Soft Computing

, Volume 13, Issue 5, pp 497–509 | Cite as

Processing time estimations by variable structure TSK rules learned through genetic programming

  • Manuel Mucientes
  • Juan C. Vidal
  • Alberto Bugarín
  • Manuel Lama
Focus

Abstract

Accuracy in processing time estimation of different manufacturing operations is fundamental to get more competitive prices and higher profits in an industry. The manufacturing times of a machine depend on several input variables and, for each class or type of product, a regression function for that machine can be defined. Time estimations are used for implementing production plans. These plans are usually supervised and modified by an expert, so information about the dependencies of processing time with the input variables is also very important. Taking into account both premises (accuracy and simplicity in information extraction), a model based on TSK (Takagi–Sugeno–Kang) fuzzy rules has been used. TSK rules fulfill both requisites: the system has a high accuracy, and the knowledge structure makes explicit the dependencies between time estimations and the input variables. We propose a TSK fuzzy rule model in which the rules have a variable structure in the consequent, as the regression functions can be completely distinct for different machines or, even, for different classes of inputs to the same machine. The methodology to learn the TSK knowledge base is based on genetic programming together with a context-free grammar to restrict the valid structures of the regression functions. The system has been tested with real data coming from five different machines of a wood furniture industry.

Keywords

Genetic programming Context-free grammar TSK fuzzy rules Production planning Processing time estimation Manufacturing industry 

Notes

Acknowledgments

Authors wish to acknowledge Xunta de Galicia and Martínez Otero Contract, S.A. for their financial support under grants PGIDIT06SIN20601PR and PGIDIT04DPI096E.

References

  1. Alcalá R, Alcalá-Fdez J, Casillas J, Cordón O, Herrera F (2007) Local identification of prototypes for genetic learning of accurate tsk fuzzy rule-based systems. Int J Intell Syst 22:909–941MATHCrossRefGoogle Scholar
  2. Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2008) Keel: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput. doi: 10.1007/s00500-008-0323-y
  3. Allahverdi A, Ng CT, Cheng TCE, Kovalyov MY (2008) A survey of scheduling problems with setup times or costs. Eur J Oper Res 187(3):985–1032MATHCrossRefMathSciNetGoogle Scholar
  4. Berlanga FJ, del Jesus MJ, Herrera F (2005) Learning compact fuzzy rule-based classification systems with genetic programming. In: Proceedings of the 4th conference of the European society for fuzzy logic and technology (EUSFLAT), Barcelona (Spain), pp 1027–1032Google Scholar
  5. Boothroyd G (1991) Assembly automation and product design. Marcel Dekker, AugustGoogle Scholar
  6. Boothroyd G, Dewhurst P, Knight W (1994) Product design for manufacture and assembly. Marcel Dekker, FebruaryGoogle Scholar
  7. Cao Q, Patterson JW, Bai X (1999) Reexamination of processing time uncertainty. Eur J Oper Res 164(1):185–194CrossRefGoogle Scholar
  8. Carse B, Fogarty TC, Munro A (1996) Genetic algorithms and soft computing. Studies in fuzziness and soft computing. In: Evolving temporal fuzzy rule-bases for distributed routing control in telecommunication networks, vol 8. Physica-Verlag, Heidelberg, pp 467–488Google Scholar
  9. Casillas J, Cordón O, Herrera F (2002) Cor: A methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules. IEEE Trans Syst Man Cybern B Cybern 32(4):526–537CrossRefGoogle Scholar
  10. Casillas J, Cordón O, del Jesus MJ, Herrera F (2005a) Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. IEEE Trans Fuzzy Syst 13(1):13–29CrossRefGoogle Scholar
  11. Casillas J, Cordón O, Fernández de Viana I, Herrera F (2005b) Learning cooperative linguistic fuzzy rules using the best-worst ant system algorithm. Int J Intell Syst 20:433–452MATHCrossRefGoogle Scholar
  12. Cheng TCE, Ding Q, Lin BMT (2004) A concise survey of scheduling with time-dependent processing times. Eur J Oper Res 152:1–13MATHCrossRefMathSciNetGoogle Scholar
  13. Cordón O, Herrera F (1999) A two-stage evolutionary process for designing TSK fuzzy rule-based systems. IEEE Trans Syst Man Cybern B 29(6):703–715CrossRefGoogle Scholar
  14. Cordón O, Herrera F (2001) Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets Syst 118:235–255MATHCrossRefGoogle Scholar
  15. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. In: Advances in fuzzy systems—applications and theory, vol 19. World Scientific, SingaporeGoogle Scholar
  16. Giornada A, Neri F (1995) Search-intensive concept induction. Evol Comput 3(4):375–416CrossRefGoogle Scholar
  17. Gupta SK, Nau DS (1995) A systematic approach for analyzing the manufacturability of machined parts. Comput Aided Des 27(5):323–342MATHCrossRefGoogle Scholar
  18. Herrmann JW, Chincholkar MM (2001) Reducing throughput time during product design. J Manuf Syst 20(6):416–428CrossRefGoogle Scholar
  19. Hoffmann F, Nelles O (2001) Genetic programming for model selection of TSK-fuzzy systems. Inf Sci 136(1-4):7–28MATHCrossRefGoogle Scholar
  20. Kovacs T (2004) Strength or accuracy: credit assigment in learning classifier systems. Springer, BerlinGoogle Scholar
  21. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeGoogle Scholar
  22. Kusiak A, He W (1999) Design of components for schedulability. Eur J Oper Res 164(1):185–194Google Scholar
  23. Leung KS, Leung Y, So L, Yam KF (1992) Rule learning in expert systems using genetic algorithm: 1, concepts. In: Proceedings of the 2nd international conference on fuzzy logic and neural networks, Iizuka (Japan), pp 201–204Google Scholar
  24. Lin CJ, Xu YJ (2006) The design of TSK-type fuzzy controllers using a new hybrid learning approach. Int J Adapt Contr Signal Process 20(1):1MATHCrossRefMathSciNetGoogle Scholar
  25. Minis I, Herrmann JW, Lam G, Lin E (1999) A generative approach for concurrent manufacturability evaluation and subcontractor selection. J Manuf Syst 18(6):383–395CrossRefGoogle Scholar
  26. Moller F (1990) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533CrossRefGoogle Scholar
  27. Papadakis SE, Theocharis JB (2006) A genetic method for designing TSK models based on objective weighting: application to classification problems. Soft Comput 10(9):805–824CrossRefGoogle Scholar
  28. Shabtay D, Steiner G (2007) A survey of scheduling with controllable processing times. Eur J Oper Res 155:1643–1666MATHMathSciNetGoogle Scholar
  29. Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33MATHCrossRefMathSciNetGoogle Scholar
  30. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern SMC 15:116–132Google Scholar
  31. Wang L-X, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427CrossRefMathSciNetGoogle Scholar
  32. Wong ML, Lam W, Leung KS, Ngan PS, Cheng JCY (2000) Discovering knowledge from medical databases using evolutionary algorithms. IEEE Eng Med Biol Mag 19(4):45–55CrossRefGoogle Scholar
  33. Yen J, Wang L, Gillespie CW (1998) Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Trans Fuzzy Syst 6(4):530–537CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Manuel Mucientes
    • 1
  • Juan C. Vidal
    • 1
  • Alberto Bugarín
    • 1
  • Manuel Lama
    • 1
  1. 1.Department of Electronics and Computer ScienceUniversity of Santiago de CompostelaSantiago de CompostelaSpain

Personalised recommendations