Metamodels in manufacturing: A review

International Journal of Production Research

Volumn 32, Issue 4, 1994, Pages 787-796

  Yu, B ^a   Popplewell, K ^a  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER SIMULATION; MATHEMATICAL MODELS; PERFORMANCE;

COMPETITION; GLOBAL MARKETS; METAMODELS; SIMULATION MODEL;

MANUFACTURE;

EID: 0028406902     PISSN: 00207543     EISSN: 1366588X     Source Type: Journal    
DOI: 10.1080/00207549408956970     Document Type: Article

References (49)

1. Blanning, W. R., 1975a, Response to Michael, Kleijnen and Permut, Interface, 5, 24-25
2. Blanning, W. R., 1975b, The construction and implementation of metamodels. Simulation, 24-25 (6), 177-184.
3. Buzacott, J. A., and Yao, D. D., 1986, Flexible manufacturing systems: a review of analytical models. Management Science, 32 (7), 890-902.
4. Carter, G., and Ignall, E. J., 1975, Virtual measures: A variance reduction technique for simulation. Management Science, 21 (2), 607-616.
5. Cochran, J. K., and Lin, L., 1992, Assembly line system dynamic behaviour for high priority job order processing. International Journal of Production Research, 30 (7), 1683-1698.
6. Deo. Porta Nova, Acacio, M., and Wilson, J. R., 1989, Estimation of multiresponse simulation metamodels using control variates. Management Science, 35 (11), 1316-1333.
7. Fox, B., 1978, Estimation and Simulation. Management Science, 24 (4), 860-861.
8. Friedman, L. W., and Hershey, H., 1984, Statistical consideration in computer simulation: the state of the art. Journal of Statistical Computing and Simulation, 19, 237-263.
9. Friedman, L. W., and Hershey, H., 1985, Validating the simulation metamodel: some practical approaches. Simulation, 48, 144-146.
10. Friedman, L. W., 1987, Design and analysis of multivariate response simulations: the state of the art. Behavioural Science, 32, 138-148.
11. Friedman, L. W., and Pressman, I., 1988, The metamodel in simulation analysis: can it be trusted? Journal of the Operational Research Society, 39 (10), 939-948.
12. Friedman, L. W., 1989, The multivariate metamodel in queuing system simulation. Computers and Industrial Engineering, 16 (2), 329-337.
13. Geffrion, A. M., 1976, The purpose of mathematical programming is insight, not numbers. Interface, 7 (11), 81-92.
14. Ghosh, J. B., 1988, Evaluating multiprocessor performance: an exercise in metamodeling. Simulation, 51 (2), 70-74.
15. Goldstein, G. B., and Dushane, T., 1976, Repro-modeling applied to the simplification of taradcom computer models. Report No. 12243, US Army Tank/Automotive R&D Command, USA.
16. Heikes, Russel, G., Montgomery, D. C., and Rardin, R. L., 1976, Using common random numbers in simulation experiments-an approach to statistical analysis, Simulation, 27, 81-92.
17. Hopmans, A. G. M. and Kleijnen, J. P. C, 1980, Regression estimation in simulation. Journal of the Operational Research Society, 31 (11), 1033-1038.
18. Ignall, E. J., Kolesar, P., and Walker, W. E., 1978, Using simulation to develop and validate analytic models: some case studies. Operations Research, 26 (2), 237-253.
19. Kleijnen, J. P. C., 1975, A comment on Blanning’s ‘Metamodel for sensitivity analysis: the regression metamodel in simulation’. Interface, 5 (5), 21-23.
20. Kleijnen, J. P. C., 1976, Comparing means and variances of two simulations. Simulation, 26, 87-88.
21. Kleijnen, J. P. C., 1977, Design and analysis simulations: practical statistical techniques. Simulation, 28, 81-90.
22. Kleijnen, J. P. C., 1979a, Regression metamodels for generalising simulation results. IEEE Transactions on Systems, Man and Cybernetics. SMC-9 (2), 93-96.
23. Kleijnen, J. P. C., van den Burg, A. J., and van der Ham, R. Th., 1979b, Generalisation of simulation results. European Journal of Operational Research, 3, 50-64.
24. Kleijnen, J. P. C., 1981, Regression analysis for simulation practitioners. Journal of the Operational Research Society, 32, 35-43.
