A new approach to R&D project selection problem and a solution proposal: UTA method

Abstract

Nowadays with the rapid increase in globalization, competitiveness is based on the information. Information is transformed into science and technology by systematizing with research and development studies. The development of a country, producing added value, providing employment, continuing to increase in production continuously is only possible by increasing the information content and by converting the increasing information into science and technology. Considering the variety of products and services is very much, innovation is necessary for countries and producers for to continue and compete in the activities. In this regard, research and development (R&D) carries great importance. R&D can be defined as the development of new products, new production methods, or the acquisition of new technologies, products, production methods from present information. As it is in other countries, our country also encourages R&D investments and supports R & D investments. In determining the size of support according to the R&D dimension, the value added which will be effective can be evaluated as benefit. For this purpose, in our study, the projects were ranked in a competition where R & D projects are selected by considering the efficient use of supports. In this sense, the UTA Method which is one of Multi-Criteria Decision Making (MCDM) methods was preferred for the solution process for our problem. UTA method preferred because of its considering marginal utility structure and linear programming approach. The GAMS program was used to solve the mathematical model obtained during the solution process. Because of being benefit-based, ıt has been tried to be shown that the solution can support the ranking of projects more effectively and correctly.

Keywords

• UTA method,Selection of R&D projects,Multi criteria decision method

Ar-Ge proje seçim problemine yeni bir yaklaşım ve çözüm önerisi: UTA yöntemi

Abstract

Küreselleşmenin hızla arttığı günümüzde rekabet edebilirliğin temeli bilgiye dayanmaktadır. Bilgi, Ar-Ge çalışmaları ile sistematik hale getirilerek bilim ve teknolojiye dönüşür. Bir ülkenin kalkınması katma değer üretmesi, istihdam sağlayabilmesi, üretimlerini sürekli artırarak devam ettirebilmesi ancak bilgi birikiminin artması ve artan bilginin bilim ve teknolojiye dönüştürülmesi ile mümkündür. Ürün ve hizmet çeşitliliğinin çok fazla olduğu göz önüne alındığında ülkelerin ve üreticilerin bu faaliyetlerini sürdürebilmeleri ve rekabet edebilmeleri için yenilikler ortaya koyması gerekmektedir. Bu konuda araştırma ve geliştirme (Ar-Ge) büyük önem taşımaktadır. Ar-Ge, hali hazırda bulunan bilgilerden yeni ürün, yeni üretim yöntemlerinin geliştirilmesi veya yeni teknolojilerin, ürünlerin, üretim yöntemlerinin elde edilmesi olarak tanımlanabilir. Diğer ülkelerde olduğu gibi ülkemiz de Ar-Ge yatırımlarını teşvik etmekte ve Ar-Ge yatırımlarına destek vermektedir. Bu kapsamda birçok kurum ve kuruluş girişimcilerin, firmaların, akademisyenlerin Ar-Ge projelerini sağladığı katma değer oranında desteklemektedir. Ar-ge boyutunda desteğin belirlenmesinde etkin olacak katma değer ise fayda olarak değerlendirilebilir. Bu amaçla desteklerin etkin bir şekilde kullanılması göz önüne alınarak, çalışmamızda Ar-Ge projelerinin seçiminin yapıldığı bir yarışmada projelerin değerlendirme süreci ele alınmıştır. UTA yöntemi marjinal faydayı dikkate alan yapısı ve doğrusal programlama yaklaşımını içermesi nedeniyle tercih edilmiştir. Yöntemin çözüm aşamasında elde edilen matematiksel modelin çözümü için de GAMS Programından yararlanılmıştır. Bulunan çözümün fayda temelli olmasından dolayı projelerin daha etkin ve doğru şekilde sıralanmasına destek sağlayabileceği gösterilmeye çalışılmıştır.

Keywords

• UTA Yöntemi,Ar-Ge proje seçimi,Çok kriterli karar verme yöntemi

References

1. Güryeli M. AR-GE Projeleri Seçim Probleminin AHP Yöntemi İle İncelenmesi: Kamu Destekli Teknolojik Ürün Yatırım Destek Programı Üzerine Bir Uygulama. Yüksek Lisans Tezi, Adnan Menderes Üniversitesi, Aydın, Türkiye, 2016.
