Skip to main content

Advertisement

SpringerLink
Log in

A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification

  • Research Article - Systems Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Multi-criteria inventory classification (MCIC) is a widely used inventory classification method that groups inventory items with respect to several criteria, in order to facilitate their management. Many researchers have been used several methods to solve MCIC problem. However, some of them are quite complex to understand and are not capable of handling qualitative data which is impractical in today’s manufacturing conditions. In addition, one of the most common problem is that, in most of the existing methods, when a new inventory item is stored in a warehouse, the classification process must be repeated. In this paper, a new hybrid model generated by genetic algorithm (GA), fuzzy c-means (FCM) and adaptive neuro-fuzzy inference system (ANFIS) is proposed for inventory classification. To create this model, three steps are followed up which are optimizing FCM algorithm by using GA, clustering the data set with FCM algorithm and generating the ANFIS classification model. This model does not need to be regenerated to solve the classification problem whenever a new inventory item is introduced. The model is also capable of handling both quantitative and qualitative criteria. The proposed model is applied to a real-life problem. Results of the model are compared with those of artificial neural network (ANN) model. The comparison showed that the proposed model is more successful than the ANN model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kumar, S.; Balaji, K.: Multicriteria inventory ABC classification in an automobile rubber components manufacturing industry. Procedia CIRP 17, 463–468 (2014)

    Article  Google Scholar 

  2. Saedi, S.; Kundakcioglu, O.E.; Henry, A.C.: Mitigating the impact of drug shortages for a healthcare facility: an inventory management approach. Eur. J. Oper. Res. 251(1), 1–17 (2015)

    MathSciNet  MATH  Google Scholar 

  3. Stanford, R.; Martin, W.: Towards normative model for inventory cost management in a generalized ABC classification system. J. Oper. Res. Soc. 58(7), 922–928 (2007)

    Article  MATH  Google Scholar 

  4. Dickie, H.F.: ABC inventory analysis shoots for dollars, not pennies. Fact. Manag. Maint. 109, 92–94 (1951)

    Google Scholar 

  5. Bhattacharya, A.; Sarkar, B.; Mukherjee, S.: Distance-based consensus method for ABC analysis. Int. J. Prod. Res. 45(15), 3405–3420 (2007)

    Article  MATH  Google Scholar 

  6. Partovi, F.Y.; Anandarajan, M.: Classifying inventory using an artificial neural network approach. Comput. Ind. Eng. 41(4), 389–404 (2002)

    Article  Google Scholar 

  7. Torabi, S.; Hatefi, S.; Pay, B.S.: ABC inventory classification in the presence of both quantitative and qualitative criteria. Comput. Ind. Eng. 63(2), 530–537 (2012)

    Article  Google Scholar 

  8. Flores, B.; Whybark, D.: Multiple criteria ABC analysis. Int. J. Oper. Prod. Manag. 6(3), 38–46 (1986)

    Article  Google Scholar 

  9. Chen, T.L.; Cheng, C.H.; Teoh, H.J.: High-order fuzzy time-series based on multiperiod adaptation model for forecasting stock market. Phys. A 387, 876–888 (2008)

    Article  Google Scholar 

  10. Hadavandi, E.; Shavandi, H.; Ghanbari, A.: Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting. Knowl. Based Syst. 23, 800–808 (2010)

    Article  Google Scholar 

  11. Ernst, R.,; Cohen, M.: Operations related groups (ORGs): a clustering procedure for production/inventory systems. Decision Sciences Working Paper, University of Pennsylvania, USA (1986)

  12. Lei, Q.; Chen, J.; Zhou, Q.: Multi criteria inventory classification based on principal components analysis and neural network. Adv. Neural Netw. 3498, 1058–1063 (2005)

    MATH  Google Scholar 

  13. Tsai, C.-Y.; Yeh, S.-W.: A multiple objective particle swarm optimization approach for inventory classification. Int. J. Prod. Econ. 114(2), 656–666 (2008)

    Article  Google Scholar 

  14. Erel, E.; Guvenir, H.: Multicriteria inventory classification using a genetic algorithm. Eur. J. Oper. Res. 105, 29–37 (1998)

    Article  MATH  Google Scholar 

  15. Chen, J.-X.: Multiple criteria ABC inventory classification using two virtual items. Int. J. Prod. Res. 50(6), 1702–1713 (2012)

