Development and integration of a reactive real-time decision support system in the aluminum industry

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 6, 2009, Pages 897-905

  Saenz de Ugarte, Benoît ^a   Hajji, Adnène ^a   Pellerin, Robert ^a   Artiba, Abdelhakim ^b  

^a ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

^b High Institute of Mechanic of Paris   (France)

Author keywords

ERP; Genetic algorithm; MES; Real time decision; Rescheduling

Indexed keywords

ALUMINUM INDUSTRY; CONTROLLED ENVIRONMENT; DECISION SUPPORTS; DISRUPTIVE EVENT; ENTERPRISE INFORMA-TION SYSTEMS; ERP; MANUFACTURING ENVIRONMENTS; RE-SCHEDULING PROCESS; REAL-TIME DECISION SUPPORT SYSTEMS; TIME DISCRETE;

ALUMINUM; ALUMINUM PLANTS; ARTIFICIAL INTELLIGENCE; DECISION SUPPORT SYSTEMS; DECISION THEORY; DISCRETE EVENT SIMULATION; ENTERPRISE RESOURCE PLANNING; GENETIC ALGORITHMS; INFORMATION SYSTEMS; RESOURCE ALLOCATION; SIMULATORS; UNCERTAINTY ANALYSIS;

REAL TIME SYSTEMS;

EID: 70449713615     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.10.021     Document Type: Article

References (16)

1. Azadivar F., and Lee Y.-H. Optimization of discrete variable stochastic systems by computer simulation. Mathematics and Computers in Simulation 30 4 (1998) 331-345
2. Baker, J.E., 1985. Adaptive selection methods for genetic algorithms. Paper presented at the Proceedings of the First International Conference on Genetic Algorithms.
3. Carson, Y., Maria, A., 1997. Simulation optimization: Methods and applications. Paper presented at the Proceedings of the 1997 Winter Simulation Conference, Atlanta, GA.
4. Chang P.-C., Hsieh J.-C., and Wang C.-Y. Adaptive multi-objective genetic algorithms for scheduling of drilling operation in printed circuit board industry. Applied Soft Computing Journal 7 3 (2007) 800-806
5. Chaudhry S.S., and Luo W. Application of genetic algorithms in production and operations management: a review. International Journal of Production Research 43 19 (2005) 4083-4101
6. Chipperfield, A.J., Fleming, P.J., Fonseca, C.M., 1994. Genetic algorithm tools for control systems engineering. Paper presented at the Proceedings of Adaptive Computing in Engineering Design and Control, Plymouth Engineering Design Center (UK).
7. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning (1989), Addison-Wesley, Boston, MA, USA
8. Iassinovski S., Artiba A., and Fagnart C. A generic production rules-based system for on-line simulation, decision making and discrete process control. International Journal of Production Economics 112 1 (2008) 62-76
9. Kouiss, K., Pierreval, H., 1999. Implementing an on-line simulation in a flexible manufacturing system. Paper presented at the Simulation in Industry'99. 11th European Simulation Symposium 1999. ESS'99.
10. Poon P.W., and Carter J.N. Genetic algorithm crossover operators for ordering applications. Computers and Operations Research 22 1 (1995) 135-147
11. Ranga, P., Dwivedi, S.N., 2003. Research issues in responsive and agile manufacturing. Paper presented at the International Conference on Agile Manufacturing, Advances in Agile Manufacturing, ICAM 2003, Beijing, China.
12. Ruiz R., Carlos Garcia-Diaz J., and Maroto C. Considering scheduling and preventive maintenance in the flowshop sequencing problem. Computers and Operations Research 34 11 (2007) 3314-3330
13. Saad S.M., and Gindy N.N. Handling internal and external disturbances in responsive manufacturing environments. Production Planning and Control 9 8 (1998) 760-770
14. Shin, F., Ram, B., Gupta, A., Yu, X., Menassa, R., 2004. A decision tool for assembly line breakdown action. Paper presented at the 2004 Winter Simulation Conference.
15. Taillard E. Benchmarks for basic scheduling problems. European Journal of Operational Research 64 2 (1993) 278-285
16. Wadhwa S., Saxena A., and Chan F.T.S. Framework for flexibility in dynamic supply chain management. International Journal of Production Research 46 6 (2008) 1373-1404