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
Link To Document