An Intelligent Algorithm for Modeling and Optimizing Dynamic Supply Chains Complexity
Traditional theories and principles on supply chains management (SCM) have implicitly assumed homogenous cultural environment characteristics across the entire supply chain (SC). In practice, however, such an assumption is too restrictive due to the dynamic and non-homogenous nature of organisational cultural attributes. By extending the evolutionary platform of cultural algorithms, we design an innovative multi-objective optimization model to test the null hypothesis – the SC’s performance is independent of its sub-chains cultural attributes. Simulation results suggest that the null hypothesis cannot be statistically accepted.
KeywordsSupply Chain Supply Chain Management Belief Degree Cultural Algorithm Supply Chain Configuration
Unable to display preview. Download preview PDF.
- 1.Chopra, S., Meindl, P.: Supply Chain Management: Strategy, Planning, and Operations. Prentice Hall College, Englewood Cliffs (2001)Google Scholar
- 3.Truong, T.H., Azadivar, F.: Simulation Based Optimization for Supply Chain Configuration Design. Presented at the Winter Simulation Conference, Piscataway, NJ (2003)Google Scholar
- 4.Joines, J.A., Kupta, D., Gokce, M.A., King, R.E., Guan, K.M.: Supply Chain Multi-Objective Simulation Optimization. Presented at the 2002 Winter Simulation Conference (2002)Google Scholar
- 5.Al-Mutawah, K., Lee, V., Cheung, Y.: Modeling Supply Chain Complexity using a Distributed Multi-objective Genetic Algorithm. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 586–595. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 6.Reynolds, R.G.: An Introduction to Cultural Algorithms. Presented at Third Annual Conference on Evolutionary Programming, River Edge, NJ (1994)Google Scholar
- 8.Reynolds, R.G., Peng, B.: Cultural Algorithms: Modeling of How Cultures Learn to Solve Problems. Presented at 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), Boca Raton, FL, USA (2004)Google Scholar
- 9.Reynolds, R.G., Peng, B.: Knowledge Learning in Dynamic Environments. Presented at IEEE International Congress on Evolutionary Computation (2004)Google Scholar