SELECTION OF UNIVERSAL SERVICE FINANCING MODEL USING FUZZY MCDM
4. METHOD OF SELECTION OF PROPOSED MODELS ON A PARTICULAR AREA
Taking into account the diversity of various regions within one country, there is a need for special consideration of each of them in terms of defining the method of financing the universal service. Therefore it is necessary to observe the territorial units established by NUTS 2 standard. In each of these units observed, depending on the characteristics of the postal market and the aim sought to be achieved by the regulatory body, weights of the criteria are set for the evaluation of certain alternatives of financing the Universal Service. The determination of these weights is carried out by AHP method. The core of the system is a performance matrix for specific criteria by proposed alternative models of financing. Values of matrices are obtained on the basis of questionnaires filled by experts for financing models. The survey was conducted over 10 experts. The experts were asked to assess the realization of each criterion, via linguistic variables which are expressed by triangular fuzzy numbers in the range of 0-5. The possible values of these variables are: “unrealized”, “poorly realized”, “medium realized”, “very realized”, “and fully realized”. From the obtained fuzzy numbers, by applying BNP, we get defuzzicated values. Ranking of values is made by implementing TOPSIS, and then the selection of the financing model most similar to the ideal solution is made.
We will observe the territory of the Republic of Serbia. In accordance with the NUTS 2 standardization and looking at the highest concentration of competition we decided to consider the statistical region of Belgrade. Looking at the characteristics of the observed area weight coefficients of the criteria defined on the basis of AHP are: fair competition – 0.15, social equality - 0.1, compliance with state aid rules - 0.1, transparency - 0.1, proportionality - 0.1, feasibility - 0.05, reliability - 0.05, allocative efficiency - 0.1, productive efficiency - 0.1, dynamic efficiency - 0.1. Results of testing experts analyzed on the basis of equation (5) are given in Table 1. After measuring performance in the form of fuzzy numbers, made their defuzzification using the Center of Area (equation (6), so that it could be applied TOPSIS the procedure of ranking (Table 3).
Table 1: Fuzzy performance of financing model
criteria / alternatives Reserved area Compensation fund “pay or play” Additional funding Fair competition (1.81, 2.21, 2.47) (3.90, 4.23, 4.58) (3.52, 4.29, 4.7) (3.91, 4.18, 4.24)
Social equality (3.63, 3.93, 4.24) (3.09, 4.06, 4.75) (4.72, 4.84, 4.95) (4.54, 4.63, 4.69) Compliance with
state aid rules (4.75, 4.91, 4.94) (4.49, 4.87, 4.96) (4.15, 4.53, 4.76) (3.97, 4.01, 4.06) Transparency (2.95, 3.12, 3.72) (1.48, 2.39, 2.54) (2.8, 2.94, 3.03) (2.04, 2.9, 3.19) Proportionality (2.42, 2.87, 3.18) (1.91, 2.40, 2.57) (2.79, 3.16, 3.22) (2.95, 3.37, 3.5) Feasibility (4.57, 4.68, 4.73) (2.03, 3.39, 3.57) (2.84, 3.22, 3.91) (3.93, 4.27, 4.40) Reliability (4.67, 4.72, 4.87) (2.75, 3.24, 3.77) (2.74, 3.08, 3.31) (3.39, 3.92, 4.12) Allocative efficiency (2.62, 3.31, 3.55) (2.17, 2.89, 3.12) (3.7, 4.34, 4.6) (3.03, 3.48, 4.18) Productive efficiency (2.27, 3.20, 3.69) (2.04, 2.48, 2.62) (4.09, 4.38, 4.71) (4.35, 4.43, 4.82) Dynamic efficiency (1.69, 2.12, 2.48) (1.31, 2.40, 2.71) (4.42, 4.68, 4.95) (4.05, 4.21, 4.44) Table 2: Overall performance measures of financing models
criteria / alternatives Reserved area Compensation fund “pay or play” Additional funding
Fair competition 2.13 4.24 4.17 4.11
Social equality 3.94 3.97 4.84 4.62
Compliance with state aid rules 4.87 4.77 4.48 4.01
Transparency 3.26 2.14 2.92 2.71 Proportionality 2.82 2.29 3.06 3.27 Feasibility 4.66 3.00 3.32 4.20 Reliability 4.75 3.25 3.04 3.81 Allocative efficiency 3.17 2.73 4.21 3.56 Productive efficiency 3.05 2.38 4.39 4.53 Dynamic efficiency 2.10 2.14 4.68 4.23
Table 3: Ranking of financing models
Rang Model The similarity to the ideal solution
1 “pay or play” 0.8047
2 Additional funding 0.7655 3 Compensation fund 0.4144
5. CONCLUSION
The problem of financing the universal postal service captures the attention of the postal sector for many years. One of the basic principles underlying the role of postal services is facing the issue of sustainability, as profitability of postal system is becoming more and more basic benchmark in successful functioning. Past experience has shown that this business segment is unattractive for operators that have emerged from the market liberalization, so public postal operators have taken this responsibility. The reason for this may be sought in the financing model that is administered on the whole territory of the state.
