Fuzzy-logic Method for Global Quality Optimization Problem of the Programmed Action Investment

David Barilla (Department of Economics – University of Messina)
Giuseppe Caristi (Department of Economics – University of Messina)
Alfonso Esposito (Unipegaso University)
Sabrina Lo Bosco (Unipegaso University)

Abstract


 In order to analyze the planning of a transport linear infrastructure (railway or ordinary road), in order to optimize a relationship work-environment after-work, the study team (engineers,architects, economists, etc), realize a careful prearranged analysis about the characteristic of the site and the large area which are involved by the work project and, once one found all possible alternative solutions, he should compare them through the use of suitable technical, economical and environmental parameters, choosing that one which maximize the global utility of the public investment. In this paper we study a fuzzy-logic method in order to help the decision maker in the analysis of the programmed action public investment.


Keywords


Fuzzy-Logic method; Multi attribute decision-making; Public investment; optimization; Global quality

Full Text:

PDF

References


[1]L. A. Zadeh, “Fuzzy sets,” Information and Control, 1965, 8(3): 338–353.

[2]Z. Xu, “Intuitionistic fuzzy aggregation operators,” IEEE Transactions on Fuzzy Systems, 2007, 15(6): 1179–1187.

[3]L. Zhou, J. M. Merigo, H. Chen, and J. Liu, “Te optimal group ´ continuous logarithm compatibility measure for interval multiplicative preference relations based on the COWGA operator,” Information Sciences, 2016, 328: 250–269.

[4]L. Zhou, F. Jin, H. Chen, and J. Liu, “Continuous intuitionistic fuzzy ordered weighted distance measure and its application to group decision making,” Technological and Economic Development of Economy, 2016, 22(1): 75–99.

[5]L. Zhou, Z. Tao, H. Chen, and J. Liu, “Intuitionistic fuzzy ordered weighted cosine similarity measure,” Group Decision & Negotiation, 2014, 23(4): 879–900.

[6]L. Zhou, Z. Tao, H. Chen, and J. Liu, “Continuous interval valued intuitionistic fuzzy aggregation operators and their applications to group decision making,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, 2014, 38(7-8): 2190–2205.

[7]P. Liu and S.-M. Chen, “Multiattribute group decision making based on intuitionistic 2-tuple linguistic information,” Information Sciences, 2018, 430/431: 599–619.

[8]P. Liu and P.Wang, “Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making,” International Journal of Intelligent Systems, 2018, 33(2): 259–280.

[9]P. Liu and J. Liu, “Some q-Rung Orthopai Fuzzy Bonferroni Mean Operators and Teir Application to Multi-Attribute Group Decision Making,” International Journal of Intelligent Systems, 2018, 33(2): 315–347.

[10]P. Liu, J. Liu, and J. M. Merigo, “Partitioned Heronian means ´ based on linguistic intuitionistic fuzzy numbers for dealing with multi-attribute group decision making,” Applied Sof Computing, 2018, 62: 395–422.

[11]P. Liu, J. Liu, and S.-M. Chen, “Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multiattribute group decision making,” Journal of the Operational Research Society, 2018, 69(1): 1–24.

[12]P. D. Liu, “Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power heronian aggregation operators,” Computers & Industrial Engineering, 2017, 108: 199–212.

[13]P. Liu and S.-M. Chen, “Group Decision Making Based on Heronian Aggregation Operators of Intuitionistic Fuzzy Numbers,” IEEE Transactions on Cybernetics, 2017, 47(9): 2514– 2530.

[14]P. Liu, S.-M. Chen, and J. Liu, “Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators,” Information Sciences, 2017, 411: 98–121, .

[15]P. Liu and H. Li, “Interval-Valued Intuitionistic Fuzzy Power Bonferroni Aggregation Operators and Teir Application to Group Decision Making,” Cognitive Computation, 2017, 9(4): 494–512.

[16]Z. S. Xu and M. M. Xia, “Distance and similarity measures for hesitant fuzzy sets,” Information Sciences, 2011, 181(11): 2128–2138.

[17]Z. S. Xu and M. M. Xia, “Hesitant fuzzy entropy and crossentropy and their use in multiattribute decision-making,” International Journal of Intelligent Systems, 2012, 27(9): 799–822.

[18]B. Farhadinia, “Distance and similarity measures for higher order hesitant fuzzy sets,” Knowledge-Based Systems, 2014, 55: 43–48.

[19]B. Farhadinia, “Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets,” Information Sciences, 2013, 240: 129–144.

[20]W. Zeng, D. Li, and Q. Yin, “Distance and similarity measures between hesitant fuzzy sets and their application in pattern recognition,” Pattern Recognition Letters, 2016, 84: 267–271.

[21]N. Zhao, Z. Xu, and F. Liu, “Uncertainty measures for hesitant fuzzy information,” International Journal of Intelligent Systems, 2015, 30(7): 818–836.

[22]N. Chen, Z. Xu, and M. Xia, “Correlation coefcients of hesitant fuzzy sets and their applications to clustering analysis,” Applied Mathematical Modelling: Simulation and Computation for Engineering and Environmental Systems, 2013, 37(4): 2197–2211.

[23]H. Liao, Z. Xu, and X.-J. Zeng, “Novel correlation coefcients between hesitant fuzzy sets and their application in decision making,” Knowledge-Based Systems, 2015, 82(3081): 115–127.

[24]M. Xia and Z. Xu, “Hesitant fuzzy information aggregation in decision making,” International Journal of Approximate Reasoning, 2011, 52(3): 395–407.