25. Kleijnen, J. P. C., 1988, Experimental design and regression analysis in simulation: an FMS case study. European Journal of Operational Research, 33, 257-261.
26. Kleijnen, J. P. C., and Rotmans, J., 1992a, Techniques for sensitivity analysis of simulation models: a case study of the C02 Greenhouse effect. Simulation, 58, (6), 410-417.
27. Kleijnen, J. P. C., 1992b, Regression metamodels for simulation with common random numbers: comparison of validation tests and confidence intervals. Management Science, 38 (8), 1167-1185.
28. Kleijnen, J. P. C., 1992c, Simulation and optimization in production planning: a case study. Decision Support Systems, 8.
29. Law, A. M., 1983, Statistical analysis of simulation output data. Operational Research, 31 (6), 983-1029.
30. Law, A. M., 1990, Simulation model’s level of detail determines effectiveness. Industrial Engineering, 10, 16-18.
31. Li L., and Cochran, J. K., 1990a, Estimating simulation metamodel parameters for unexpected shop floor real time events. Computers in Industrial Engineering, 19 (1-4), 62-66.
32. Li L., and Cochran, J. K., 1990b, Metamodels of production line transient behaviour for sudden machine breakdowns. International Journal of Production Research, 28 (10), 1791-1806.
33. Li L., Cochran, J. K., and Sarkis, J., 1992, A metamodel-based decision support system for shop floor production control. Computers in Industry, 18, 155-168.
34. Madu, C. N., 1990, Simulation in manufacturing: a regression metamodel approach. Computers in Industrial Engineering, 18 (3), 381-389.
35. Madu, C. N., and Kuei, C. H., 1992a, Group screening and taguchi design in the optimization of multi-echecon maintenance float simulation metamodels. Computers & Operations Research, 19 (2), 95-105.
36. Madu, C. N., and Chanin, M. N., 1992b, A regression metamodel of a maintenance float problem with Erlang-2 failure distribution. International Journal of Production Research, 30 (4), 871-885.
37. Meidt, G. J., and Bauer, K. W., Jr., 1992, PCRSM: A decision support system for simulation metamodel construction. Simulation, 59 (3), 183-191.
38. Meisel, W. S., and Collins, D. C., 1973, Repro-modelling: an approach to efficient model utilisation and interpretation. IEEE Transactions on Systems, Man and Cybernetics, SMC-3 (4), 349-358.
39. Pierreval, H., 1992, Rule-based simulation metamodels. European Journal of Operational Research, 61, 6-17.
40. Panis, R. P., Meyers, R. H., and Houck, E. G., 1992, Combining regression diagnostics with simulation metamodels. European Journal of Operational Research.
41. Rose, M. R., and Harmsen, R., 1978, Using sensitivity analysis to simplify ecosystem models: A case study. Simulation, 31 (7), 15-26.
42. Rotmans, J., and Vrieze, O. J., 1990, Metamodelling and experimental design: case study of the Greenhouse effect. European Journal of Operational Research, 47, 317-329.
43. Schruben, L., 1982, Detecting initialization bias in simulation output. Operational Research, 30 (3), 569-590.
44. Schruben, L., Sigh, H., and Tierney, L., 1983, Optimal tests for initialization bias simulation output. Operational Research, 31 (6), 1167-1178.
45. Shang, J. S., and Tadikamalla, P. R., 1993, Output maximization of a CIM system: simulation and statistical approach. International Journal of Production Research, 31 (1), 19-41.
46. Starhird, S. A., 1990, A metamodel specification for a tomato processing plant. Journal of the Operational Research Society (UK), 41 (3), 229-240.
47. Starr, P. J., 1991, Integration of simulation and analytical submodels for supporting manufacturing decisions. International Journal of Production Research, 29 (9), 1733-1746.
48. Weeks, J. K., and Fryer, J. S., 1976, A simulation study of operating policies in a hypothetical dual-constrained job shop. Management Science, 22, 1362-1371.
49. Weeks, J. K., and Fryer, J. S., 1977, A methodology for assigning minimum cost due dates. Management Science, 23, 872-881.