Application of genetic algorithms to semiconductor supply chain planning

Proceedings of the 2005 International Conference on Algorithmic Mathematics and Computer Science, AMCS'05

Volumn , Issue , 2005, Pages 77-83

  Chidambaram, Rama ^a   Armbruster, Dieter ^b  

^a UNIVERSITY OF MICHIGAN DEARBORN   (United States)

^b ARIZONA STATE UNIVERSITY   (United States)

Author keywords

Genetic algorithm; Linear programming; Supply chain; Throughput time

Indexed keywords

COMPUTERS; DYNAMIC PROGRAMMING; ELECTRIC CONDUCTIVITY; GENETIC ALGORITHMS; LINEAR PROGRAMMING; LINEARIZATION; OPTIMIZATION; PARTICLE SIZE ANALYSIS; SEMICONDUCTOR MATERIALS; SUPPLY CHAIN MANAGEMENT; THROUGHPUT;

MANUFACTURING FACILITIES; NON-LINEAR; PLANNING ALGORITHMS; SEMICONDUCTOR PRODUCTIONS; SEMICONDUCTOR SUPPLY CHAINS; SUPPLY CHAIN PLANNING; THROUGHPUT TIME;

SUPPLY CHAINS;

EID: 60749100378     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. Armbruster, D., Chidambaram, R., Godding, G., G., Kempf, K. and Katzorke, I. (2001). Modeling and analysis of decision flow in complex supply networks, Proceedings of POMS 2001, SaoPaulo, 1106-1114.
2. Hackman, S.T. and R.C. Leachman (1989). A general framework for modeling production, Management Science, Vol. 35, No 4, pp. 478-495.
3. Hung, Y.F. and Leachman, C.R. (1996). A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations, HE Transactions on semiconductor manufacturing, Vol. 9, No 2, pp. 257-272.
4. Kempf, K. (2003). Semiconductor Supply chain modeling test cases, Intel Corporation Personal Communications.