Evolutionary metamodelling of discrete-event simulation models

20th European Modeling and Simulation Symposium, EMSS 2008

Volumn , Issue , 2008, Pages 87-94

  Can, Birkan ^a   Murphy, Gearoid ^a   Heavey, Cathal ^b  

^a UNIVERSITY OF LIMERICK   (Ireland)

^b NONE

Author keywords

Buffer allocation problem; Decision support; Genetic programming; Metamodelling; Neural networks; Simulation optimisation; Symbolic regression

Indexed keywords

BUFFER ALLOCATION; DECISION SUPPORTS; META-MODELLING; SIMULATION OPTIMISATION; SYMBOLIC REGRESSION;

DECISION SUPPORT SYSTEMS; GENETIC PROGRAMMING; NEURAL NETWORKS; REGRESSION ANALYSIS;

COMPUTER SIMULATION;

EID: 84871560340     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Afonin, P., V. Derjabkina, A. Kozhukhova and O. Lamskova (2007). "The design and analysis of complex system neural network metamodels." Optical Memory \& Neural Networks 16(3): 154-158.
2. Altiparmak, F., B. Dengiz and A. A. Bulgak (2007). "Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach." Applied Soft Computing 7(3): 946-956.
3. Andijani, A. A. and M. Anwarul (1997). "Manufacturing blocking discipline: A multicriterion approach for buffer allocations." International Journal of Production Economics 51(3): 155-163.
4. Batmaz, I. and S. Tunali (2002). "Second-order experimental designs for simulation metamodeling." Simulation-Transactions of the Society for Modeling and Simulation International 78(12): 699-715.
5. Biles, W. E., J. P. C. Kleijnen, W. C. M. van Beers and I. A. van Nieuwenhuyse (2007). Kriging metamodeling in constrained simulation optimization: an explorative study Kriging metamodeling in constrained simulation optimization: an explorative study. Simulation Conference, 2007 Winter.
6. Bulgak, A. A. (2006). "Analysis and design of split and merge unpaced assembly systems by metamodelling and stochastic search." International Journal of Production Research 44: 4067-4080.
7. Castillo, F., A. Kordon, J. Sweeney and W. Zirk (2005). Using Genetic Programming in Industrial Statistical Model Building. Genetic Programming Theory and Practice II, Springer US: 31-48.
8. Chambers, M. and C. A. Mount-Campbell (2002). "Process optimization via neural network metamodeling." International Journal of Production Economics 79(2): 93-100.
9. Conway, R., W. Maxwell, J. O. McClain and L. J. Thomas (1988). "The Role of Work-in Progress Inventory in Serial Production Lines." Operations Research 36(2): 229.
10. De Almeida, D. and P. Kellert (2000). "An Analytical Queueing Network Model for Flexible Manufacturing Systems with a Discrete Handling Device and Transfer Blockings." International Journal of Flexible Manufacturing Systems 12(1): 25-57.
11. Diamantidis, A. C. and C. T. Papadopoulos (2004). "A dynamic programming algorithm for the buffer allocation problem in homogeneous asymptotically reliable serial production lines." Mathematical Problems in Engineering 2004(3): 209-223.
12. Dolgui, A., A. Eremeev and V. Sigaev (2007). "HBBA: hybrid algorithm for buffer allocation in tandem production lines." Journal of Intelligent Manufacturing 18(3): 411-420.
13. Duffy, J. and J. Warnick (1999). Using Symbolic Regression to Infer Strategies from Experimental Data, Society for Computational Economics.
14. Durieux, S. and H. Pierreval (2004). "Regression metamodeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource." International Journal of Production Economics 89(1): 21-30.
15. Fu, M. C. (2002). Optimization for discrete-event simulation: Theory and practice. System Simulation and Scientific Computing. Z. J. Chen. Beijing, Int Acad Publ Beijing World Publ Corp: 50-50.
16. Jin, R., W. Chen and T. W. Simpson (2001). "Comparative studies of metamodelling techniques under multiple modelling criteria." Structural and Multidisciplinary Optimization 23(1): 1-13.
17. Kim, S. and H. J. Lee (2001). "Allocation of buffer capacity to minimize average work-inprocess." Production Planning and Control 12: 706-716.
18. Korns, M. (2007). Large-Scale, Time-Constrained Symbolic Regression. Genetic Programming Theory and Practice IV: 299-314.
19. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Massachusetts, The MIT Press.
20. Li, G., S. Azarm, A. Farhang-Mehr and A. R. Diaz (2006). "Approximation of multiresponse deterministic engineering simulations: a dependent metamodeling approach." Structural and Multidisciplinary Optimization 31(4): 260-269.
21. Luke, S. and L. Spector (1997). A Comparison of Crossover and Mutation in Genetic Programming. Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA.
22. Moore, J. H., N. Barney and B. C. White (2008). Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitionercomputer Interaction. Genetic Programming Theory and Practice V, Springer Berlin / Heidelberg: 69-85.
23. Murphy, G. and C. Ryan (2008). A Simple Powerful Constraint for Genetic Programming. Genetic Programming, Springer Berlin / Heidelberg: 146-157.
24. Nahas, N., D. Ait-Kadi and M. Nourelfath (2006). "A new approach for buffer allocation in unreliable production lines." International Journal of Production Economics 103(2): 873-881.
25. Noguera, J. H. and E. F. Watson (2006). "Response surface analysis of a multi-product batch processing facility using a simulation metamodel." International Journal of Production Economics 102(2): 333-343.
26. Papadopoulos, H. T., C. Heavey and J. Browne (1993). Queuing Theory, Springer.
27. Person, A., H. Grimm and A. Ng (2008). A Metamodel- Assisted Steady-State Evolution Strategy for Simulation-Based Optimization. Trends in Intelligent Systems and Computer Engineering: 1-13.
28. Rumelhart, D. E., G. E. Hinton and R. J. Williams (1986). "Learning representations by backpropagating errors." Nature 323(6088): 533-536.
29. Simpson, T. W., J. D. Peplinski, P. N. Koch and J. K. Allen (2001). "Metamodels for computerbased engineering design: survey and recommendations." Engineering with Computers 17(2): 129-150.
30. Tekin, E. and I. Sabuncuoglu (2004). "Simulation optimization: A comprehensive review on theory and applications." IIE Transactions 36(11): 1067-1081.
31. Tetko, I. V., D. J. L. Alexander and I. Luik (1995). "Neural Network Studies. 1. Comparison of Overfitting and Overtraining." Chemical Information and Modeling 35(5): 826-833.
32. Vidalis, M. I., C. T. Papadopoulos and C. Heavey (2005). "On the workload and 'phaseload' allocation problems of short reliable production lines with finite buffers." Comput. Ind. Eng. 48(4): 825-837.
33. Wang, G. G. and S. Shan (2007). "Review of metamodeling techniques in support of engineering design optimization." Journal of Mechanical Design 129(4): 370-380.
34. William, L. C., R. Jerzy and A. T. Bahill (2001). "System design is an NP-complete problem: Correspondence." Syst. Eng. 4(3): 222-229.
35. Yang, F., B. Ankenman and B. L. Nelson (2007). "Efficient generation of cycle time-throughput curves through simulation and metamodeling." Naval Research Logistics 54(1): 78-93.