Genetic Algorithm in Process Optimisation Problems

  • Victor Oduguwa
  • Ashutosh Tiwari
  • Rajkumar Roy
Part of the Advances in Soft Computing book series (AINSC, volume 32)


Genetic Algorithm (GA) is generating considerable interest for solving industrial optimisation problems. It is proving robust in delivering global optimal solutions and helping to resolve limitations encountered in traditional methods. However there are fewer GA applications in the process optimisation. This paper presents an overview of recent GA applications in process optimisation. The paper explores the features of process optimisation and critically evaluates how current GA techniques are suited for such complex problems. The survey outlines the current status and trends of GA applications in process related industries. For each industry, the paper describes the general domain problem, common issues, current trends, and the improvements generated by adopting the GA strategy. The paper concludes with an outline of future research directions.


Genetic Algorithm Multiobjective Optimisation Assembly Sequence Planning Process Planning Problem Classical Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    V. Oduguwa, “Rolling System Design Optimisation using Soft Computing Techniques,” EngD Thesis, Cranfield University, Bedford, UK, 2003.Google Scholar
  2. 2.
    H. W. Ray and J. Szekely, Process Optimization with Applications in Metallurgy and Chemical Engineering. New York: John Wiley and Sons, 1973.Google Scholar
  3. 3.
    R. Bellman, Dynamic Programming: Princeton University Press, 1957.Google Scholar
  4. 4.
    G. V. Reklatis, A. K. Sunol, D. W. T. Rippin, and O. Hortaçsu, “Overview of scheduling and planning operations: batch processing systems engineering,” in Batch Processing Systems: Fundamentals and Applications for Chemical Engineering, O. Hortaçsu, Ed.: Springer, 1996.Google Scholar
  5. 5.
    C. Floudas, A, Nonlinear and mixed-integer optimization: fundamentals and applications (topics in chemical engineering). New York: Oxford University Press, 1995.Google Scholar
  6. 6.
    R. Yokoyama and K. Ito, A revised decomposition method for MILP problems and its application to operational planning of thermal storage systems, Journal of Energy Resources Technology, 118: 277–284, 1996.Google Scholar
  7. 7.
    D. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning. Massachusetts: Addison Wesley, 1989.Google Scholar
  8. 8.
    B. Sarler, B. Filipic, M. Raudensky, and J. Horsky, An interdisciplinary approach towards optimal continuous casting of steel. In Materials Processing in the Computer Age III: Proceedings of TMS, pp. 27–36, Nashville, Tennessee. Warrendale, Pennsylvania, 2000.Google Scholar
  9. 9.
    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, Engineering Application of Artificial Intelligence, 13: 397–406, 2000.CrossRefGoogle Scholar
  10. 10.
    N. Chakraborti, R. Kumar, and D. Jain, A study of the continuous casting mold using a pareto-converging genetic algorithm, Applied Mathematical Modelling, 25: 287–297, 2001.CrossRefGoogle Scholar
  11. 11.
    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, Applied Mathematical Modelling, 26: 1077–1092, 2002.CrossRefGoogle Scholar
  12. 12.
    J. S. Chung, S. M. Byon, H. J. Kim, and S. M. Hwang, Process Optimal Design in Metal Forming by Double-Objective Genetic Algorithm, Transactions of the NAMRI/SME, XXVII: 51–56, 2000.Google Scholar
  13. 13.
    V. Oduguwa and R. Roy, An Integrated Design Optimisation approach for Quantitative and Qualitative Search Space, In Proceedings of ASME: 2003 ASME Design Engineering Technical Conference, pp., Chicago, Illinois, 2003 (accepted for publication).Google Scholar
  14. 14.
    N. Chakraborti, K. Deb, and A. Jha, A genetic algorithm based heat transfer analysis of a bloom re-heating furnace, Steel research, 71(10): 396–420, 2000.Google Scholar
  15. 15.
    C. A. Conceicao Antonio and N. Magalhaes Dourado, Metal-forming process optimisation by inverse evolutionary search, Journal of Material Processing Technology, 121: 403–413, 2002.CrossRefGoogle Scholar
  16. 16.
