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

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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.

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Notes

  1. Results of methods WM, MOGUL-TSK, and NN-MPCG have been obtained using the software KEEL (Alcalá-Fdez et al. 2008).

  2. Really, COR+TUN outperforms the MSEtst values of MOGUL-TSK in three of the data sets, but with a much higher number of rules. With a similar number of rules, MOGUL-TSK has better accuracy than COR+TUN.

References

  • 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 22:909–941

    Article  MATH  Google Scholar 

  • 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

  • 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):985–1032

    Article  MATH  MathSciNet  Google Scholar 

  • 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 1027–1032

  • Boothroyd G (1991) Assembly automation and product design. Marcel Dekker, August

  • Boothroyd G, Dewhurst P, Knight W (1994) Product design for manufacture and assembly. Marcel Dekker, February

  • Cao Q, Patterson JW, Bai X (1999) Reexamination of processing time uncertainty. Eur J Oper Res 164(1):185–194

    Article  Google Scholar 

  • 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 467–488

  • 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):526–537

    Article  Google Scholar 

  • 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):13–29

    Article  Google Scholar 

  • 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 20:433–452

    Article  MATH  Google Scholar 

  • Cheng TCE, Ding Q, Lin BMT (2004) A concise survey of scheduling with time-dependent processing times. Eur J Oper Res 152:1–13

    Article  MATH  MathSciNet  Google Scholar 

  • 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):703–715

    Article  Google Scholar 

  • Cordón O, Herrera F (2001) Hybridizing genetic algorithms with sharing scheme and evolution strategies for designing approximate fuzzy rule-based systems. Fuzzy Sets Syst 118:235–255

    Article  MATH  Google Scholar 

  • 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, Singapore

  • Giornada A, Neri F (1995) Search-intensive concept induction. Evol Comput 3(4):375–416

    Article  Google Scholar 

  • Gupta SK, Nau DS (1995) A systematic approach for analyzing the manufacturability of machined parts. Comput Aided Des 27(5):323–342

    Article  MATH  Google Scholar 

  • Herrmann JW, Chincholkar MM (2001) Reducing throughput time during product design. J Manuf Syst 20(6):416–428

    Article  Google Scholar 

  • Hoffmann F, Nelles O (2001) Genetic programming for model selection of TSK-fuzzy systems. Inf Sci 136(1-4):7–28

    Article  MATH  Google Scholar 

  • Kovacs T (2004) Strength or accuracy: credit assigment in learning classifier systems. Springer, Berlin

  • Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge

  • Kusiak A, He W (1999) Design of components for schedulability. Eur J Oper Res 164(1):185–194

    Google Scholar 

  • 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 201–204

  • 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):1

    Article  MATH  MathSciNet  Google Scholar 

  • Minis I, Herrmann JW, Lam G, Lin E (1999) A generative approach for concurrent manufacturability evaluation and subcontractor selection. J Manuf Syst 18(6):383–395

    Article  Google Scholar 

  • Moller F (1990) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  • Papadakis SE, Theocharis JB (2006) A genetic method for designing TSK models based on objective weighting: application to classification problems. Soft Comput 10(9):805–824

    Article  Google Scholar 

  • Shabtay D, Steiner G (2007) A survey of scheduling with controllable processing times. Eur J Oper Res 155:1643–1666

    MATH  MathSciNet  Google Scholar 

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33

    Article  MATH  MathSciNet  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern SMC 15:116–132

    Google Scholar 

  • Wang L-X, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427

    Article  MathSciNet  Google Scholar 

  • 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):45–55

    Article  Google Scholar 

  • 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):530–537

    Article  Google Scholar 

Download references

Acknowledgments

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

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Authors and Affiliations

  1. Department of Electronics and Computer Science, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Manuel Mucientes, Juan C. Vidal, Alberto Bugarín & Manuel Lama

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  1. Manuel Mucientes
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  2. Juan C. Vidal
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  3. Alberto Bugarín
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  4. Manuel Lama
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Correspondence to Manuel Mucientes.

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Mucientes, M., Vidal, J.C., Bugarín, A. et al. Processing time estimations by variable structure TSK rules learned through genetic programming. Soft Comput 13, 497–509 (2009). https://doi.org/10.1007/s00500-008-0364-2

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