Advertisement

Dynamic Models for Simulation and Optimization of Supply Networks

  • Simone Göttlich
  • Michael Herty

Abstract

We are interested in supply chain event management (SCEM) which is a part of supply chain management. In our considerations a supply chain consists of suppliers, manufacturers, warehouses and stores. Goods or parts can be produced and distributed among different production facilities and stores. Further, different suppliers provide possibly different raw materials or parts in a preliminary stage of production. We call all arising machines, stores, etc. and their interconnections the supply network. Based on description, we understand as SCEM the cost efficient distribution of parts among different locations and at different times in a supply chain. Additionally, SCEM monitors and measures current business processes and predicts future work loads of machines, buffer level of stores and other application dependent events.

Keywords

Supply Chain Continuous Model Discrete Event Simulation Supply Network Discrete Event Simulation Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Armbruster D, Degond P, Ringhofer C (2006) A Model for the Dynamics of large Queuing Networks and Supply Chains. SIAM J. Applied Mathematics 66:896–920CrossRefGoogle Scholar
  2. Armbruster D, Degond P, Ringhofer C (2004) Kinetic and fluid Models for supply chains supporting policy attributes. Submitted, Transport Theory and Statistical PhysicsGoogle Scholar
  3. Baumol WJ (1970) Economic Dynamics. Macmillan, New YorkGoogle Scholar
  4. Bixby RE, Simchi-Levi D, Martin A, Zimmermann U (2004) Mathematics in the Supply Chain. Oberwolfach Reports 1:963–1036CrossRefGoogle Scholar
  5. Daganzo CF (2003) A Theory of Supply Chains. Springer, Berlin Heidelberg New YorkGoogle Scholar
  6. Degond P, Göttlich S, Herty M, Klar A (2007) A network model for supply chains with multiple policies. To appear in SIAM J. Multiscale ModelingGoogle Scholar
  7. Forrester JW (1964) Industrial Dynamics. MIT Press, MassachusettsGoogle Scholar
  8. Fügenschuh A, Göttlich S, Herty M, Klar A, Martin A (2007) A Discrete Optimization Approach to Large Scale Supply Networks based on Partial Differential Equations. Submitted, SIAM J. Scientific ComputingGoogle Scholar
  9. Fügenschuh A, Herty M, Klar A, Martin A (2006) Combinatorial and Continuous Models for the Optimization of Traffic Flows on Networks. SIAM J. Optimization, 16:1155–1176CrossRefGoogle Scholar
  10. Göttlich S, Herty M, Klar A (2005) Network models for supply chains. Communications in Mathematical Sciences 3:545–559Google Scholar
  11. Göttlich S, Herty M, Klar A (2006) Modelling and optimization of supply chains on complex networks. Communications in Mathematical Sciences 4:315–330Google Scholar
  12. ILOG CPLEX Division, 889 Alder Avenue, Suite 200, Incline Village, NV 89451, USA. Information available at URL http://www.cplex.comGoogle Scholar
  13. Koch T (2004) Rapid Mathematical Programming. PhD Thesis, TU BerlinGoogle Scholar
  14. MATLAB Version 7.0. Information available at-www.mathworks.comGoogle Scholar
  15. Newell GF (1993) A simplified theory of kinematic waves in highway traffic. Transportation Research 27B:281–313Google Scholar
  16. Voß S, Woodruff DL (2003) Introduction to Computational Optimization Models for Production Planning in a Supply Chain. Springer, Berlin Heidelberg New YorkGoogle Scholar
  17. Wolsey L, Pochet Y (2006) Production Planning by Mixed Integer Programming. Springer, Berlin Heidelberg New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simone Göttlich
    • 1
  • Michael Herty
    • 1
  1. 1.Department of MathematicsTU KaiserslauternKaiserslauternGermany

Personalised recommendations