2. Zerenler M, Necdet T, Esen Ş. “Küresel teknoloji, araştırma-geliştirme (Ar-Ge) ve yenilik ilişkisi”. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(17), 653-667, 2007.
3. OECD, Frascati Kılavuzu, Bilimsel ve Teknolojik Faaliyetlerin Ölçümü (Türkçe Versiyonu), 6 Baskı. Ankara, Türkiye, Tübitak Yayınları, 2002.
4. Pesen E. Analitik Hiyerarşi Proses ile Ar-Ge Projesi Seçimi: İş Makinaları Sektöründe Bir Uygulama. Yüksek Lisans Tezi, Çağ Üniversitesi, Mersin, Türkiye, 2012.
5. Ghasemzadeh F, Norman PA. “Project portfolio selection through decision support”. Decision Support Systems, 29(1), 73-88, 2000.
6. Karasakal E, Aker P. “A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem”. Omega, 73, 79-92, 2017.
7. Yakıcı AT, Perçin S. “AR-GE projelerinin seçiminde grup kararına dayalı bulanık karar verme yaklaşımı”. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 26(2), 237-255, 2012.
8. Peker D. AR-GE Projelerinin Önceliklendirilmesi ve Seçimi İçin Çok Kriterli Bir Model Önerisi. Yüksek Lisans Tezi, Gazi Üniversitesi, Ankara, Türkiye, 2014.

9. Liu F, Chen YW, Yang JB, Xu DL, Liu W. “Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule”. International Journal of Project Management, 37(1), 87-97, 2019.
10. Aydin A, Parker RP. ”Innovation and technology diffusion in competitive supply chains”. European Journal of Operational Research, 265(3), 1102-1114, 2018.
11. Salimi N, Jafar R.” Evaluating firms’ R&D performance using best worst method”. Evaluation and Program Planning, 66, 147-155, 2018.
12. Liang D, Xu Z, Liu D, Wu Y. “ Method for three-way decisions using ideal TOPSIS solutions at pythagorean fuzzy information”. Information Sciences, 435, 282-295, 2018.
13. Cheng CH, Liou J, Chiu CY. “A consistent fuzzy preference relations based ANP model for R&D project selection”. Sustainability, 9(8), 1352-1368, 2017.
14. Marcondes GAB, Leme RC. “R&D projects selection in telecommunications under uncertainty and resource restrictions using scheduling”. IEEE International Conference on Communications Workshops, Paris, France, 21-25 May 2017.
15. Liu F, Zhu WD, Chen YW, Xu DL, Yang JB. “Evaluation, ranking and selection of R&D projects by multiple experts: an evidential reasoning rule based approach”. Scientometrics, 111(3), 1501-1519, 2017.
16. Collan M, Fedrizzi M, Luukka P. “Multi-distance and fuzzy similarity based fuzzy topsis”. Studies in Computational Intelligence, 613, 227-244, 2016.
17. Wu J, Chu J, Zhu Q, Li Y, Liang L. “Determining common weights in data envelopment analysis based on the satisfaction degree”. Journal of the Operational Research Society, 67(12), 1446-1458, 2016.
18. Liu O, Wang J, Ma J, Sun Y. “An ıntelligent decision support approach for reviewer assignment in R&D project selection”. Computers In Industry, 76, 1-10, 2016.
19. Wu J, Chu J, Sun J, Zhu Q, Liang L. “Extended secondary goal models for weights selection in DEA cross-efficiency evaluation”. Computers & Industrial Engineering, 93, 143-151, 2016.
20. Latipova AT. “On optimization of r&d project selection and scheduling”. IFAC-PapersOnLine, 48(25), 6-10, 2015.
21. Collan M, Fedrizzi M, Luukka P. “New closeness coefficients for fuzzy similarity based fuzzy TOPSIS: an approach combining fuzzy entropy and multidistance”. Advances in Fuzzy Systems, 2015, 1-12, 2015.
