Skip to main content

Modeling Supply Chain Complexity Using a Distributed Multi-objective Genetic Algorithm

  • Conference paper
Computational Science and Its Applications - ICCSA 2006 (ICCSA 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3980))

Included in the following conference series:

Abstract

The aim of this paper is to use a Distributed Multi-objective Genetic Algorithm (DMOGA) to model and solve a three Sub-chains model within the supply chain (SC) problem for optimality. It is widely accepted that all SC problems are characterized by decisions that can be conflicting by nature, distributed, and constrained. Modeling these complex problems using multiples objectives, constrained satisfaction, and distribution algorithms gives the decision maker a set of optimal or near-optimal solutions from which to choose. This paper discusses some literature in SC optimization, proposes the use of the DMOGA to solve for optimality in SC optimization problems, and provides the implementation of the DMOGA to a simulated hypothetical SC problem having three Sub-chains. It is then followed by a discussion on the algorithm’s performance based on simulation results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Harrison, T.P., Lee, H.L., Neale, J.J.: The Practice of Supply Chain Management. Kluwer Academic Publishing, Dordrecht (2003)

    MATH  Google Scholar 

  2. Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning, and Operations. Prentice Hall College, Englewood Cliffs (2001)

    Google Scholar 

  3. Cohen, M.A., Lee, H.L.: Resource Deployment Analysis of Global Manufacturing and Distribution Networks. International Journal of Manufacturing and Operations Management 2, 81–104 (1989)

    Google Scholar 

  4. Handfield, R.B., Nichols, E.L.: Introduction to Supply Chain Management. Prentice Hall, Upper Saddle River (1999)

    Google Scholar 

  5. Truong, T.H., Azadivar, F.: Simulation based optimization for supply chain configuration design. Presented at the Winter Simulation Conference, Piscataway, NJ (2003)

    Google Scholar 

  6. Elmahi, I., Merzouk, S., Grunder, O., El Moudni, A.: A Genetic Algorithm Approach for the Batches Delivery Optimization in a Supply Chain: case of multiple vehicle. In: Presented at The 2004 IEEE International Conference on Networking, Sensing and Control (ICNSC 2004), Taiwan (2004)

    Google Scholar 

  7. Joines, J.A., Kupta, D., Gokce, M.A., King, R.E., K.M.G.: Supply Chain Multi-Objective Simulation Optimization. Presented at the 2002 Winter Simulation Conference (2002)

    Google Scholar 

  8. Swaminathan, J.M.: Quantitative Analysis of Emerging Practices in Supply Chains. Carnegie Mellon University, Pittsburg (1996)

    Google Scholar 

  9. Whitley, D.: A Genetic Algorithm Tutorial. Statistics and Computing 4, 65–85 (1994)

    Article  Google Scholar 

  10. Davis, L.: Handbook on Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  11. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  12. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Al-Mutawah, K., Lee, V., Cheung, Y. (2006). Modeling Supply Chain Complexity Using a Distributed Multi-objective Genetic Algorithm. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_62

Download citation

  • DOI: https://doi.org/10.1007/11751540_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34070-6

  • Online ISBN: 978-3-540-34071-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics