Interactive Particle Models in Supply Chain Management
Alexandra L. Wang 1, Jin Wang 2, and Zhaotong Lian 3
1. Department of Economics, Harvard University, Cambridge, MA 02138, USA
2. Department of Mathematics and Computer Science, Valdosta State University, Valdosta, GA 31698, USA
3. Faculty of Business Administration, University of Macau, Taipa, Macau, China
2. Department of Mathematics and Computer Science, Valdosta State University, Valdosta, GA 31698, USA
3. Faculty of Business Administration, University of Macau, Taipa, Macau, China
Abstract—We study on stochastic models of emergent enterprise systems. Our focus is on developing and investigating efficient analytical and numerical methodologies to evaluate the overall performance of emergent enterprise systems. As a multi-stage supply chains, an emergent enterprise system can be modeled as an interacting particle system. Under some reasonable assumptions, the overall performance can be estimated through a homogeneous Markov chain. The stationary distribution of the Markov chain can be derived analytically, as well as the overall system performance can be predicted efficiently and accurately.
Index Terms—supply chain management, interactive particle, emergent enterprise system, Markov chain, multi-stage supply chain
Cite: Alexandra L. Wang, Jin Wang, Zhaotong Lian, "Interactive Particle Models in Supply Chain Management" Journal of Advanced Management Science, Vol. 5, No. 4, pp. 245-249, July 2017. doi: 10.18178/joams.5.4.245-249
Index Terms—supply chain management, interactive particle, emergent enterprise system, Markov chain, multi-stage supply chain
Cite: Alexandra L. Wang, Jin Wang, Zhaotong Lian, "Interactive Particle Models in Supply Chain Management" Journal of Advanced Management Science, Vol. 5, No. 4, pp. 245-249, July 2017. doi: 10.18178/joams.5.4.245-249
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