Title :
Tackling the Simple Supply Chain Model
Author :
Gosling, Timothy ; Tsang, Edward
Author_Institution :
Essex Univ., Colchester
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;
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
DOI :
10.1109/CEC.2006.1688576