22. Wan SP, Xu G, Wang F, Dong J. “A new method for atanassov’s ınterval-valued ıntuitionistic fuzzy MAGDM with ıncomplete attribute weight information”. Information Sciences, 316, 329-347, 2015.
23. Bin A, Azevedo A, Duarte L, Salles-Filho S, Massaguer P. “R&D and ınnovation project selection: can optimization methods be adequate?”. Procedia Computer Science, 55, 613-621, 2015.
24. Zolfani SH, Salimi J, Maknoon R, Kildiene S. “Technology foresight about R&D projects selection; application of SWARA method at the policy making level”. Engineering Economics, 26(5), 571-580,2015.
25. Haddad AN, Candido RM, Freitas ALP, Rosa LV. “Selection of R&D projects through technological, social and environmental analysis”. In IIE Annual Conference. Proceedings, Honolulu, Hawaii, USA, 18-21 January 2015.
26. Eshlaghy AT, Razi FF. “A hybrid grey-based k-means and genetic algorithm for project selection”. International Journal of Business Information Systems, 18(2), 141-159,2015.
27. Silva T, Jian M, Chen Y. “Process analytics approach for R&D project selection”. ACM Transactions on Management Information Systems, 5(4), 1-34, 2014.
28. Huang X, Zhao T. “Project selection and scheduling with uncertain net income and investment cost”. Applied Mathematics and Computation, 247, 61-71, 2014.
29. Collan M, Luukka P. “Evaluating R&D projects as investments by using an overall ranking from four new fuzzy similarity measure-based TOPSIS variants”. IEEE Transactions on Fuzzy Systems, 22(3), 505-515, 2014.
30. Arunachalam N, Sathya E, Begum SH, Makeswari M. “An ontology based text mining framework for R&D project selection”. International Journal of Computer Science & Information Technology, 5(1), 161-171, 2013.
31. Gosenheimer C. “Project Prioritization: A Structured Approach to Working on What Matters Most”. Office of Quality Improvement, University of Wisconsin, Wisconsin, USA, Sciencific Report, 3-6, 2012.
32. Graves SB, Ringuest JL. “Patient decision making: exponential versus hyperbolic discounting”. Managerial and Decision Economics, 33(7-8), 453-462, 2012.
33. Mohaghar A, Fathi MR, Faghih A, Turkayesh MM. “An integrated approach of fuzzy ANP and fuzzy TOPSIS for R&D project selection: a case study”. Australian Journal of Basic and Applied Sciences, 6(2), 66-75, 2012.
34. Ayan TY, Perçin S. “Ar-Ge projelerinin seçiminde grup kararına dayalı bulanık karar verme yaklaşımı”. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 26(2), 237-255, 2012.
35. Eckhause JM, Gabriel SA, Hughes DR. “An integer programming approach for evaluating R&D funding decisions with optimal budget allocations”. IEEE Transactions on Engineering Management, 59(4), 679-691, 2012.
36. Hung YH, Huang ML, Fanchiang KL. “Applying the fuzzy analytic network process to the selection of an advanced integrated circuit (IC) packaging process development project”. International Journal of Physical Sciences, 7(2), 281-296, 2012.
37. Murray SL, Alpaugh A, Burgher K, Flachsbart B. “Development of a systematic approach to project selection for rural economic development”. Journal of Rural And Community Development, 5(3), 1-8, 2011.
38. Feng B, Ma J, Fan ZP. “An integrated method for collaborative R&D project selection: supporting innovative research teams”. Expert Systems with Applications, 38(5), 5532-5543, 2011.
39. Huang CC, Chu PY. “Using the fuzzy analytic network process for selecting technology R&D projects”. International journal of technology management, 53(1), 89-115, 2011.
40. Wang Z, Zhang S, Kuang J. “A dynamic MAUT decision model for R&D project selection”. 2010 International Conference on Computing, Control and Industrial Engineering, Wuhan, China, 5-6 June 2010.
41. Verbano C, Nosella A. “Addressing R&D investment decisions: a cross analysis of R&D project selection methods”. European Journal of Innovation Management, 13(3), 355-379, 2010.