[25]M. Xia, Z. Xu, and N. Chen, “Some hesitant fuzzy aggregation operators with their application in group decision making,” Group Decision and Negotiation, 2013, 22(2): 259– 279.

[26]H. Liao and Z. Xu, “Subtraction and division operations over hesitant fuzzy sets,” Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2014, 27(1): 65– 72.

[27]H. Liao, Z. Xu, and M. Xia, “Multiplicative consistency of hesitant fuzzy preference relation and its application in group decision making,” International Journal of Information Technology & Decision Making, 2014, 13(1): 47–76.

[28]Y. He, Z. Xu, and J. Gu, “An approach to group decision making with hesitant information and its application in credit risk evaluation of enterprises,” Applied Sof Computing, 2016, 43: 159–169.

[29]Z. Xu and X. Zhang, “Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information,” Knowledge-Based Systems, 2013, 52: 53–64.

[30]G. Sun, X. Guan, X. Yi, and Z. Zhou, “An innovative TOPSIS approach based on hesitant fuzzy correlation coefcient and its applications,”Applied Sof Computing, 2018, 68: 249–267.

[31]X. Zhang and Z. Xu, “Interval programming method for hesitant fuzzy multi-attribute group decision making with incomplete preference over alternatives,” Computers & Industrial Engineering, 2014, 75(1): 217–229.

[32]M. Ashtiani and M. A. Azgomi, “A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty,” Applied Sof Computing, 2016, 42: 18–37.

[33]M. K. Ebrahimpour and M. Efekhari, “Ensemble of feature selection methods: A hesitant fuzzy sets approach,” Applied Sof Computing, 2017, 50: 300–312.

[34]R. M. Rodr´ıguez, L. Martinez, and F. Herrera, “Hesitant fuzzy linguistic term sets for decision making,” IEEE Transactions on Fuzzy Systems, 2012, 20(1): 109–119.

[35]H. Liao, Z. Xu, and X.-J. Zeng, “Hesitant Fuzzy Linguistic VIKOR Method and Its Application in Qualitative Multiple Criteria Decision Making,” IEEE Transactions on Fuzzy Systems, 2015, 23(5): 1343–1355.

[36]J. Wang, J. Wang, and H. Zhang, “A likelihood-based TODIM approach based on multi- hesitant fuzzy linguistic information for evaluation in logistics outsourcing,” Computers & Industrial Engineering, 2016, 99: 287–299.

[37]F. Meng, J. Tang, and C. Li, “Uncertain linguistic hesitant fuzzy sets and their application in multi-attribute decision making,” International Journal of Intelligent Systems, 2018, 33: 586– 614.

[38]X. Feng, L. Zhang, and C. Wei, “Te consistency measures and priority weights of hesitant fuzzy linguistic preference relations,” Applied Sof Computing, 2018, 65: 79–90.

[39]Z. Wu and J. Xu, “A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters,” Information Fusion, 2018, 41: 217–231.

[40]C. Li, R. M. Rodr´ıguez, L. Mart´ınez, Y. Dong, and F. Herrera, “Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions,” Knowledge-Based Systems, 2018, 145: 156–165.

[41]Z. Zhang, X. Kou, and Q. Dong, “Additive consistency analysis and improvement for hesitant fuzzy preference relations,” Expert Systems With Applications, 2018, 98: 118–128.

[42]P. Li, X. Chen, X. Qu, and Q. Xu, “Te Evaluation of Mineral Resources Development Efciency Based on Hesitant Fuzzy Linguistic Approach and Modifed TODIM,” Mathematical Problems in Engineering, 2018: 1–9.

[43]M. Xue and Y. Du, “A group decision-making model based on regression method with hesitant fuzzy preference relations,” Mathematical Problems in Engineering, Article ID 6549791, 2017: 8.

[44]J. Liu, N. Zhou, L.-H. Zhuang, N. Li, and F.-F. Jin, “IntervalValued Hesitant Fuzzy Multiattribute Group Decision Making Based on Improved Hamacher Aggregation Operators and Continuous Entropy,” Mathematical Problems in Engineering, 2017.

[45]Y. Yang, L. Lang, L. Lu, and Y. Sun, “A new method of multiattribute decision-making based on interval-valued hesitant fuzzy sof sets and its application,” Mathematical Problems in Engineering, 2017: 8.

[46]Zimmermann H.J.. Fuzzy set theory and its applications, Boston, Kluwer Academic.

[47]Zimmermann H.J.. Fuzzy sets, Decision making and expert system, Boston, Kluwer Academic.

[48]Lo Bosco D., Praticò F. G, Research prospects in Pay Adjustment models for roads and railways: a theoretical and experimental study, V International Conference Of Stochastic Geometry, Convex Bodies, Empirical Measures & Applications, Mondello (PA), 2005, 77.

[49]Lo Bosco S.. Un criterio matematico per l’analisi dell’utilità globale di un intervento pubblico sulle reti di mobilità, Collana scientificaAracne (ISBN 978-88-255-1507-7), Gioacchino Onorati Editore Srl, Roma, 2018.

[50]Collan M., Fuller R., Mezzei J.. A Fuzzy pay-off method for real option valuation, Journal of applied mathematics and decision sciences, 2009: 1-14.



DOI: https://doi.org/10.30564/jbar.v2i3.909

Refbacks

  • There are currently no refbacks.
Copyright © 2019 David Caristi, Giuseppe Caristi, Alfonso Esposito, Sabrina Lo Bosco Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.