Evolutionary computing in manufacturing industry: An overview of recent applications

Applied Soft Computing Journal

Volumn 5, Issue 3 SPEC.ISS., 2005, Pages 281-299

  Oduguwa, V ^a   Tiwari, A ^a   Roy, R ^a  

^a CRANFIELD UNIVERSITY   (United Kingdom)

Author keywords

Evolutionary computing; Genetic algorithms; Manufacturing industry

Indexed keywords

GENETIC ALGORITHMS; INDUSTRIAL APPLICATIONS; KNOWLEDGE REPRESENTATION; LARGE SCALE SYSTEMS; OPTIMIZATION; PROBLEM SOLVING; REAL TIME SYSTEMS; STRATEGIC PLANNING;

EVOLUTION STRATEGIES (ES); EVOLUTIONARY COMPUTING; GENETIC PROGRAMMING (GP); MANUFACTURING INDUSTRY;

COMPUTER AIDED MANUFACTURING;

EID: 13844311429     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2004.08.003     Document Type: Article

References (100)

1. D.B. Fogel What is Evolutionary computation? IEEE Spectrum 2000 26 32
2. R. Roy, Adaptive search and the preliminary design of gas turbine blade cooling system, Ph.D. Thesis, University of Plymouth, Plymouth, 1997.
3. V. Oduguwa, R. Roy, Qualitative and quantitative knowledge in engineering system design, Presented at the 17th National Conference on Manufacturing Research, Cardiff University, UK, 2001, pp. 81-86.
4. A. Tettamanzi, and M. Tomassini Soft Computing: Integrating Evolutionary, Neural and Fuzzy System 2001 Springer-Verlag New York
5. K. Deb Optimization for Engineering Design: Algorithms and Examples 1995 Prentice-Hall New Delhi, India
6. K. Deb Multi-objective Optimization using Evolutionary Algorithms 2001 Wiley
7. W. Spendley, G.R. Hext, and F.R. Himsworth Sequential application of simplex designs in optimization and evolutionary operation Technometrics 4 1962 441
8. R. Hooke, and T.A. Jeeves Direct search solution of numerical and statistical problems J. ACM 8 1961 212 229
9. M.J.D. Powell An efficient method for finding the minimum of a function of several variables without calculating derivatives Comput. J. 7 1964 303 307
10. A.L. Cauchy Methode generale pour la resolution des systemes d'equations simultanees Comptes rendus de l'academie des sciences 25 1847 536 538
11. D. Marquardt An algorithm for least squares estimation of non-linear parameters SIAM J. Appl. Math. 11 1963 431 441
12. R. Fletcher, and C.M. Reeves Function minimization by conjugate gradients Comput. J. 7 1964 149 154
13. A. Tiwari, Evolutionary computing techniques for handling variable interaction in engineering design optimisation, Ph.D. Thesis, Cranfield University, Cranfield, UK, 2001.
14. G.E.P. Box Evolutionary operation: a method of increasing industrial productivity Appl. Stat. 6 1957 81 101
15. A.S. Fraser Simulation of genetic systems by automatic digital computers. I. Introduction Aust. J. Biol. Sci. 10 1957 484 491
16. J.H. Holland Outline of a logical theory of adaptive systems J. ACM 9 1962 297 314
17. J.H. Holland Adaptation in Natural and Artificial Systems 1975 University of Michigan Press Ann Arbor, MI
18. J.R. Koza Genetic Programming 1992 MIT Press
19. L.J. Fogel, A.J. Owens, and M.J. Walsh Artificial Intelligence through Simulated Evolution 1966 Wiley New York
20. I. Rechenberg, Cybernetic solution path of an experimental problem, Royal Aircraft Establishment, Library Translation no. 1122, 1965.
21. H.-P. Schwefel Numerical Optimisation of Computer Models 1981 Wiley
22. D.B. Fogel Evolutionary Computation 1995 IEEE Press New York
23. J.S. Chung, and S.M. Hwang Application of genetic algorithm to process optimal design in non-isothermal metal forming J. Mater. Process. Technol. 80-81 1998 136 143
24. K.E. Kinnear Advances in Genetic Programming. 1994 MIT Press Cambridge, MA
25. T. Back, H.-P. Schwefel, Evolutionary computation: an overview, Presented at the Proceedings of the Third IEEE Conference on Evolutionary Computation, Piscataway, NJ, 1996, pp. 20-29.
26. D.H. Wolpert No Free Lunch Theorems for Optimization 1996 IBM Almaden Research Center California
27. H.W. Ray, and J. Szekely Process Optimization with Applications in Metallurgy and Chemical Engineering 1973 Wiley New York
28. R. Bellman Dynamic Programming 1957 Princeton University Press
29. A. Law, and D.W. Kelton Simulation Modeling and Analysis 1991 McGraw-Hill