42. Thal Jr AE, Mayer GC, Weir JD. “Strategic R&D project selection using decision analysis”. 60th Annual Conference and Expo of the Institute of Industrial Engineers, Cancun, Mexico, 5-9 June 2010.
43. Calof J, Smith J. “The integrative domain of foresight and competitive intelligence and its impact on R&D management”. R&D Management, 40(1), 31-39, 2010.
44. Feng J, Li X. “Making multiple attribute decision with DEA method”. Proceedings of the 3rd International Conference on Management Science and Engineering Management, Bangkok, Thailand, 2-4 November 2009.
45. Cheung MT, Greenfield PF, Liao Z. “Selecting R&D projects for technology-based innovation: Knowledge management in the face of embarras de choix”. Journal of General Management, 35(4), 61-80, 2009.
46. Habib M, Khan R, Piracha JL. “Analytic network process applied to R&D project selection”. In 2009 International Conference on Information and Communication Technologies, Karachi, Pakistan, 15-16 August 2009.
47. Yi C. “ A decision-making approach for R&D project selection in a fuzzy environment”. In 2008 International Seminar on Business and Information Management, Wuhan, China, 19 December 2008.
48. Fernandez E, Lopez F, Navarro J, Vega I, Litvinchev I. “An integrated mathematical-computer approach for R&D project selection in large public organisations”. International Journal of Mathematics in Operational Research, 1(3), 372-396, 2009.
49. Yuen KK, Lau HC. “A linguistic possibility-probability aggregation model for decision analysis with imperfect knowledge”. Applied Soft Computing, 9(2), 575-589, 2009.
50. Wu J, Liang L, Yang F, Yan H. “ Bargaining game model in the evaluation of decision making units”. Expert Systems with Applications, 36(3), 4357-4362, 2009.
51. Yi C, Ning Y, Jin Q. “A fuzzy multi-criteria evaluation approach for R&D project selection”. In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China, 12-17 October 2008.
52. Li CH, Sun YH, Du YW. “A new MCDM approach based on cross-impact analysis for ranking dependent alternatives”. In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China, 12-17 October 2008.
53. Tolga AÇ, Kahraman C. “A new fuzzy real optıons valuatıon model: ıts applıcatıon to multıcrıterıa R&D project selection”. Computational Intelligence In Decision And Control-Proceedings of the 8th International Flins Conference, Singapore, 30 December 2008.
54. Huang CC, Chu PY, Chiang YH. “A fuzzy AHP application in government-sponsored R&D project selection”. Omega, 36(6), 1038-1052, 2008.
55. Tolga AC, Kahraman C. “Fuzzy multi-criteria evaluation of R&D projects and a fuzzy trinomial lattice approach for real options”. 2008 3rd International Conference on Intelligent System and Knowledge Engineering, Xiamen, China, 17-19 November 2008.
56. Chen CT, Hung WZ. “Applying fuzzy linguistic variable and ELECTRE method in R&D project evaluation and selection”. In 2008 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, 8-11 December 2008.
57. Tolga AÇ. “Fuzzy multicriteria R&D project selection with a real options valuation model”. Journal of Intelligent & Fuzzy Systems, 19(4-5), 359-371, 2008.
58. Tolga AÇ, Kahraman C. “Fuzzy multiattribute evaluation of R&D projects using a real options valuation model”. International Journal of Intelligent Systems, 23(11), 1153-1176,2008.
59. De Piante Henriksen A, Palocsay SW. “An excel-based decision support system for scoring and ranking proposed R&D projects”. International Journal of Information Technology & Decision Making, 7(3), 529-546, 2008.
60. Yao T. “Dynamic R&D projects selection: the role of uncertainty and managerial flexibility”. IIE Annual Conference, Orlando, Florida, 22 May 2006.
61. Lawson CP, Longhurst PJ, Ivey PC. “The application of a new research and development project selection model in SMEs”. Technovation, 26(2), 242-250, 2006.
62. Sun H, Ma T. “A packing-multiple-boxes model for R&D projectselection and scheduling”. Technovation, 25(11), 1355-1361, 2005.
