu
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Editor information

Editors and Affiliations

  1. CeBA Canada, Ottawa, ON, Canada

    Vasil Khachidze

  2. ITSB, PWGSC, Ottawa, ON, Canada

    Tim Wang

  3. Algonquin College, Woodroffe Campus, Ottawa, ON, Canada

    Sohail Siddiqui

  4. Macau University of Science and Technology, Taipa, Macau

    Vincent Liu

  5. Nationan Research Council, Ottawa, ON, Canada

    Sergio Cappuccio

  6. Carleton University, Ottawa, ON, Canda

    Alicia Lim

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© 2012 Springer-Verlag Berlin Heidelberg

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Tao, W., Liu, J. (2012). Research on Location Selection Based on Genetic and Simulated Annealing Algorithm. In: Khachidze, V., Wang, T., Siddiqui, S., Liu, V., Cappuccio, S., Lim, A. (eds) Contemporary Research on E-business Technology and Strategy. iCETS 2012. Communications in Computer and Information Science, vol 332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34447-3_25

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  • DOI: https://doi.org/10.1007/978-3-642-34447-3_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34446-6

  • Online ISBN: 978-3-642-34447-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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