An Algorithm Research  for Supply Chain Management Optimization Model

Ruomeng Kong; Chengjiang Yin

doi:10.14569/IJACSA.2014.050522

Research Article | Open Access |

An Algorithm Research for Supply Chain Management Optimization Model

Author 1: Ruomeng Kong Author 2: Chengjiang Yin

International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 5, No. 5 · Published 2014

DOI: https://doi.org/10.14569/IJACSA.2014.050522

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Abstract

In this paper, we consider the extended linear complementarity problem on supply chain management optimization model. We first give a global error bound for the extended linear complementarity problem, and then propose a new type of algorithm based on the error bound estimation. Both the global and quadratic rate of convergence are established. These conclusions can be viewed as extensions of previously known results.

Keywords

supply chain management optimization model the extended linear complementarity problem error bound algorithm quadratical convergence

How to Cite this Article

Kong, R., & Yin, C. (2014). An Algorithm Research for Supply Chain Management Optimization Model. International Journal of Advanced Computer Science and Applications, 5(5). https://doi.org/10.14569/IJACSA.2014.050522

Kong, Ruomeng, and Chengjiang Yin. "An Algorithm Research for Supply Chain Management Optimization Model." International Journal of Advanced Computer Science and Applications, vol. 5, no. 5, 2014, https://doi.org/10.14569/IJACSA.2014.050522.

@article{Kong2014,
  title     = {An Algorithm Research  for Supply Chain Management Optimization Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {5},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Ruomeng Kong and Chengjiang Yin},
  doi       = {10.14569/IJACSA.2014.050522},
  url       = {https://doi.org/10.14569/IJACSA.2014.050522}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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