63. Mohanty RP, Agarwal R, Choudhury AK, Tiwari MK. “A fuzzy ANP-based approach to R&D project selection: a case study”. International Journal of Production Research, 43(24), 5199-5216, 2005.
64. Tian Q, Ma J, Liang J, Kwok RC, Liu O. “An organizational decision support system for effective R&D project selection”. Decision Support Systems, 39(3), 403-413, 2005.
65. Coldrick S, Longhurst P, Ivey P, Hannis J. “An R&D options selection model for investment decisions”. Technovation, 25(3), 185-193, 2005.
66. Cho KT, Kwon CS. “Hierarchies with dependence of technological alternatives: A cross-impact hierarchy process”. European Journal of Operational Research, 156(2), 420-432, 2004.
67. Hsu YG, Tzeng GH, Shyu JZ.“Fuzzy multiple criteria selection of government‐sponsored frontier technology R&D projects”. R&D Management, 33(5), 539-551, 2003.
68. Tian Q, Ma J, Liu O. “A hybrid knowledge and model system for R&D project selection”. Expert systems with applications, 23(3), 265-271, 2002.
69. Tian Q, Ma J, Liang CJ, Kwok RCW, Liu O, Zhang Q. “An organizational decision support approach to R and D project selection”. In Proceedings of the 35th Annual Hawaii International Conference on System Sciences IEEE, Big Island, HI, USA, 10 January 2002.
70. Coldrick S, Lawson CP, Ivey PC, Lockwood C. “A decision framework for R&D project selection”. In IEEE International Engineering Management Conference, Cambridge, United Kingdom, 18-20 August 2002.
71. Meade LM, Presley A. “R&D project selection using the analytic network process". Transactions on Engineering Management, 49(1), 59-66, 2002.
72. Kuchta D. “A fuzzy model for R&D project selection with benefit, outcome and resource interactions”. The Engineering Economist, 46(3), 164-180,2001.
73. Loch CH, Pich MT, Terwiesch C, Urbschat M. “Selecting R&D projects at BMW: A case study of adopting mathematical programming models”. IEEE Transactions on Engineering Management, 48(1), 70-80, 2001.
74. Abo-Sinna, Mahmoud A, Al-Azzaz, Abdalah S. “Multi-objective R&D project selection problem under fuzziness”. Modelling, Measurement and Control D, 21(1-2), 27-55, 2000.
75. Vislosky DM, Fischbeck PS. “A mental model approach applied to R&D decision-making”. International Journal of Technology Management, 19(3-5), 453-471, 2000.
76. Henriksen AD, Traynor AJ. “A practical R&D project-selection scoring tool”. IEEE Transactions on Engineering Management, 46(2), 158-170, 1999.
77. Heidenberger K, Stummer C. “Research and development project selection and resource allocation: a review of quantitative modelling approaches”. International Journal of Management Reviews, 1(2), 197-224, 1999.
78. Vonortas NS, Hertzfeld HR. “Research and development project selection in the public sector”. Journal of Policy Analysis and Management, 17(4), 621-638, 1998.
79. Bordley RF. “R&D project selection versus R&D project generation”. IEEE Transactions on Engineering Management, 45(4), 407-413, 1998.
80. Lee M, Om K. “The concept of effectiveness in R&D project selection”. International Journal of Technology Management, 13(5-6), 511-524, 1997.
81. Al-Mazidi S, Ghosn AA. “A management model for technology and R&D selection”. International Journal of Technology Management, 13(5-6), 525-541, 1997.
82. Cabral-Cardoso C, Payne RL.“Instrumental and supportive use of formal selection methods in R&D project selection”. IEEE Transactions on Engineering Management, 43(4), 402-410, 1996.
83. Santhanam R, Kyparisis GJ.“A decision model for interdependent information system project selection”. European Journal of Operational Research, 89(2), 380-399, 1996.
84. Coffin MA, Taylor III BW. “Multiple criteria R&D project selection and scheduling using fuzzy logic”. Computers and Operations Research, 23(3), 207-220, 1996.
85. Coffın MA, Taylor III BW. “R&D project selection and scheduling with a filtered beam search approach”. IIE transactions, 28(2), 167-176, 1996.
