A modified tabu search strategy for multiple-response grinding process optimisation

International Journal of Intelligent Systems Technologies and Applications

Volumn 4, Issue 1-2, 2008, Pages 97-122

  Mukherjee, Indrajit ^a   Ray, Pradip Kumar ^b  

^a INDIAN INSTITUTE OF ENGINEERING SCIENCE AND TECHNOLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

ANN; artificial neural network; back propagation algorithm; desirability function; GA; genetic algorithm; grinding; Mahalanobis distance; MD; multiple response, tabu search; TS

Indexed keywords

EID: 80255139782     PISSN: 17408865     EISSN: 17408873     Source Type: Journal    
DOI: 10.1504/IJISTA.2008.016361     Document Type: Article

References (46)

1. Adra, S., Bogdanov, K. and Griffin, I. (2003) ‘Optimization techniques for gas turbine engine control systems’, MSC Dissertation Final Report, Department of Computer Science, University of Sheffield.
2. Brinksmeier, E., Tonshoff, H.K., Czenkusch, C. and Heinzel, C. (1998) ‘Modelling and optimization of grinding process’, Journal of Intelligent Manufacturing, Vol. 9, pp.303–314.
3. Chen, Y.T. and Kumara, S.R.T. (1998) ‘Fuzzy logic and neural network for design of process parameters: a grinding process application’, International Journal of Production Research, Vol. 36, No. 2, pp.395–415.
4. Coit, D.W., Jackson, B.T. and Smith, A.E. (1998) ‘Static neural network process models: considerations and case studies’, International Journal of Production Research, Vol. 36, No. 11, pp.2953–2967.
5. Cvijovic, D. and Klinowski, J. (1995) ‘Taboo search: an approach to the multiple minima problem’, Science, Vol. 267, pp.664–666.
6. Derringer, G.C. (1994) ‘A balancing act: optimizing a product’s properties’, Quality Progress, pp.51–57.
7. Derringer, G.C. and Suich, R. (1980) ‘Simultaneous optimization of several response variables’, Journal of Quality Technology, Vol. 12, No. 4, pp.214–219.
8. de Werra, D. and Hertz, A. (1989) ‘Tabu search techniques’, OR Spectrum, pp.131–141.
9. Feng, C., Wang, X. and Yu, Z. (2002) ‘Neural networks modelling of honing surface roughness parameter defined by ISO 13565’, SIAM Journal of Manufacturing Systems, Vol. 21, No. 8, pp.1–35.
10. Gendreau, M. (2003) Handbook of Metaheuristics, in F. Glover and G.A. Kochenberger (Eds). NY: Kluwer Academic Publisher, pp.40–54.
11. Glover, F. (1989) ‘Tabu search – Part I’, ORSA Journal on Computing, Vol. 1, pp.190–206.
12. Glover, F. (1990a) ‘Tabu search –Part II’, ORSA Journal on Computing, Vol. 2, No. 1 pp 4–32.
13. Glover, F. (1990b) ‘Tabu search: a tutorial’, Interface, Vol. 20, pp.74–94.
14. Glover, F. (1994) ‘Tabu search for nonlinear and parameter optimization (with links to genetic algorithm)’, Discrete Applied Mathematics, Vol. 49, pp.213–255.
15. Glover, F. (1998) ‘Tabu search-wellsprings and challenges’, European Journal of Operations Research, Vol. 106, pp.221–225.
16. Glover, F. and Greenberg, H.J. (1989) ‘New approach for heuristic search: a bilateral linkage with artificial intelligence’, European Journal of Operations Research, Vol. 39, pp.119–130.
17. Glover, F., Kelly, J.P. and Laguna, M. (1995) ‘Genetic algorithm and tabu search: hybrids for optimization’, Computers and Operations Research, Vol. 22, No. 1, pp.111–134.
18. Glover, F. and Laguna, M. (2002) Tabu Search, 6th print, London: Kluwer Academic Publisher.
19. Glover, F. and Marti, R. (2000) ‘Fundamentals of scatter search and path relinking’, Control and Cybernetics, Vol. 29, No. 3, pp.653–684.
20. Goldberg, D.E. (2002) Genetic Algorithms, in Search, Optimization and Machine Learning, Delhi, India: Pearson Education, Inc.
21. Harrington Jr., E.C. (1965) ‘The desirability function’, Industrial Quality Control, pp.494–498.
22. Hauft, R.L. and Hauft, S.E. (1998) Practical Genetic Algorithms, NY: John Wiley & Sons, Inc.