30. Z. Michalewicz, A survey of constraint handling techniques in evolutionary computation methods, Presented at the Proceedings of the Fourth Annual Conference on EP, Cambridge, MA, 1995, pp. 135-155.
31. M.S. Elred, Optimization Strategies for Complex Engineering Applications, Sandia Sand98-0340.UC-705, February 1998.
32. H.A. Simon The Sciences of the Artificial 1969 MIT Press Cambridge, MA
33. V. Oduguwa, A. Tiwari, R. Roy, Genetic algorithm in process optimisation, Presented at CDROM Proceedings of the Eighth Online World Conference on Soft Computing in Industrial Applications (WSC-8), Finland, 2003.
34. V. Oduguwa, R. Roy, Bilevel optimisation using genetic algorithm, Presented at 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002), Divnomorskoe, Russia, 2002, pp. 322-327.
35. G.J. Klir, and B. Yuan Fuzzy Sets and Fuzzy Logic Theory and Applications 1995 Prentice-Hall New Jersey
36. B. Filipic, B. Sarler, Evolving parameter settings for continuous casting of steel, Presented at the Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing EUFIT'98, Aachen, Germany, 1998, pp. 444-449.
37. B. Sarler, B. Filipic, M. Raudensky, J. Horsky, An interdisciplinary approach towards optimal continuous casting of steel, Presented at Materials Processing in the Computer Age III, Proceedings of TMS, Nashville, Tennessee, TMS, Warrendale, PA, 2000, pp. 27-36.
38. K. Deb, N. Chakraborti, A combined heat transfer and genetic algorithm modeling of an integrated steel plant bloom re-heating furnace, Presented at the Proceedings of the Sixth European Congress on Intelligent Techniques and Soft Computing EUFIT'98, Aachen, Germany, 1998, pp. 439-443.
39. N. Chakraborti, K. Deb, and A. Jha A genetic algorithm based heat transfer analysis of a bloom re-heating furnace Steel Res. 71 2000 396 420
40. N. Chakraborti, R. Kumar, and D. Jain A study of the continuous casting mold using a pareto-converging genetic algorithm Appl. Math. Model. 25 2001 287 297
41. C.A. Santos, J.A. Spim Jr., M.C.F. Ierardi, and A. Garcia The use of artificial intelligence technique for the optimisation of process parameters used in the continuous casting of steel Appl. Math. Model. 26 2002 1077 1092
42. C.A. Conceicao Antonio, and N. Magalhaes Dourado Metal-forming process optimisation by inverse evolutionary search J. Mater. Process. Technol. 121 2002 403 413
43. S. Roy, S. Ghosh, and R. Shivpuri Optimal design of process variables in multi-pass wire drawing by genetic algorithms J. Manuf. Sci. Eng. 118 1996
44. L. Nolle, D.A. Armstrong, J.A. Ware, F. Biegler-Konig, Optimization of Roll Profiles in the Hot Rolling of Wide Steel Strip, Genetic Algorithm in Engineering Systems: Innovations and Applications, 1997, pp. 133-138.
45. D.D. Wang, A.K. Tieu, F.G. de Boer, B. Ma, and W.Y.D. Yuen Towards a heuristic optimum design of rolling schedules for tandem cold rolling mills Eng. Appl. Artif. Intell. 13 2000 397 406
46. J.S. Chung, S.M. Byon, H.J. Kim, S.M. Hwang, Process Optimal Design in Metal Forming by Double-objective Genetic Algorithm, in: Transactions of the NAMRI/SME XXVII, 2000, pp. 51-56.
47. V. Oduguwa, R. Roy, Multi-objective optimisation of rolling rod product design using meta-modelling approach, Presented at the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, 2002, pp. 1164-1171.
48. V. Oduguwa, R. Roy, An integrated design optimisation approach for quantitative and qualitative search space, Presented at the Proceedings of the ASME: 2003 ASME Design Engineering Technical Conference, Chicago, IL, 2003.
49. R. Roy, V. Oduguwa, Multiobjective optimisation of rod design in long product rolling within a quantitative and qualitative search space, Presented at the 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003.
50. V. Oduguwa, R. Roy, D. Farrugia, Fuzzy multi-objective optimisation approach for rod shape design in long product rolling, Presented at IFSA'03, Istanbul, Turkey, 2003.
51. V. Oduguwa, R. Roy, G. Watts, J. Wadsworth, D. Farrugia, Comparison of low-cost response surface models for rod rolling design problems, Presented at the 18th National Conference on Manufacturing Research, Leeds Metropolitan University, UK, 2002, pp. 209-213.
