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A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management

  • Conference paper
Simulated Evolution and Learning (SEAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

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Abstract

This paper aims to propose a knowledge-based Fuzzy - GA Decision Support System with performance metrics for better measuring postponement strategies. The Fuzzy - GA approach mainly consists of two stages: knowledge representation and knowledge assimilation. The relevant knowledge of deciding what type of postponement strategies to adopt is encoded as a string with a fuzzy rule set and the corresponding membership functions. The historical data on performance measures forming a combined string is used as the initial population for the knowledge assimilation stage afterwards. GA is then further incorporated to provide an optimal or nearly optimal fuzzy set and membership functions for related performance measures. The originality of this research is that the proposed system is equipped with the ability of assessing the loss caused by discrepancy away from the different supply chain parties, and therefore enabling the identification of the best set of decision variables.

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Author information

Authors and Affiliations

  1. Dept. of Industrial and Systems Engineering, The Hong Kong Polytechnic University Hunghom, Kowloon, Hong Kong

    Cassandra X. H. Tang & Henry C. W. Lau

Authors
  1. Cassandra X. H. Tang
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  2. Henry C. W. Lau
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Tang, C.X.H., Lau, H.C.W. (2008). A Fuzzy-GA Decision Support System for Enhancing Postponement Strategies in Supply Chain Management. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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