Improving the Quality of Transport Management Services with Fuzzy Signatures
Authors: Csaba I. Hencz, István Á. Harmati
Abstract:
Nowadays the significance of road transport is gradually increasing. All transport companies are working in the same external environment where the speed of transport is defined by traffic rules. The main objective is to accelerate the speed of service and it is only dependent on the individual abilities of the managing members. These operational control units make decisions quickly (in a typically experiential and/or intuitive way). For this reason, support for these decisions is an important task. Our goal is to create a decision support model based on fuzzy signatures that can assist the work of operational management automatically. If the model sets parameters properly, the management of transport could be more economical and efficient.
Keywords: Freight transport, decision support, information handling, fuzzy methods.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1132503
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 816References:
[1] L. A. Zadeh, Fuzzy Sets, Information and Control 8 (3) (1965), pp. 338-353.
[2] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning I, Information Science 8 (1975), pp. 199-251.
[3] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning II, Information Science 8 (1975), pp. 301-357.
[4] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning III, Information Science 9 (1975), pp. 43-80.
[5] G. Zhang, L. Jie, Model and approach of fuzzy bilevel decision making for logistics planning problem, Journal of Enterprise Information Management 20 (2) (2007), pp. 178-197.
[6] Liu Hao-Tien, Wei-Kai Wang An integrated fuzzy approach for provider evaluation and selection in third-party logistics, Expert Systems with Applications 36 (3) (2009), pp. 4387-4398.
[7] C.T. Chen, A fuzzy approach to select the location of the distribution center Fuzzy sets and systems, 118 (1) (2001), pp. 65-73.
[8] K. W. Wong, T. D. Gedeon, L. T. Kóczy, Construction of fuzzy signature from data: an example of SARS pre-clinical diagnosis system, in: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE2004), Budapest, Hungary, 2004, pp.1649-1654.
[9] Á. Ballagi, L. T. Kóczy, T. D. Gedeon, Robot cooperation without explicit communication by fuzzy signatures and decision trees, in: Proceedings of the Joint 2009 International Fuzzy Systems Association World Congress and 2009 European Society of Fuzzy Logic and Technology Conference (IFSA-EUSFLAT2009), Lisbon, Portugal, 2009, pp.1468-1473.
[10] T. Vámos, L. T. Kóczy, G. Biró, Fuzzy signatures in datamining, in: Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada, 2001, pp. 2842-2846 (5).
[11] G. Molnárka, L. T. Kóczy, Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures, Int. J. of Computers , Communications & Control, 2011, No. 3, pp. 442-457.
[12] Á. Bukovics, L.T. Kóczy, Fuzzy Signature-based Model for Qualification and Ranking of Residential Buildings, XXXVIII. IAHS World Congress on Housing, Istanbul, Turkey, 2012. pp. 290-297.
[13] C. Pozna, N. Minculete, R. E. Precup, L. T. Kóczy, Á. Ballagi, Signatures: Definitions, operators and applications to fuzzy modelling, Fuzzy Sets and Systems 201 (2012), pp. 86-104.
[14] J.A.Goguen, L-fuzzy sets, J. Math. Anal. Appl. 18(1) (1967), pp. 145-174.
[15] L. T. Kóczy, T. Vámos, G. Biró, Fuzzy signatures, in: Proceedings of the 4th Meeting of the Euro Working Group on Fuzzy Sets and the 2nd International Conference on Soft and Intelligent Computing (EUROPUSE-SIC99), Budapest, Hungary, 1999, pp. 210-217.
[16] U. Kaymak, H.R. van Nauta Lemke, T. Boer: A sensitivity-based analysis of weighted fuzzy aggregation, In: Proceedings of the IEEE World Congress on Computational Intelligence, IEEE International Conference on Fuzzy Systems, IEEE, 1998. pp. 755-760.
[17] V. Torra: Sensitivity analysis for WOWA, OWA and WM operators, In: Proceedings of ISIE 2001, IEEE International Symposium on Industrial Electronics, IEEE, 2001. pp. 134-137.
[18] M. Zarghami, F. Szidarovszky: Fuzzy quantifiers in sensitivity analysis of OWA operator, Computers & Industrial Engineering 54 (2008), pp. 1006-1018.
[19] I. Á. Harmati, Á. Bukovics, L. T. Kóczy, Sensitivity Analysis of the Weighted Generalized Mean Aggregation Operator and its Application to Fuzzy Signatures, IEEE World Congress on Computational Intelligence (WCCI 2014 - FUZZ-IEEE 2014). Beijing, China, 2014.07.06-2014.07.11. New York: IEEE, 2014. pp. 1327-1332.
[20] I. Á. Harmati, Á. Bukovics, L. T. Kóczy, Sensitivity Analysis of Fuzzy Signatures Using Minkowski’s Inequality, Proceedings in Adaptation, Learning and Optimization: Proc. of the 18th Asia Pacific Symp. on Intell. and Evol. Systems, Singapore, 2014.11.10-2014.11.12. (s. l.): Springer International Publishing Switzerland, 2014. pp. 587-596.
[21] I. Á. Harmati, Á. Bukovics, L. T. Kóczy, Minkowskis inequality based sensitivity analysis of fuzzy signatures, Journal of Artificial Intelligence and Soft Computing Research 6 (4) (2016), pp. 219-229.
[22] Timothy J. Ross: Fuzzy Logic with Engineering Applications, Fourth edition. 2017. pp.45.
[23] T. Hartványi, A. Bakó: Transportation network realization with an optimization method, 2009 4th International Symposium on Computational Intelligence and Intelligent Informatics, 21-25 Oct. 2009