52. T.A. Runkler, E. Gerstorfer, M. Schang, E. Junnemann, and J. Hollatz Modelling and optimisation of a refining process for fibre board production Contr. Eng. Pract. 11 2003 1229 1241
53. A. Santos, and A. Dourado Global optimization of energy and production in process industries: a genetic algorithm application Contr. Eng. Pract. 7 1999 549 554
54. M. Borairi, H. Wang, J.C. Roberts, Dynamic modelling of a paper making process based on bilinear system modelling and genetic neural networks, Presented at UKACC International Conference on Control, 1998, pp. 1277-1282.
55. H. Wang, M. Borairi, J.C. Roberts, H. Xiao, Modelling of a paper making process via genetic neural networks and first principle approaches, Presented at the IEEE International Conference on Intelligent Processing Systems, ICIPS, Beijing, China, 1997, pp. 584-588.
56. P. Pongcharoen, C. Hicks, P.M. Braiden, and D.J. Stewardson Determining optimum genetic algorithm parameters for scheduling the manufacturing and assembly of complex products Int. J. Prod. Econ. 78 2002 311 322
57. K. Wang, T. Lohi, M. Stobbe, and S. Engell A genetic algorithm for online-scheduling of a multiproduct polymer batch plant Comput. Chem. Eng. 24 2000 393 400
58. R.M. Marian, L.H.S. Luong, K. Abhary, Assembly sequence planning and optimisation using genetic algorithm, Applied Soft Computing 2/3F, 2003, pp. 223-253.
59. M. Sakawa, K. Kato, S. Ushiro, and M. Inaoka Operation planning of district heating and cooling plants using genetic algorithms for mixed integer programming Appl. Soft Comput. (ASC) J. 1 2001 139 150
60. M. Yokoyama, and H.W. Lewis III Optimization of the stochastic dynamic production cycling problem by a genetic algorithm Comput. Oper. Res. 30 2003 1831 1849
61. M. Gen, R. Cheng, and S.S. Oren Network design techniques using genetic algorithms Adv. Eng. Software 32 2001 731 744
62. P. Cortes, M.S. Caraballo, J.M. Garcia, J. Larraneta, L. Onieva, GA for planning cable telecommunication networks, Presented at WSC5 Proceedings of the Fifth Online World Conference on Soft Computing Methods in Industrial Applications, 2000, pp. 135-141.
63. K.R. Baker Introduction to Sequencing and Scheduling 1974 Wiley New York, USA
64. Y. Tsujimura, M. Cheng, and M. Gen Improved genetic algorithms for job shop scheduling problems Eng. Des. Autom. 3 1997 133 144
65. H.D. Young, University Physics, Reading, MA, 1992.
66. M. Sakawa, K. Kato, Integer programming through genetic algorithm with double strings based on reference solution updating, Presented at the Proceedings of the 2000 IEEE International Conference on Industrial Electronics, Control and instrumentation, Nagoya, Japan, 2000, pp. 2744-2749.
67. S.D. Harris, L. Elliot, D.B. Ingham, M. Pourkashanian, and C.W. Wilson The optimisation of reaction rate parameters for chemical kinetic modelling of combustion using genetic algorithms Comput. Meth. Appl. Mech. Eng. 190 2000 1065 1090
68. J.K. Rajesh, S.K. Gupta, G.P. Rangaiah, and A.K. Ray Multi-objective optimization of industrial hydrogen plants Chem. Eng. Sci. 56 2001 999 1010
69. M. Grujicic, G. Cao, and B. Gersten Optimization of the chemical vapor deposition process for carbon nanotubes fabrication Appl. Surf. Sci. 199 2002 90 106
70. L. Balland, N. Mouhab, J.M. Cosmao, and L. Estel Kinetic parameter estimation of solvent-free reactions: application to esterification of acetic anhydride by methanol Chem. Eng. Process. 41 2002 395 402
71. M. Emmerich, B. Gro, M. Grotzner, P. Roosen, M. Schutz, A mixed integer evolution strategy for chemical plant optimization, Presented at the Evolutionary Design and Manufacture, ACDM, Plymouth, UK, 2000, pp. 55-67.
72. M. Emmerich, M. Grötzner, and M. Schütz Design of graph-based evolutionary algorithms: a case study for chemical process networks Evol. Comput. 9 2001 329 354
73. J.E. Cuthrell, and L.T. Biegler Simultaneous optimization and solution methods for batch reactor control profiles Comput. Chem. Eng. 13 1989 49 56
74. R. Luus Application of dynamic programming to differential-algebraic process systems Comput. Chem. Eng. 17 1993 373 377
75. C. Floudas A Non-linear and Mixed-integer Optimization: Fundamentals and Applications (Topics in Chemical Engineering) 1995 Oxford University Press New York
76. G. Schernbecker, and K.H. Simmrock Heuristic numeric process synthesis with PROSYN AIChE Symp. Ser. 92 1996 275 278
