Abstract
In the article, a new tool for the assessment of suppliers’ performance is proposed. This tool uses a probabilistic fuzzy approach based on the assessment of a delivery process. The approach uses a probabilistic fuzzy system of MISO type, in which the knowledge base is described as fuzzy if-then rules with the probabilities of fuzzy events in the antecedents and consequents of rules at the same time. The system identification with limiting the number of elementary rules based on a measure of the minimal support of rules is presented. Various cases of suppliers assessments in a real-life company are illustrated by the analysis of the probabilistic fuzzy knowledge base.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Skowronek, C., Sarjusz-Wolski, Z.: Logistics in the Enterprise. edn. III changed, Polskie Wydawnictwo Ekonomiczne, Warszawa (2003). (in Polish)
Zeydan, M., Colpan, C., Cobanoglu, C.: A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach. Int. J. Prod. Res. 47, 4327–4349 (2011)
Aberdeen Group: The Supplier Performance Measurement Benchmark Report, September 2005
Merigó, J.M.: Fuzzy multi-person decision making with fuzzy probabilistic aggregation operators. Int. J. Fuzzy Syst. 13(3), 163–174 (2011)
Yan, L., Ma, Z.M.: A Fuzzy probabilistic relational database model and algebra. Int. J. Fuzzy Syst. 15(1), 244–253 (2013)
Chen, C., Xiao, T.: Probabilistic fuzzy control of mobile robots for range sensor based reactive navigation. Intell. Control Autom. 2, 77–85 (2011)
Yager, R.R., Filev, D.: Using dempster-shafer structures to provide probabilistic outputs in fuzzy systems modeling. In: Trillas, E., Bonissone, P.P., Magdalena, L., Kacprzyk, J. (eds.) Studies in Fuzziness and Soft Computing. Combining Experimentation and Theory, vol. 271, pp. 301–327. Springer-Verlag, Berlin, Heidelberg (2012)
Amiri, M., Ardeshir, A., Zarandi, M.H.F.: Fuzzy probabilistic expert system for occupational hazard assessment in construction. Saf. Sci. 93, 16–28 (2017)
Meghdadi, A.H., Akbarzadeh-T, M.R.: Probabilistic fuzzy logic and probabilistic fuzzy systems. In: Proceedings of 10th IEEE International Conference on Fuzzy Systems, vol. 2, pp. 1127–1130. Melbourne, Australia (2001)
Tang, M., Chen, X., Hu, W., Yu, W.: Generation of a probabilistic fuzzy rule base by learning from examples. Inf. Sci. 217, 21–30 (2012)
Almeida, R.J., Verbeek, N., Kaymak, U., Sousa, J.M.C.: Probabilistic fuzzy systems as additive fuzzy systems. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems Communications in Computer and Information Science, vol. 442, pp. 567–576 (2014)
Sozhamadevi, N., Sathiyamoorthy, S.: A probabilistic fuzzy inference system for modeling and control of nonlinear process. Arab. J. Sci. Eng. 21 March 2015. https://doi.org/10.1007/s13369-015-1627-8
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Walaszek-Babiszewska, A.: Construction of fuzzy models using probability measures of fuzzy events. In: Proceedings of the 13th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR 2007, pp. 661–666. Szczecin, Poland (2007)
Walaszek-Babiszewska, A.: Fuzzy Modeling in Stochastic Environment; Theory, Knowledge Bases, Examples. LAP LAMBERT Academic Publishing, Saarbrűcken (2011)
Walaszek-Babiszewska, A., Rudnik, K.: Stochastic fuzzy knowledge-based approach to temporal data modeling. In: Pedrycz, W., Chen, S.M. (eds.) New Volume on Time Series Analysis, Modeling and Applications. A Computational Intelligence Perspective, pp. 97–118. Springer, Heidelberg (2013)
Rudnik, K., Pisz, I.: Probabilistic fuzzy approach to evaluation of logistics service effectiveness. Manag. Prod. Eng. Rev. 5(4), 66–75 (2014)
Kuok, C.M., Fu, A.W., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Rec. 17(1), 41–46 (1998)
Rudnik, K.: Inference System with Probabilistic-fuzzy Knowledge Base: Theory, Conception and Application. Oficyna Wydawnicza Politechniki Opolskiej, Opole (2013). (in Polish)
Rudnik, K., Deptuła, A.M.: System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects. Expert Syst. Appl. 42(1718), 6365–6379 (2015)
Dickson, G.W.: An analysis of vendor selection systems and decisions. J. Purch. 2, 5–17 (1966)
Gierulski, W., Luściński, S., Serafin, R.: Probabilistic measures of the mass production of logistics chain. Logistics 4/2015, CD no 2, 3363–3373 (2015). (in Polish)
Yager, R.R.: On a general class of fuzzy connectives. Fuzzy Sets Syst. 4, 235–242 (1980)
Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making Methods and Applications: A State-of-the-Art Survey. Springer-Verlag, New York (1981)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rudnik, K., Serafin, R. (2019). Probabilistic Fuzzy Approach to Assessment of Supplier Based on Delivery Process. In: Burduk, A., Chlebus, E., Nowakowski, T., Tubis, A. (eds) Intelligent Systems in Production Engineering and Maintenance. ISPEM 2018. Advances in Intelligent Systems and Computing, vol 835. Springer, Cham. https://doi.org/10.1007/978-3-319-97490-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-97490-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97489-7
Online ISBN: 978-3-319-97490-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)