• DocumentCode
    2464846
  • Title

    Tackling the Simple Supply Chain Model

  • Author

    Gosling, Timothy ; Tsang, Edward

  • Author_Institution
    Essex Univ., Colchester
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    2179
  • Lastpage
    2186
  • Abstract
    In the future a need will exist, If it does not already, to automate supply chains as trading electronically becomes increasingly important. Using the simple supply chain model (SSCM) allows a supply chain situation to be captured for experimentation. This paper describes efforts to evolve strategies for tackling SSCM specified problems through the use of a strategy framework (SSF) and market simulation system (SMSS). While the SSF provides a basic strategy representation system, the SMSS evolves strategies over multiple supply chain simulations using population based incremental learning with guided mutation. The paper further discuss some of the techniques being used to analyse the resultant data.
  • Keywords
    learning (artificial intelligence); marketing; supply chains; guided mutation; incremental learning; market simulation system; simple supply chain model; strategy framework; strategy representation system; Analytical models; Bridges; Data analysis; Environmental economics; Evolutionary computation; Game theory; Genetic mutations; Robustness; Supply chains; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9487-9
  • Type

    conf

  • DOI
    10.1109/CEC.2006.1688576
  • Filename
    1688576