77. J.M. Douglas Synthesis of separation system flowsheets AIChE J. 41 1995 2522 2536
78. M. Schutz, H.-P. Schwefel, Evolutionary approaches to solve three challenging engineering tasks, Computer Methods in Applied Mechanics and Engineering Application of Artificial Intelligence, vol. Special issue on Evolutionary and Genetic Algorithms in Computational Mechanics and Engineering, 1999.
79. D.C. Montgomery Design and Analysis of Experiments 4th ed. 1997 Wiley
80. H. Watabe, N. Okino, A study on genetic shape design, Presented at the Proceedings of the Fifth International Conference on Genetic Algorithms, University of Illinois, USA, 1993, pp. 445-450.
81. K. Weinert, J. Mehnen, Prestifilippo, Optimal surface reconstruction from digitized point data using CI methods, University of Dortmund, Germany, Technical Report CI-5/97, 1997.
82. M. Higashi, K. Kohji, M. Hosaka, Generation of high-quality curve and surface with smoothly varying curvature, Presented at the Proceedings of EUROGRAPHICS'88 Conference, 1988, pp. 79-92.
83. R. Mussa, R. Roy, G. Jared, Surface optimisation within a CAD/CAM environment using genetic algorithms, Presented at the Third World Conference on Soft Computing in Engineering Design and Manufacturing (WSC3), Cyberspace, 1998.
84. R. Roy, A. Tiwari, O. Munaux, G. Jared, Real-life engineering design optimisation: features and techniques, Presented at the CDROM Proceedings of the Fifth Online World Conference on Soft Computing in Industrial Applications (WSC-5), Finland, 2000.
85. R. Roy, A. Tiwari, A. Braneby, Making evolutionary design optimisation popular in industry: Issues and techniques, Presented at the Sixth Online World Conference on Soft Computing in Industrial Applications (WSC-6), Cyberspace, 2001.
86. G. Liden, and S.K.E. Westberg Fairing of surfaces with optimization techniques using FANGA curves as quality criterion Comput. Aided Des. 25 1993 411 420
87. H. Einar, and E. Skappel FORMELA: a general design and production data system for sculptured products Computer Aided Des. 5 1973 68 76
88. E. Kauffman, and R. Klass Smoothing surfaces using reflection lines for families of splines Comput. Aided Des. 20 1988 73 78
89. R. Roy, G. Jared, J. Grau, T. Buchannan, Adaptive design decision support within CAD environment, Presented at Design Reuse, 1998, pp. 255-262.
90. T. Back, U. Hammel, and H.P. Schwefel Evolutionary computation: comments on the history and current state IEEE Trans. Evol. Comput. 1 1997 3 17
91. M. Szilvasi-Nagy Detecting regions with undesirable curvature Comput. Aided Geom. Des. 14 1997 699 706
92. G. Jared, R. Roy, J. Grau, T. Buchannan, Flexible optimisation within the CAD/CAM environment, Presented at the Proceedings of the CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, Capri, Italy, 1998, pp. 503-508.
93. G.E. Carrillo-Ureta, P.D. Roberts, V.M. Becerra, Genetic algorithm for optimal control of beer fermentation, Presented at the Proceedings of IEEE International Symposium on Intelligent Control, Mexico City, 2001, pp. 391-396.
94. H.S. Cho, W.S. Park, B.W. Choi, and M.C. Leu Determining optimal parameters for stereolithography processes via genetic algorithm J. Manuf. Syst. 19 2000 18 27
95. J.A. Covas, A.G. Cunha, and P. Oliveira An optimization approach to practical problems in plasticating single screw extrusion Polym. Eng. Sci. 39 1999 443 456
96. M. Cerrolaza, and W. Annicchiarico Optimization of finite element bidimensional models: an approach based on genetic algorithms Finite Element Anal. Des. 29 1998 231 257
97. K. Deb, and S. Gulati Design of truss-structures for minimum weight using genetic algorithms Finite Elements Anal. Des. 37 2001 447 465
98. T. Chen, and C. Lin Determination of optimum design spaces for topology optimization Finite Elements Anal. Des. 36 2000 1 16
99. K. Hamza, H. Mahmoud, K. Saitou, Design optimization of N-shaped trusses, Presented at the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, 2002, pp. 1089-1096.
100. L. Barone, L. White, P. Hingston, Designing crushers with a multi-objective evolutionary algorithm, in: Presented at the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, 2002, pp. 995-1002.