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STUDY ON MARITIME LOGISTICS WAREHOUSING CENTER MODEL AND PRECISION MARKETING STRATEGY OPTIMIZATION BASED ON FUZZY METHOD AND NEURAL NETWORK MODEL

Abstract

The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.

Keywords:

Maritime logistics warehousing center mode, Precision marketing strategy optimization, Fuzzy method, Neural network model, Polarity reversal

Details

Issue
Vol. 24 No. S2(94) (2017)
Section
Latest Articles
Published
13-09-2017
DOI:
https://doi.org/10.1515/pomr-2017-0061
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Kefeng Xiao

    Economics and Management School, Wuhan University
  • Xiaolan Hu

    Economics and Management School, Wuhan University

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