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The Robust Optimization in Centralized Supply Chain

Received: 4 May 2016     Published: 5 May 2016
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Abstract

Considering the inaccurate demand forecasting in supply chain, we introduce robust optimization to reduce uncertainty. The method is mainly to modify the probability distribution of the demand, in order to obtain a more accurate demand. A classical model and a corresponding robust model are established in the context of a fixed number of products offered by the supplier. As to calculation, we also propose the fast Fourier transform approach which greatly reduces the amount of computation. Finally, the process of robust optimization and improved algorithm are interpreted by numerical examples. The results show that the expected revenue of the robust model is lower. Because the method is conservative and robust.

Published in Science Journal of Business and Management (Volume 4, Issue 2)
DOI 10.11648/j.sjbm.20160402.16
Page(s) 61-66
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Supply Chain, Robust Optimization, Demand Forecasting Uncertainty, Fast Fourier Transform Approach

References
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  • APA Style

    Li Chenlu. (2016). The Robust Optimization in Centralized Supply Chain. Science Journal of Business and Management, 4(2), 61-66. https://doi.org/10.11648/j.sjbm.20160402.16

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    ACS Style

    Li Chenlu. The Robust Optimization in Centralized Supply Chain. Sci. J. Bus. Manag. 2016, 4(2), 61-66. doi: 10.11648/j.sjbm.20160402.16

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    AMA Style

    Li Chenlu. The Robust Optimization in Centralized Supply Chain. Sci J Bus Manag. 2016;4(2):61-66. doi: 10.11648/j.sjbm.20160402.16

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  • @article{10.11648/j.sjbm.20160402.16,
      author = {Li Chenlu},
      title = {The Robust Optimization in Centralized Supply Chain},
      journal = {Science Journal of Business and Management},
      volume = {4},
      number = {2},
      pages = {61-66},
      doi = {10.11648/j.sjbm.20160402.16},
      url = {https://doi.org/10.11648/j.sjbm.20160402.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjbm.20160402.16},
      abstract = {Considering the inaccurate demand forecasting in supply chain, we introduce robust optimization to reduce uncertainty. The method is mainly to modify the probability distribution of the demand, in order to obtain a more accurate demand. A classical model and a corresponding robust model are established in the context of a fixed number of products offered by the supplier. As to calculation, we also propose the fast Fourier transform approach which greatly reduces the amount of computation. Finally, the process of robust optimization and improved algorithm are interpreted by numerical examples. The results show that the expected revenue of the robust model is lower. Because the method is conservative and robust.},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - The Robust Optimization in Centralized Supply Chain
    AU  - Li Chenlu
    Y1  - 2016/05/05
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sjbm.20160402.16
    DO  - 10.11648/j.sjbm.20160402.16
    T2  - Science Journal of Business and Management
    JF  - Science Journal of Business and Management
    JO  - Science Journal of Business and Management
    SP  - 61
    EP  - 66
    PB  - Science Publishing Group
    SN  - 2331-0634
    UR  - https://doi.org/10.11648/j.sjbm.20160402.16
    AB  - Considering the inaccurate demand forecasting in supply chain, we introduce robust optimization to reduce uncertainty. The method is mainly to modify the probability distribution of the demand, in order to obtain a more accurate demand. A classical model and a corresponding robust model are established in the context of a fixed number of products offered by the supplier. As to calculation, we also propose the fast Fourier transform approach which greatly reduces the amount of computation. Finally, the process of robust optimization and improved algorithm are interpreted by numerical examples. The results show that the expected revenue of the robust model is lower. Because the method is conservative and robust.
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • School of Management, Shanghai University, Shanghai, China

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