86. Henig MI, Katz H. “R&D project selection: A decision process approach”. Journal of Multi‐Criteria Decision Analysis, 5(3), 169-177, 1996.
87. Venkatraman R, Venkatraman S. “R&D project selection and scheduling for organizations facing product obsolescence”. R&D Management, 25(1), 57-70, 1995.
88. Chun YH. “Sequential decisions under uncertainty in the R&D project selection problem”. IEEE Transactions on Engineering Management, 41(4), 404-413, 1994.
89. Silvennoinen P.“R&D project selection for promoting the efficiency of energy use”. R&D Management, 24(4), 317-324, 1994.
90. Schmidt RL.“A model for R&D project selection with combined benefit, outcome and resource interactions”. IEEE Transactions on Engineering Management, 40(4), 403-410, 1993.
91. Graves SB, Ringuest JL.“Choosing the best solution in an R&D project selection problem with multiple objectives”. The Journal of High Technology Management Research, 3(2), 213-224, 1992.
92. Ringuest JL, Graves SB.“The linear R&D project selection problem: an alternative to net present value”. Transactions on Engineering Management, 37(2), 143-146, 1990.
93. Fahrni P, Spätig M. “An application‐oriented guide to R&D project selection and evaluation methods”. R&D Management, 20(2), 155-171, 1990.
94. Ringuest JL, Graves SB. “The Linear Multi-Objective R&D Project Selection Problem”. IEEE Transactions on Engineering Management, 36 (1), 54-57, 1989.
95. Júnior OPD. “The R & D project selection problem with fuzzy coefficients”. Fuzzy Sets and Systems, 26(3), 299-316, 1988.
96. Liberatore MJ.“An expert support system for R&D project selection”. Mathematical and Computer Modelling, 11, 260-265, 1988.
97. Bard JF, Balachandra R, Kaufmann PE.“An interactive approach to R&D project selection and termination”. IEEE Transactions on Engineering Management, 35(3), 139-146, 1988.
98. Mehrez A.“Selecting R&D projects: a case study of the expected utility approach”. Technovation, 8(4), 299-311, 1988.
99. Liberatore MJ.“An extension of the analytic hierarchy process for industrial R&D project selection and resource allocation”. IEEE Transactions on Engineering Management, 1, 12-18, 1987.
100. Lee J, Lee S, Bae ZT.“R&D project selection: behavior and practice in a newly industrializing country”. IEEE transactions on engineering management, 3, 141-147, 1986.
101. Liberatore MJ. “R&D project selection”. Telematics and Informatics, 3(4), 289-300, 1986.
102. Czajkowski AF, Jones S.“Selecting interrelated R & D projects in space technology planning”. IEEE Transactions on Engineering management, (1), 17-24, 1986.
103. Costello D.“A practical approach to R&D project selection. Technological Forecasting and Social Change, 23(4), 353-368, 1983.
104. Fiksel J, Cox LA, Richardson DL, Adamantiades AG. “Selection of nuclear safety research and development projects through value-impact analysis”. Nuclear Safety, 24(1), 12-25,1983.
105. Cook WD, Seiford LM. “R&D project selection in a multidimensional environment: A practical approach”. Journal of the Operational Research Society, 33(5), 397-405, 1982.
106. Taylor III BW, Moore LJ, Clayton ER. “R&D project selection and manpower allocation with integer nonlinear goal programming”. Management science, 28(10), 1149-1158, 1982.
107. Aaker DA, Tyebjee TT. “A model for the selection of interdependent R&D projects”. IEEE Transactions on engineering management, 25(2), 30-36, 1978.
108. Baker NR. “R & D project selection models: An assessment”. IEEE Transactions on Engineering Management, (4), 165-171, 1974.
109. Moore JR, Baker NR. “An analytical approach to scoring model design-application to research and development project selection”. IEEE Transactions on Engineering Management, (3), 90-98, 1969.
110. Ishizaka A, Nemery P. Multi-Criteria Decision Analysis: Methods and Software, 1st ed. United Kingdom, John Wiley & Sons Ltd 2013.
