Advertisement

Threshold-Type Control of Supply Chain Systems with Assembly Operations

  • Dong-Ping SongEmail author
Chapter
Part of the Advances in Industrial Control book series (AIC)

Abstract

This chapter first discusses the system stability of the supply chain system with assembly operations described in  Chap. 6. Numerical examples are given to illustrate the structural characteristics of the optimal raw materials ordering and finished goods assembly policy in both situations with reliable assembly machines and with failure-prone assembly machines. The results verify the analytical results established in  Chap. 6. Then, threshold-type policies are constructed based on the concepts of Kanban, base-stock, and order-up-to-point and the structural properties of the optimal policy for both reliable and unreliable assembly supply chains. The effectiveness of the proposed threshold control policies and their sensitivity to key system parameters are examined through a range of experiments.

Keywords

Supply Chain Optimal Policy Assembly System Assembly Operation Switching Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Avsar, Z.M., Zijm, W.H., Rodoplu, U.: An approximate model for base-stock-controlled assembly systems. IIE Trans. 41(3), 260–274 (2009)CrossRefGoogle Scholar
  2. Benjaafar, S., Elhafsi, M.: Production and inventory control of a single product assemble-to-order system with multiple customer classes. Manage. Sci. 52(12), 1896–1912 (2006)zbMATHCrossRefGoogle Scholar
  3. Benjaafar, S., ElHafsi, M., Lee, C.Y., Zhou, W.H.: Optimal control of an assembly system with multiple stages and multiple demand classes. Oper. Res. 59(2), 522–529 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  4. Chaouiya, C., Liberopoulos, G., Dallery, Y.: The extended Kanban control system for production coordination of assembly manufacturing systems. IIE Trans. 32(10), 999–1012 (2000)Google Scholar
  5. De Kok, A.G., Visschers, J.W.C.H.: Analysis of assembly systems with service level constraints. Int. J. Prod. Econ. 59(1–3), 313–326 (1999)CrossRefGoogle Scholar
  6. Dellaert, N.P., De Kok, A.D.: Push and pull strategies in multi-stage assembly systems. Stat. Neerl. 54(2), 175–189 (2000)zbMATHCrossRefGoogle Scholar
  7. Duenyas, I., Hopp, W.J.: CONWIP assembly with deterministic process and random outages. IIE Trans. 24(4), 97–109 (1992)CrossRefGoogle Scholar
  8. Duenyas, I., Hopp, W.J.: Estimating the throughput of an exponential CONWIP assembly system. Queueing Syst. 14(1–2), 135–157 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  9. Ghrayeb, O., Phojanamongkolkij, N., Tan, B.A.: A hybrid push/pull system in assemble-to-order manufacturing environment. J. Intell. Manuf. 20(4), 379–387 (2009)CrossRefGoogle Scholar
  10. Hazra, J., Schweitzer, P.J., Seidmann, A.: Analyzing closed Kanban-controlled assembly systems by iterative aggregation disaggregation. Comput. Oper. Res. 26(10–11), 1015–1039 (1999)zbMATHCrossRefGoogle Scholar
  11. Huh, W.T., Janakiraman, G.: Base-stock policies in capacitated assembly systems: convexity properties. Nav. Res. Logist. 57(2), 109–118 (2010)MathSciNetzbMATHGoogle Scholar
  12. Ip, W.H., Huang, M., Yung, K.L., Wang, D.W., Wang, X.W.: CONWIP based control of a lamp assembly production line. J. Intell. Manuf. 18(2), 261–271 (2007)CrossRefGoogle Scholar
  13. Khojasteh-Ghamari, Y.: A performance comparison between Kanban and CONWIP controlled assembly systems. J. Intell. Manuf. 20(6), 751–760 (2009)CrossRefGoogle Scholar
  14. Langenhof, L.J.G., Zijm, W.H.M.: An analytical theory of multi-echelon production/distribution systems. Stat. Neerl. 44(3), 149–174 (1990)CrossRefGoogle Scholar
  15. Matta, A., Dallery, Y., Di Mascolo, M.: Analysis of assembly systems controlled with Kanbans. Eur. J. Oper. Res. 166(2), 310–336 (2005)zbMATHCrossRefGoogle Scholar
  16. Ramakrishnan, R., Krishnamurthy, A.: Performance evaluation of a synchronization station with multiple inputs and population constraints. Comput. Oper. Res. 39(3), 560–570 (2012)CrossRefGoogle Scholar
  17. Rosling, K.: Optimal inventory policies for assembly systems under random demands. Oper. Res. 37(4), 565–579 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  18. Sbiti, N., Di Mascolo, M., Bennani, T., Amghar, M.: Modeling and performance evaluation of base-stock controlled assembly systems. In: Gershwin, S.B., Dallery, Y., Papadopoulos, C.T., Smith, J.M. (eds.) Analysis and Modeling of Manufacturing Systems. Kluwer’s International Series in Operations Research and management Science, pp. 307–341. Kluwer, Norwell (2003)CrossRefGoogle Scholar
  19. Topan, E., Avsar, Z.M.: An approximation for Kanban controlled assembly systems. Ann. Oper. Res. 182(1), 133–162 (2011)MathSciNetzbMATHCrossRefGoogle Scholar
  20. Zhao, Y.: Evaluation and optimization of installation base-stock policies in supply chains with compound Poisson demand. Oper. Res. 56(2), 437–452 (2008)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of ManagementUniversity of PlymouthPlymouthUK

Personalised recommendations