    V. Oduguwa and R. Roy, Multi-Objective Optimisation of Rolling Rod Product Design using Meta-Modelling Approach, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), pp. 1164–1171, New York, 2002.Google Scholar
  17. 17.
    R. Roy and V. Oduguwa, Multiobjective Optimisation of Rod Design in Long Product Rolling within a Quantitative and Qualitative Search Space, In 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, 2003 (accepted for publication).Google Scholar
  18. 18.
    S. Kim and G. J. Vanchtsevanos, An Intelligent approach to integration and control of textile process, Information Sciences, 123: 181–199, 2000.CrossRefGoogle Scholar
  19. 19.
    F. Cus and J. Balic, Optimization of cutting process by GA approach, Robotics and Computer Integrated Manufacturing, 19: 113–121, 2003.CrossRefGoogle Scholar
  20. 20.
    M. L. Fravolini, A. Ficola, and M. La Cava, Optimal operation of the leavening process for a bread-making industrial plant, Journal of Food Engineering, 2003 (submitted for publication).Google Scholar
  21. 21.
    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, International Journal of Production Economics, 78: 311–322, 2002.CrossRefGoogle Scholar
  22. 22.
    R. M. Marian, L. H. S. Luong, and K. Abhary, Assembly sequence planning and optimisation using genetic algorithm, Applied Soft Computing, 2/3F: 223–253, 2003.CrossRefGoogle Scholar
  23. 23.
    M. Sakawa, K. Kato, S. Ushiro, and M. Inaoka, Operation planning of district heating and cooling plants using genetic algorithms for mixed integer programming, Applied Soft Computing (ASC) Journal, 1: 139–150, 2001.CrossRefGoogle Scholar
  24. 24.
    M. Yokoyama and H. W. Lewis III, Optimization of the stochastic dynamic production cycling problem by a genetic algorithm, Computer & Operations Research, 30: 1831–1849, 2003.MathSciNetCrossRefGoogle Scholar
  25. 25.
    P. Cortes, M. S. Caraballo, J. M. Garcia, J. Larraneta, and L. Onieva, GA for Planning Cable Telecommunication Networks, In WSC5 Proceedings of the 5th Online World Conference on Soft Computing Methods in Industrial Applications, pp. 135–141, 2000.Google Scholar
  26. 26.
    J. K. Rajesh, S. K. Gupta, G. P. Rangaiah, and A. K. Ray, Multi-objective optimization of industrial hydrogen plants, Chemical Engineering Science, 56: 999–1010, 2001.CrossRefGoogle Scholar
  27. 27.
    M. Grujicic, G. Cao, and B. Gersten, Optimization of the chemical vapor deposition process for carbon nanotubes fabrication, Applied Surface Science, 199: 90–106, 2002.CrossRefGoogle Scholar
  28. 28.
    S. Roy, S. Ghosh, and R. Shivpuri, Optimal Design of Process Variables in Multi-Pass Wire Drawing by Genetic Algorithms, Journal of Manufacturing Science and Engineering, 118, 1996.Google Scholar
  29. 29.
    D. C. Montgomery, Design and Analysis of Experiments, Fourth ed: John Wiley & Sons, 1997.Google Scholar
  30. 30.
    M. Gendreau, P. Marcotte, and G. Savard, A Hybrid Tabu-Ascent Algorithm for the Linear Bilevel Programming Problem, Journal of Global Optimization, 8(3): 217–233, 1996.MathSciNetCrossRefGoogle Scholar
  31. 31.
    Y. Yin, Genetic Algorithm based approach for bilevel programming models, Journal of Transportation Engineering, 126(2): 115–120, 2000.CrossRefGoogle Scholar
  32. 32.
    V. Oduguwa and R. Roy, Bilevel Optimisation using Genetic Algorithm, In 2002 IEEE International Conference on Artificial Intelligence Systems, (ICAIS 2002), pp. 322–327, Divnomorskoe, Russia, 2002.Google Scholar
  33. 33.
    R. Roy, “Adaptive Search and the Preliminary Design of Gas Turbine Blade Cooling System,” PhD Thesis, University of Plymouth, Plymouth, 1997.Google Scholar
  34. 34.
    V. Oduguwa, R. Roy and D. Farrugia, Fuzzy Multi-Objective Optimisation Approach for Rod Shape Design in Long Product Rolling, Fuzzy Sets and Systems IFSA 2003, 10th International Fuzzy Systems Association World Congress. 2003. Istanbul, Turkey: Springer-Verlag. pp. 636–643.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Victor Oduguwa
    • 1
  • Ashutosh Tiwari
    • 1
  • Rajkumar Roy
    • 1
  1. 1.Department of Enterprise Integration, School of Industrial and Manufacturing ScienceCranfield UniversityCranfield, BedfordUK

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