111. Chhipi-Shrestha G, Kaur M, Hewage K, Sadiq R. “Optimizing residential density based on water–energy–carbon nexus using UTilités Additives (UTA) method”. Clean Technologies and Environmental Policy, 20(4), 855-870, 2018.
112. Jacquet-Lagreze, E, Siskos J. “Assessing a set of additive utility functions for multicriteria decision-making, the UTA method”. European Journal of Operational Research, 10(2), 151-164, 1982.
113. Işık AT, Adalı EA. “UTA method for the consulting firm selection problem”. Journal of Engineering Science & Technology Review, 9(1), 56-60, 2016.
114. Yıldırım BF, Önder E, Turan G. Operasyonel, Yönetsel ve Stratejik Problemlerin Çözümünde Çok Kriterli Karar Verme Yöntemleri. Ankara, Türkiye, Dora Yayıncılık, 2015.
115. Roszkowska E. “The application of UTA method for support evaluation”. Negotiation Offers, 2(80), 144-162, 2016.
116. Athawale VM, Rajanikar K, Shankar C. “Decision making for material selection using the UTA method”. The International Journal of Advanced Manufacturing Technology, 57, 1-4, 2011.
117. Sen P, Yang JB. Multiple criteria decision support in engineering design. New York, Springer Science & Business Media, 2012.
118. Siskos Y, Yannacopoulos D. “UTASTAR: An ordinal regression method for building additive value functions”. Investigaçao Operacional, 5(1), 39-53, 1985.
119. Devaud JM, Groussaud G, Jacquet-Lagreze E. "UTADIS: Une méthode de construction de fonctions d’utilité additives rendant compte de jugements globaux. (A method for the construction of Additive Utility function based on global judgements)". 12th Meeting of the EURO Working Group Multicriteria Aid for Decisions, Bochum, Germany, 9-10 October 1980.
120. Greco S, Mousseau V, Slowinski R. “Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions”. European Journal of Operational Research, 191, 416-436, 2008.
121. Figueira J, Greco S, Slowinski R.“Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method”. European Journal of Operational Research, 195, 460-486, 2009.
122. Siskos J. “Analyse de systèmes de décision multicritère en univers aléatoire”. Foundations of Control Engineering, 10(3-4), 193-212, 1983.
123. Beuthe M, Scannella G. “Comparative analysis of UTA multicriteria methods”. European Journal of Operational Research, 130(2), 246.262, 2001.
124. Despotis DK, Yannacopoulos D, Zopounidis C. “A review of the UTA multicriteria method and some improvements”. Foundations of Computing and Decision Sciences, 15, 63-76, 1990.
125. Stavrou DI, Siskos EY, Ventikos NP, Psarras JE. Robust Evaluation of Risks in Ship-to-Ship Transfer Operations: Application of the STOCHASTIC UTA Multicriteria Decision Support Method. Editors: Lee, Paul Tae-Woo and Yang, Zaili. Multi-Criteria Decision Making in Maritime Studies and Logistics, 175-218, Cham, Springer International Publishing, 2018.
126. Inuiguchi M, Inoue H. “A fuzzily partitioned ınterval function model for ordinal regression”. In 2018 4th International Conference on Computer and Information Sciences, Kuala Lumpur, Malaysia, 13-14 August 2018.
127. Matsatsinis NF, Grigoroudis E, Siskos E. Disaggregation Approach to Value Elicitation. Editors: Luis C Dias, Alec Morton, John Quigley. International Series in Operations Research & ManagementScience, 313-348, Cham, Switzerland, Springer, 2018.
128. Kaynar N, Karsu Ö. “Equitable decision making approaches over allocations of multiple benefits to multiple entities”. Omega, 81, 85-98, 2018.
129. Morano P, Tajani F, Locurcio M. “Multicriteria analysis and genetic algorithms for mass appraisals in the Italian property market”. International Journal of Housing Markets and Analysis, 11(2), 229-262, 2018.
130. Minnetti V. “On the UTA methods for solving the model selection problem.” International Conference on Optimization and Decision Science Springer, Sorrento, Italy, 4-7 September 2017.