Different characteristics of each region within the state should be considered in order to establish appropriate mechanisms to encourage other operators to take an active role in the provision of universal service. On the model that we presented, we came to the conclusion that the most appropriate model for the observed region is “pay or play”, while a reserved area that is currently in force took the last place in relation to the considered models of financing.
REFERENCES
Buckley, J. J. (1985). Ranking alternatives. Using fuzzy numbers. FuzzySets and Systems, 15(1), 21–31. Deng, Y., & Chan, F. T. (2011). A new fuzzy dempster MCDM method and its application in supplier
selection. Expert Systems with Applications, 38(8), 9854-9861.
Dieke, A. K., Bender, C., Campbell Jr, J. I., Cohen, R. H., Müller, C., Niederprüm, A., de Streel, A., Thiele, S., & Zanker, C. (2013). Main Developments in the Postal Sector (2010-2013). Bad Honnef: WIK Consulting.
Directive 97/67/EC of the European Parliament and of the Council of 15 December 1997. Directive 2002/39/EC of the European Parliament and of the Council of 10 June 2002. Directive 2008/6/EC of the European Parliament and of the Council of 20 February 2008.
Dubois, D., & Prade, H. (1978). Operations on fuzzy number,International Journal of System Science, 9(6), 613–626.
Gautier, A., & Paolini, D. (2010). Universal service financing in competitive postal markets: One size does not fit all. In Sixth conference on regulation, competition and universal service in the postal sector, Toulouse.
Gautier, A., & Wauthy, X. (2010). Price competition under universal service obligations. International Journal of Economic Theory, 6(3), 311-326.
Hwang, C. L., Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications, Springer, Berlin Heidelberg.
Jaag, C., & Trinkner, U. (2011). The interaction between universal service costing and financing in the postal sector: a calibrated approach. Journal of regulatory economics, 39(1), 89-110.
Jaag, C., Trinkner, U., Lisle, J., Waghe, N., & Van Der Merwe, E. (2011). Practical approaches to USO costing and financing. Competition and Regulation in Network Industries, 12(2), 108-129.
Kabak, M., Burmaoğlu, S., & Kazançoğlu, Y. (2012). A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications, 39(3), 3516-3525.
Kujačić, M. (2002), Primena analitičkog mrežnog procesa u projektovanju organizacije poštanskog saobraćaja, Saobraćajni fakultet, Beograd
Oxera Consulting Ltd (2007) Funding universal service obligations in the postal sector- Prepared for La Poste, De Post-La Poste, Hellenic Post, Poste Italiane, P&T Luxembourg, Correos, Magyar Posta, Cyprus Post, Poczta Polska.
Price Waterhouse Coopers (2006) The Impact on Universal Service of the Full Market Accomplishment of the Postal Internal Market in 2009, report to the European Commission.
Ross, T. J. (2009). Fuzzy logic with engineering applications. John Wiley & Sons.
Saaty, T. L. (1986). Axiomatic Foundation of the Analytic Hierarchy Process. Management Science, 32, 841- 855.
Teodorović, D. (1999). Fuzzy logic systems for transportation engineering: the state of the art. Transportation Research Part A: Policy and Practice, 33(5), 337-364.
Teodorović D., Šelmić M. (2012) Računarska inteligencija u saobraćaju. Saobraćajni fakultet, Beograd. Tsaur, S. H., Chang, T. Y., & Yen, C. H. (2002). The evaluation of airline service quality by fuzzy MCDM.
Tourism management, 23(2), 107-115.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.