Optimization of Customer Satisfaction Using an Improved Simulation Annealing

Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)

Introduction

Chapters 3 and 4 discussed using the fuzzy AHP approaches to determine the importance weights of customer requirements of new product designs. Chapters 5 to 8 discussed using fuzzy and evolutionary methods to generate models which represent relationships between customer requirements and the design attributes of new products. Based on the models and the importance weights for customer requirements, the optimization problems for maximizing overall customer satisfaction for the new products can be formulated. However, nonlinearity exists between customer requirements and design attributes of new products. Therefore, these optimization problems have multiple optima arising from local optima, and cannot be handled by classical optimization methods such as gradient-based methods. This chapter discusses a computational intelligence optimization method, namely simulated annealing (SA), to solve these multi-optima problems for new product design.

Keywords

Simulated Annealing Product Design Customer Satisfaction Candidate Solution Orthogonal Array 
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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References

  1. Aydin, M.E., Fogarty, T.C.: A distributed evolutionary simulated annealing for combinatorial optimisation problems. Journal of Heuristics 10(3), 269–292 (2004)CrossRefGoogle Scholar
  2. Box, G.E.P., Hunter, W.G., Hunter, J.S.: Statistics for Experimenters. John Wiley (1978)Google Scholar
  3. Bryne, D.M., Taguchi, S.: The Taguchi approach to parameter design. ASQC Quality Congress Transaction, 168 (1986)Google Scholar
  4. Cerny, V.: Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications 45, 41–51 (1985)MathSciNetMATHCrossRefGoogle Scholar
  5. Chatterjee, S., Carrera, C., Lynch, L.A.: Genetic algorithms and travelling salesman problems. European Journal of Operational Research 93, 490–510 (1995)CrossRefGoogle Scholar
  6. Chan, K.Y., Kwong, C.K., Luo, X.G.: Improved orthogonal array based simulated annealing for design optimization. Expert Systems with Applications 36, 7379–7389 (2009)CrossRefGoogle Scholar
  7. Davidor, Y.: Epistasis variance: a viewpoint on GA-hardness. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo (1991)Google Scholar
  8. Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Transactions of Neural Networks 5(1), 3–14 (1994)CrossRefGoogle Scholar
  9. Gong, G., Liu, Y., Qian, M.: An adaptive simulated annealing algorithm. Stochastic Processes and their Applications 94, 95–103 (2001)MathSciNetMATHCrossRefGoogle Scholar
  10. Ho, S.J., Ho, S.Y., Shu, L.S.: OSA: Orthogonal simulated annealing algorithm and its application to designing mixed H2=H ∞  Optimal Controllers. IEEE Transactions on Systems. Man and Cybernetics – Part A: Systems and Humans 34(5), 588–600 (2004a)CrossRefGoogle Scholar
  11. Ho, S.Y., Ho, S.J., Lin, Y.K., Chu, W.C.C.: An orthogonal simulated annealing algorithm for large floorplanning problems. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 12(8), 874–876 (2004b)CrossRefGoogle Scholar
  12. Ho, S.Y., Shu, L.S., Chen, J.H.: Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Transactions on Evolutionary Computation 8(6), 522–541 (2004c)CrossRefGoogle Scholar
  13. Ho, S.J., Shu, L.S., Ho, S.Y.: Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA. IEEE Transactions on Fuzzy Systems 14(3), 421–434 (2006)CrossRefGoogle Scholar
  14. Kim, J.D., Choi, M.S.: Stochastic approach to experimental analysis of cylindrical lapping process. International Journal of Machines Tools Manufacturing 35(1), 51–59 (1995)CrossRefGoogle Scholar
  15. Kirkpatrick, S., Gelatt, J., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetMATHCrossRefGoogle Scholar
  16. Kratica, J., Tosic, D., Filipovic, V., Ljubic, I.: Solving the simple plant location problem by genetic algorithm. RAIRO Operations Research 35, 127–142 (2001)MathSciNetMATHCrossRefGoogle Scholar
  17. Kwong, C.K., Chen, Y., Chan, K.Y.: Integrating perceptual product mapping with QFD for new product design (working paper)Google Scholar