131. Siskos Y, Evangelos G, Nikolaos FM. “UTA methods, multiple criteria decision analysis”. Springer, 315-362, 2016.
132. Karande P, Chakraborty S. “Supplier Selection Using Weighted Utility Additive Method”. Journal of the Institution of Engineers, 96(4), 397-406, 2015.
133. Luo H, Zhao-xu S. “A study on stock ranking and selection strategy based on UTA method under the condition of inconsistence”. In 2014 International Conference on Management Science & Engineering 21th Annual Conference Proceedings, Helsinki, Finland, 17-19 August 2014.
134. Gruca A, Sikora M. “Rule based functional description of genes–estimation of the multicriteria rule ınterestingness measure by the UTA Method.” Biocybernetics And Biomedical Engineering, 33(4), 222-234, 2013.
135. Van ND. “Global maximization of UTA functions in multi-objective optimization”. European Journal of Operational Research, 228(2), 397-404, 2013.
136. Narayan P, Meyer P, Campbell D. “Embedding human expert cognition into autonomous UAS trajectory planning”. IEEE transactions on cybernetics, 43(2), 530-543,2013.
137. Demesouka OE, Vavatsikos AP, Anagnostopoulos K. “Spatial UTA (S-UTA)–A new approach for raster-based GIS multicriteria suitability analysis and its use in implementing natural systems for wastewater treatment”. Journal of environmental management, 125, 41-54, 2013.
138. Spyridakos A.Aggregation of individual preference models in collaborative decision making through disaggregation-aggregation approach with the RACES system. Editors: Ana Respicio, Frada Burstein. Fusing Decision Support Systems into the Fabric of the Context, 241-252, Amsterdam, Netherland, IOS Pres, 2012.
139. Grigoroudis E, Zopounidis C. “Developing an employee evaluation management system: the case of a healthcare organization”. Operational research, 12(1), 83-106, 2012.
140. Gruca A, Sikora M. “Identification of the compound subjective rule interestingness measure for rule-based functional description of genes”. In International Conference on Artificial Intelligence: Methodology, Systems, and Applications Springer, Heidelberg, Berlin, 12-15 September 2012.
141. Bous G, Fortemps P, Glineur F, Pirlot M. “ACUTA: A novel method for eliciting additive value functions on the basis of holistic preference statements”. European Journal of Operational Research, 206(2), 435-444, 2010.
142. Gomes L, Flávio AM, Luís ADR. “ Determining the utility functions of criteria used in the evaluation of real estate.” International Journal of Production Economics. 117(2), 420-426, 2009.
143. Farah M. “Ordinal regression based model for personalized ınformation retrieval”. In Conference on the Theory of Information Retrieval, Heidelberg, Berlin, 09 September 2009.
144. Wang JQ. “Fuzzy Multi-Criteria UTA approach with uncertain ınformation”. Systems Engineering And Electronics. 28(4), 545-550, 2006.
145. Siskos Y, Evangelos G, Nikolaos FM. UTA Methods, Multiple Criteria Decision Analysis. New York, USA Springer Science Business Media, 2005.
146. Walter B, Błażej P.“Multi-Criteria detection of bad smells ın code with UTA method”. International Conference on Extreme Programming and Agile Processes İn Software Engineering Springer, Sheffield, United Kingdom, 18-23 June 2005.
147. Angilella S, Greco S, Lamantia F, Matarazzo B. “Assessing non-additive utility for multicriteria decision aid”. European Journal of Operational Research, 158(3), 734-744, 2004.
148. González-Araya MC, Rangel LAD, Lins MPE, Gomes LFAM. “Building the additive utility functions for CAD-UFRJ evaluation staff criteria”. Annals of Operations Research, 116(1-4), 271-288, 2002.
149. Duckstein L, Treichel W, Magnouni SE. “Ranking ground-water management alternatives by multicriterion analysis”. Journal of Water Resources Planning and Management, 120(4), 546-565, 1994.
150. Stewart TJ. “Pruning of decision alternatives in multiple criteria decision making, based on the UTA method for estimating utilities”. European Journal of Operational Research, 28(1), 79-88, 1987.