    Article  Google Scholar 

  16. Hadi-Vencheh, A.; Mohamadghasemi, A.: A fuzzy AHP-DEA approach for multiple criteria ABC inventory classification. Expert Syst. Appl. 38, 3346–3352 (2011)

    Article  Google Scholar 

  17. Kiris, S.: Multi-criteria inventory classification by using a fuzzy analitic network process (ANP) approach. Informatica 24(2), 199–217 (2013)

    MathSciNet  MATH  Google Scholar 

  18. Soylu, B.; Akyol, B.: Multi-criteria inventory classification with reference items. Comput. Ind. Eng. 69, 12–20 (2014)

    Article  Google Scholar 

  19. Ketkar, M.; Vaidya, O.S.: Developing ordering policy based on multiple inventory classification schemes. Procedia Soc. Behav. Sci. 133, 180–188 (2014)

    Article  Google Scholar 

  20. Ghorabaee, M.K.; Zavadskas, E.K.; Olfat, L.; Turksis, Z.: Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3), 435–451 (2015)

    Article  Google Scholar 

  21. Ramanathan, R.: ABC inventory classification with multiple criteria using weighted linear optimization. Comput. Oper. Res. 33(3), 695–700 (2006)

    Article  MATH  Google Scholar 

  22. Ng, W.L.: A simple classifier for multiple criteria ABC analysis. Eur. J. Oper. Res. 177, 344–353 (2007)

    Article  MATH  Google Scholar 

  23. Zhou, P.; Fan, L.: A note on multi-criteria ABC inventory classification using weighted linear optimization. Eur. J. Oper. Res. 182(3), 1488–1491 (2007)

    Article  MATH  Google Scholar 

  24. Chu, C.; Liang, G.S.; Liao, C.: Controlling inventory by combining ABC analysis and fuzzy classification. Comput. Ind. Eng. 55, 841–851 (2008)

    Article  Google Scholar 

  25. Hadi-Vencheh, A.: An improvement to multiple criteria ABC inventory classification. Eur. J. Oper. Res. 201(3), 962–965 (2010)

    Article  MATH  Google Scholar 

  26. Rezaei, J.; Dowlatshahi, S.: A rule-based multi-criteria approach to inventory classification. Int. J. Prod. Res. 48(23), 7107–7126 (2010)

    Article  MATH  Google Scholar 

  27. Park, J.; Bae, H.; Bae, J.: Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification. Comput. Ind. Eng. 76, 40–48 (2014)

    Article  Google Scholar 

  28. Keskin, G.A.; Ozkan, C.: Multiple criteria ABC analysis with FCM clustering. J. Ind. Eng. 2013 (2013). https://doi.org/10.1155/2013/827274

  29. Lolli, F.; Ishizaka, A.; Gamberini, R.: New AHP-based approaches for multi-criteria inventory classification. Int. J. Prod. Econ. 156, 62–74 (2014)

    Article  Google Scholar 

  30. Barak, S.; Dahooie, J.H.; Tichý, T.: Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Syst. Appl. 42(23), 9221–9235 (2015)

    Article  Google Scholar 

  31. Dariane, A.; Azimi, S.: Forecasting streamflow by combination of genetic input selection algorithm and wavelet transforms using ANFIS model. Hydrol. Sci. J. 61(3), 585–600 (2016)

    Article  Google Scholar 

  32. Ho, W.-H.; Chen, J.-X.; Lee, N.; Su, H.-C.: An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm. Expert Syst. Appl. 38(10), 13050–13056 (2011)

    Article  Google Scholar 

  33. Sangaiah, A.K.; Thangavelu, A.K.; Gao, X.Z.; Anbazhagan, N.; Durai, M.S.: An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm. Appl. Soft Comput. 30, 628–635 (2015)

    Article  Google Scholar 

  34. Sarkheyli, A.; MohdZain, A.; Sharif, S.: Robust optimization of ANFIS based on a new modified GA. Neurocomputing 166, 357–366 (2015)

    Article  Google Scholar 

  35. Yuan, Z.; Wang, L.-N.; Ji, X.: Prediction of concrete compressive strength: research on hybrid models genetic based algorithms and ANFIS. Adv. Eng. Softw. 67, 156–163 (2014)

    Article  Google Scholar 

  36. Wali, W.; Al-Shamma, A.; Hassan, K.H.; Cullen, J.: Online genetic-ANFIS temperature control for advanced microwave biodiesel reactor. J. Process Control 22(7), 1256–1272 (2012)