  18. Lin, C.K.Y., Haley, K.B., Sparks, C.: A comparative study of both standard and adaptive versions of threshold accepting and simulated annealing algorithms in three scheduling problems. European Journal of Operational Research 83, 330–346 (1995)MATHCrossRefGoogle Scholar
  19. Lin, Y.H., Tyan, Y.Y., Chang, T.P., Chang, C.Y.: An assessment of optimal mixture for concrete made with recycled concrete aggregates. Cement and Concrete Research 34, 1373–1380 (2004)CrossRefGoogle Scholar
  20. Locatelli, M.: Simulated annealing algorithms for continuous global optimization: convergence conditions. Journal of Optimization Theory and Applications 104(1), 121–133 (2000)MathSciNetMATHCrossRefGoogle Scholar
  21. Mohan, N.S., Ramachandra, A., Kulkarni, S.M.: Influence of process parameters on cutting force and torque during drilling of glass fiber polyester reinforced composites. Composite Structures 71, 407–413 (2005)CrossRefGoogle Scholar
  22. Moilanen, A.: Simulated evolutionary optimization and local search: introduction and application to tree search. Cladistics 17, 12–15 (2001)CrossRefGoogle Scholar
  23. Phadke, M.S.: Quality engineering using robust design. Prentic Hall, New York (1987)Google Scholar
  24. Reeves, C.R., Wright, C.C.: An experimental design perspective on genetic algorithms. Foundation of Genetic Algorithms 3, 7–22 (1995)Google Scholar
  25. Reeves, C.R., Wright, C.C.: Epistasis in Genetic Algorithms: An Experimental Design Perspective. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 217–224 (1995)Google Scholar
  26. Reeves, C.R.: Predictive measures for problem difficulty. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 1, pp. 736–742 (1999)Google Scholar
  27. Ruiz-Torres, A.J., Enscore, E.E., Barton, R.R.: Simulated annealing heuristics for the average flow-time and the number of Tardy jobs bi-criteria identical parallel machine Problem. Computers Industry Engineering 33, 257–260 (1997)CrossRefGoogle Scholar
  28. Shu, L.S., Ho, S.Y., Ho, S.J.: A novel orthogonal simulated annealing algorithm for optimization of electromagnetic problems. IEEE Transactions on Magnetics 40(4), 1791–1795 (2004)CrossRefGoogle Scholar
  29. Szu, H., Hartley, R.: Fast simulated annealing. Physical Letters 122, 157–162 (1987)CrossRefGoogle Scholar
  30. Szu, H.: Nonconvex optimization by fast simulated annealing. Proceedings of the IEEE 75(11), 1538–1540 (1987)CrossRefGoogle Scholar
  31. Tsallis, C., Stariolo, D.A.: Generalized simulated annealing. Physica A 233, 395–406 (1996)CrossRefGoogle Scholar
  32. Unal, R., Stanley, D.O., Joyner, C.R.: Propulsion system design optimization using the Taguchi Method. IEEE Transactions on Engineering Management 40(3), 315–322 (1993)CrossRefGoogle Scholar
  33. Van Laarhoven, P.J.M., Aarts, E.H., Lenstra, J.K.: Job shop scheduling by simulated annealing. Operations Research 40(1), 113–125 (1992)MathSciNetMATHCrossRefGoogle Scholar
  34. Van Laarhoven, P.J.M., Aarts, E.H.L.: Simulated Annealing: Theory and Applications. D. Reidel Publishing Co. (1987)Google Scholar
  35. Vaessens, R.J.M., Aarts, E.H.L., Lenstra, J.K.: A local search template. In: Proceedings of Parallel Problem-Solving from Nature, pp. 65–74 (1992)Google Scholar
  36. Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Building better test function. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 239–246 (1995)Google Scholar
  37. Wong, S.Y.W.: Hybrid simulated annealing/genetic algorithm approach to short term hydro-thermal scheduling with multiple thermal plants. Electric Power Energy Systems 23, 565–575 (2001)CrossRefGoogle Scholar
  38. Yao, X., Lin, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)MathSciNetCrossRefGoogle Scholar
  39. Yao, X.: Simulated annealing with extended neighbourhood. International Journal of Computer Mathematics 40, 169–189 (1991)MATHCrossRefGoogle Scholar
  40. Yao, X.: Comparison of different neighbourhood sizes in simulated annealing. In: Proceedings of 4th Australian Conference on Neural Networks, pp. 216–219 (1993)Google Scholar
  41. Yin, G.Z., Jillie, D.W.: Orthogonal design for process optimization and its application in plasma etching. In: Bendell, A., Disney, J., Pridmore, W.A. (eds.) Taguchi Methods: Applications in World Industry, pp. 181–198. IFS Publications/Springer-Verlag (1989)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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