Simulation of a stochastic multi-echelon distribution supply chain under a continuous inventory control policy

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Multi Echelon Distribution System (MEDS) is a multifaceted system focusing on integration of all factors involved in the entire distribution process of finished goods to customers. This paper proposes a simulation framework at the operational level of MEDS. The proposed model includes three echelons, based on discrete-event simulation approach, where the performed operations within our system are depending on several key variables that often seem to have strong interrelationships. It is necessary to simulate such complicated system, in order to understand the whole mechanism, to analyze the interactions between various components and eventually to provide information without decomposing the system. The simulation framework is used to evaluate the performance of the considered system at initial conditions and to compare it with different scenarios generated by simulation running. The study concludes with an analysis of system performance and the finding results according to each scenario.




  • Keywords

    Multi-Echelon Distribution Supply Chain; Operational Level; Simulation Framework; Discrete-Event Simulation.

  • References

      [1] Agus P., “Multi-echelon inventory model for repairable items emergency with lateral transshipment in retail supply chain,” Australian Journal of Basic and Applied Sciences, 2011, p.462-474.

      [2] Bollapragada R., Rao U. S., and Zhang J., “Managing two-stage serial inventory systems under demand and supply uncertainty and customer service level requirements,” IIE Transactions, (2004). 36: 73-85.

      [3] Brady Stephan P., “Multi-echelon inventory impact of varied ordering policy on realized service level,” Thesis in Pennsylvania State University, 1999.

      [4] Cong Guo and Xueping, A multi-echelon inventory system with supplier selection and order allocation under stochastic demand, International Journal of Production Economics, In Press, Accepted Manuscript, Available online 25 January 2014.

      [5] Gurnani H., Akella R., and Lehoczky J., “Supply management in assembly systems with random yield and random demand,” IIE Transactions, 2000, 32: 701-714.

      [6] Min H., and Zhou G., “Supply chain modeling: past, present and future,” Computers and Industrial Engineering, Vol. 43, 2002, p.231-249.

      [7] Niranjan S., “A study of multi-echelon inventory systems with stochastic capacity and intermediate product demand,” Thesis in Wright State University, 2008.

      [8] Ng W., Piplani R., Viswanathan S., “Simulation workbench for analyzing multi-echelon supply chains,” Integrated manufacturing systems, vol. 14, n° 5, 2003, p. 449-457.

      [9] Shang K. H., and Song J., “A closed-form approximation for serial inventory systems and application to system design,” Manufacturing & Service Operations Management, 8(4), 2006, 394-406.

      [10] Tsai S. C., Liu C. H., A simulation-based decision support system for a multi-echelon inventory problem with service level constraints, Computers & Operations Research, 2014.

      [11] Van Beek P., Van Der Vorst J., Beulens M., “Modeling and simulating multi-echelon food systems,” European Journal of Operational Research, 122, 2000, 354-366.

      [12] Wan J., and Zhao C., “Simulation Research on Multi-Echelon Inventory System in Supply Chain Based on Arena,” First International Conference on Informa tion Science and Engineering, 2009, pp.397-400.

      [13] Zare A.G., Abouee-Mehrizi H. and Berman O., Exact analysis of the (R, Q) inventory policy in a two-echelon production-inventory system, Operations Research Letters, 2017.




Article ID: 29963
DOI: 10.14419/ijet.v9i2.29963

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.