Isaac Scientific Publishing

Frontiers in Management Research

Modeling and Optimizing for the Regional Logistics Distribution Systems of Drinking Water Companies Based on the Random Service System Theory

Download PDF (377.9 KB) PP. 30 - 36 Pub. Date: January 20, 2018

DOI: 10.22606/fmr.2018.21004

Author(s)

  • Jia-jing Zhou
    School of Management, University of South China, Hengyang, China
  • Wen-hao Liu
    School of Management, University of South China, Hengyang, China.
  • Zhao-yun Zhang
    School of Management, University of South China, Hengyang, China.
  • Jiang-ying Zhang
    School of Management, University of South China, Hengyang, China.
  • Dong-yang Xin
    School of Management, University of South China, Hengyang, China.
  • Yao-yao Wei*
    School of Management, University of South China, Hengyang, China.

Abstract

Due to the complex service mechanism and the random market requirements, lots of factors influence the regional logistics distribution processes of the drinking water companies. It's hard for those companies to optimize their logistics distribution systems only by using the traditional decision methods. Thus, this paper puts forward an idea so as to optimize the regional logistic distribution system and to establish the logistics information support system based on the theory of Random Service System (RSS). Furthermore, the regional logistics distribution system of “NongFu Spring (Zhejiang)” is taken as a concrete example. Through modeling and simulating of their distribution system, the major factors cause the resource-wasting and the transportation costs can be found and the optimized system is designed.

Keywords

Regional logistics, distribution, theory of random service system (RSS).

References

[1] Qian Yu, Ping Li. The research of the distribution system for NongFu Spring in yibin[J].China-ASEAN EXPO,2013.

[2] Erhan Kutanoglu, Mohit Mahajan. An inventory sharing and allocation method for a multi-location service parts logistics network with time-based service levels[J]. European Journal of Operational Research, pp.194(3), 2007.

[3] Linxing Hu. The reginal distribution of Hangzhou Wahaha and the optimization scheme of logistics transportation[D]. Zhejiang University of Techonology, 2009.

[4] Tian Li. The research of logistics distribution system in beer industry[D]. Jilin University, 2010.

[5] Wei Xu. Analysis on the Regional Logistics Network for Drinking Enterprises and Selection on the Logistics Distribution Model[D].Tianjin University,2006.

[6] Wang J, Jia J. The application of model M/M/C and M/G/K Mode[J].Henan Sciences, 2010.

[7] Budgaga, W., Malensek, M., Pallickara, S., Harvey, N., Breidt, F. J., Pallickara, S. (2016) Predictive analytics using statistical, learning, and ensemble methods to support real-time exploration of discrete event simulations. Future Generation Computer Systems, vol.56, pp. 360-374, 2016.

[8] Booker, M. T, O’Connell, R. J., Desai, B., Duddalwar, V. A. (2016) Quality improvement with discrete event simulation: A primer for radiologists. Journal of the American College of Radiology, vol.13, pp. 417-423, 2016.

[9] Furian, N., O’Sullivan, M., Walker, C., Vossner, S., Neubacher, D. (2015) A conceptual modeling framework for discrete event simulation using hierarchical control structures. Simulation Modelling Practice and Theory, vol.56, pp. 82-96, 2015.

[10] Chandra V, Huang Z, Kumar R. Correspondence - Automated Control Synthesis for an Assembly Line Using Discrete Event System Control Theory[J]. Ieee Transactions on Systems, Man and Cybernetics. Part C, Applications and Reviews: a Publication of the Ieee Systems, Man, and Cybernetics Society, pp.33(2), 2003.

[11] X. Zhu, R. Zhang, F. Chu, Z. He, J. Li. A FlexSim-based Optimization for the Operation Process of Cold-Chain Logistics Distribution Centre[J]. Journal of Applied Research and Technology, pp.270-278, 2014.