    Article  Google Scholar 

  37. Dogantekin, E.; Avci, E.; Erkus, O.: Automatic RNA virus classification using the entropy-ANFIS method. Digit. Signal Process. 23(4), 1209–1215 (2013)

    Article  MathSciNet  Google Scholar 

  38. Fariman, H.J.; Ahmad, S.A.; Marhaban, M.H.; Jan, M.A.: Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs ANN. Intell. Autom. Soft Comput. 21(4), 559–573 (2015)

    Article  Google Scholar 

  39. Inan, G.; Ubeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods 148(2), 113–121 (2005)

    Article  Google Scholar 

  40. Wu, J.-D.; Kuo, J.-M.: Fault conditions classification of automotive generator using an adaptive neuro-fuzzy inference system. Expert Syst. Appl. 37(12), 7901–7907 (2010)

    Article  Google Scholar 

  41. Wu, R.-N.; Chung, I.-H.; Chang, C.: Classification of partial discharge patterns in GIS using adaptive neuro-fuzzy inference system. J. Chin. Inst. Eng. 37(8), 1043–1054 (2014)

    Article  Google Scholar 

  42. Zapata, J.; Vilar, R.; Ruiz, R.: An adaptive-network-based fuzzy inference system for classification of welding defects. NDT & E Int. 43(3), 191–199 (2010)

    Article  Google Scholar 

  43. Zheng, H.; Jiang, B.; Lu, H.: An adaptive neural-fuzzy inference system (ANFIS) for detection of bruises on Chinese bayberry (Myrica rubra) based on fractal dimension and RGB intensity color. J. Food Eng. 104(4), 663–667 (2011)

    Article  Google Scholar 

  44. Jang, J.: ANFIS: adaptive-network-based fuzzy inference system. IEEE 23(3), 665–685 (1993)

    Google Scholar 

  45. Abdulshahed, A.M.; Longstaff, A.P.; Fletche, S.; Myers, A.: Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera. Appl. Math. Model. 39(7), 1837–1852 (2016)

    Article  Google Scholar 

  46. Selma, B.; Chouragui, S.: Neuro-fuzzy controller to navigate an unmanned vehicle. SpringerPlus 2, 188 (2013)

    Article  Google Scholar 

  47. Zhou, K.; Yang, S.: Exploring the uniform effect of FCM clustering: a data distribution perspective. Knowl. Based Syst. 96, 76–83 (2016)

    Article  Google Scholar 

  48. Wikaisuksakul, S.: A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Appl. Soft Comput. 24, 679–691 (2014)

    Article  Google Scholar 

  49. Alata, M.; Molhim, M.; Ramini, A.: Using GA for optimization of the fuzzy C-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 5, 695–701 (2013)

    Google Scholar 

  50. Mittal, A.; Sharma, S.; Kanungo D.P.: A comparison of ANFIS and ANN for the prediction of peak ground acceleration in Indian Himalayan Region. In: Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS ), pp. 485–495 (2011)

  51. Azeez, D.; Ali, M.A.M.; Gan, K.B.; Saiboon, I.: Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department. SpringerPlus 2, 416 (2013)

    Article  Google Scholar 

  52. Uzuner, S.; Cekmecioglu, D.: Comparison of artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in simulating polygalacturonase production. Bioresorces 11(4), 8676–8685 (2016)

    Google Scholar 

  53. Tang, J.; Zhang, G.; Wanga, Y.; Wang, H.; Liu, F.: A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transp. Res. 51, 29–40 (2015)

    Google Scholar 

  54. Bezdek, J.C.; Ehrlich, R.; Full, W.: FCM: the fuzzy C-means clustering algorithm. Comput. Geosci. 10(3), 191–203 (1984)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Science and Technology, Sakarya University, 54100, Sakarya, Turkey

    Elif İsen

  2. Department of Industrial Engineering, Sakarya University, 54100, Sakarya, Turkey

    Semra Boran

Authors
  1. Elif İsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Semra Boran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elif İsen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

İsen, E., Boran, S. A Novel Approach Based on Combining ANFIS, Genetic Algorithm and Fuzzy c-Means Methods for Multiple Criteria Inventory Classification. Arab J Sci Eng 43, 3229–3239 (2018). https://doi.org/10.1007/s13369-017-2987-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2987-z

Keywords

Search

Navigation