23. Howard, D. and Beale, M. (1998) Neural Network Toolbox: For Use with Matlab; User’s Guide, Version 3, USA: The Mathworks Inc.
24. Jain, R.K., Jain, V.K. and Kalra, P.K. (1999) ‘Modelling of abrasive flow machining process: a neural network approach’, Wear, Vol. 231, pp.242–248.
25. Kim, K.J. and Lin, D.K.J. (2000) ‘Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability functions’, Applied Statistics, Vol. 49, No. 3, pp.311–325.
26. Kwak, J. (2005) ‘Application of taguchi and response surface methodologies for geometric error in surface grinding process’, International Journal of Machine Tools and Manufacturing, Vol. 45, pp.327–334.
27. Lee, C.W. and Shin, Y.C. (2000) ‘Evolutionary modelling and optimization of grinding processes’, International Journal of Production Research, Vol. 38, No. 12, pp.2787–2831.
28. Lokketangen, A. and Glover, F. (1998) ‘Solving zero-one mixed integer programming problems using tabu search’, European Journal of Operations Research, Vol. 106, pp.624–658.
29. Luong, L.H.S. and Spedding, T.A. (1995) ‘A neural-network system for predicting machining behaviour’, Journal of Materials Processing Technology, Vol. 52, pp.585–591.
30. Maesschalck, R., Jouan-Rimbaud, D. and Massart, D.L. (2000) ‘Tutorial-the Mahalanobis distance’, Chemo Metrics and Intelligent Laboratory Systems, Vol. 50, pp.1–18.
31. Mahalanobis, P. (1936) ‘On the generalized distance in statistics’, Proceedings of the Indian National Institute Science, Calcutta, India, Vol. 2, pp.49–55.
32. Mitra, K. and Gopinath, R. (2004) ‘Multiobjective optimization of an industrial grinding operation using elitist nondominated sorted genetic algorithm’, Chemical Engineering Science, Vol. 59, pp.385–396.
33. Montgomery, D.C. (2001) Design and Analysis of Experiment, 5th edition, New York: Wiley & Sons Inc.
34. Montgomery, D.C. and Peck, E.A. (1992) Introduction to Linear Regression Analysis, 2nd edition, New York: John Wiley & Sons Inc.
35. Petri, K.L., Billo, R.E. and Bidanda B. (1998) ‘A neural network process model for abrasive flow machining operations’, Journal of Manufacturing Systems, Vol. 17, No.1, pp.52–64.
36. Rencher, A.C. (1995) Method of Multivariate Analysis, USA: John Wiley & Sons, Inc.
37. Rencher, A.C. (1998) Multivariate Statistical Inference and Applications, New York: John Wiley & Sons, Inc.
38. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) ‘Learning representations by back-propagating errors’, Nature, Vol. 323, No. 9, pp.533–536.
39. Saravanan, R. and Sachithanandam, M. (2001) ‘Genetic algorithm (GA) for multivariable surface grinding process optimization using multi-objective function model’, International Journal of Advance Manufacturing Technology, Vol. 17, pp.330–338.
40. Sathyanarayanan, G., Lin, I.J. and Chen, M. (1992) ‘Neural network modelling and multiobjective optimization of creep feed grinding of superalloys’, International Journal of Production Research, Vol. 30, No. 10, pp.2421–2438.
41. Shaji, S. and Radhakrisnan, V. (2003) ‘Analysis of process parameters in surface grinding with graphite as lubricant based on taguchi method’, Journal of Material Processing Technology, pp.1–9.
42. Shin, Y.C. and Vishnupad, P. (1996) ‘Neuro-fuzzy control of complex manufacturing processes’, International Journal of Production Research, Vol. 34, No. 12, pp.3291–3309.
43. Shunta, J.P. (1995) Achieving World Class Manufacturing Through Process Control, Englewood Cliffs, NJ: Prentice Hall PTR.
44. Tao, L. (2004) Research Incubator: Combinatorial Optimization, A Technical Report, Pace University.
45. Timm, N.H. (2002) Applied Multivariate Analysis, Springer Text in Statistics, Springer-Verlag Newyork Inc.
46. Wasserman, P.D. (1993) Advanced Methods in Neural Computing, NY: Van Nostrand Reinhold.