How to cite this paper
Er, A., Özkale, C & Coşkun, S. (2024). Project portfolio selection criteria in the oil & gas industry and a decision support tool based on fuzzy Multimoora.Journal of Project Management, 9(3), 197-212.
Refrences
Altuntas, S., Dereli, T., & Yilmaz, M. K. (2015). Evaluation of excavator technologies: application of data fusion based MULTIMOORA methods. Journal of Civil Engineering and Management, 21(8), 977-997. https://doi.org/10.3846/13923730.2015.1064468
Baležentis, A., Baležentis, T., & Brauers, W. K. (2012a). MULTIMOORA-FG: a multi-objective decision-making method for linguistic reasoning with an application to personnel selection. Informatica, 23(2), 173-190. https://doi.org/10.15388/Informatica.2012.355
Baležentis, A., Baležentis, T., & Brauers, W. K. (2012b). Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Systems with applications, 39(9), 7961-7967. https://doi.org/10.1016/j.eswa.2012.01.100
Baležentis, T., & Baležentis, A. (2014). A survey on development and applications of the multi‐criteria decision making method MULTIMOORA. Journal of multi‐criteria decision analysis, 21(3-4), 209-222. https://doi.org/10.1002/mcda.1501
Baležentis, T., & Baležentis, A. (2016). Group decision making procedure based on trapezoidal intuitionistic fuzzy numbers: MULTIMOORA methodology. Economic computation and economic cybernetics studies and research, 50(1), 103-122. https://api.semanticscholar.org/CorpusID:124604168
Bilgin, N. (2014). Sosyal Bilimlerde İçerik Analizi Teknikler ve Örnek Çalışmalar [Content Analysis Techniques and Case Studies in Social Sciences], Siyasal Kitabevi, Ankara, 3.
Brauers, W. K., Baležentis, A., & Baležentis, T. (2011). MULTIMOORA for the EU Member States updated with fuzzy number theory. Technological and Economic Development of Economy, 17(2), 259-290. https://doi.org/10.3846/20294913.2011.580566
Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and cybernetics, 35(2), 445-469. http://eudml.org/doc/209425
Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi‐objective decision‐making for road design. Transport, 23(3), 183-193. https://doi.org/10.3846/1648-4142.2008.23.183-193
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and economic development of economy, 16(1), 5-24. https://doi.org/10.3846/tede.2010.01
Brauers, W. K. M., & Zavadskas, E. K. (2011). MULTIMOORA optimization used to decide on a bank loan to buy property. Technological and economic development of economy, 17(1), 174-188. https://doi.org/10.3846/13928619.2011.560632
Brauers, W. K. M., & Zavadskas, E. K. (2012). Robustness of MULTIMOORA: a method for multi-objective optimization. Informatica, 23(1), 1-25. https://doi.org/10.15388/Informatica.2012.346
Brealey, R.A., & Myers, S.C. (1991). Principles of Corporate Finance. McGraw-Hill, 4.
Carlsson, C., Fuller, R., Heikkilä, M., & Majlender, P. (2007). A fuzzy approach to R&D project portfolio selection. International Journal of Approximate Reasoning, 44(2), 93-105. https://doi.org/10.1016/j.ijar.2006.07.003
Collan, M., & Luukka, P. (2013). 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. https://doi.org/10.1109/TFUZZ.2013.2260758
Ding, C., Ferro, A., Fitzgibbon, T., & Szabat, P. Refining in the energy transition through 2040. Last Modified November 3, 2022. Accessed May 5, 2023. https://www.mckinsey.com/industries/oil-and-gas/our-insights/refining-in-the-energy-transition-through-2040
Doerner, K., Gutjahr, W. J., Hartl, R. F., Strauss, C., & Stummer, C. (2004). Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Annals of operations research, 131, 79-99. https://doi.org/10.1023/B:ANOR.0000039513.99038.c6
Enea, M., & Piazza, T. (2004). Project selection by constrained fuzzy AHP. Fuzzy optimization and decision making, 3, 39-62. https://doi.org/10.1023/B:FODM.0000013071.63614.3d
Ghasemzadeh, F., Archer, N., & Iyogun, P. (1999). A zero-one model for project portfolio selection and scheduling. Journal of the operational Research Society, 50(7), 745-755. https://doi.org/10.1057/palgrave.jors.2600767
Golabi, K., Kirkwood, C. W., & Sicherman, A. (1981). Selecting a portfolio of solar energy projects using multiattribute preference theory. Management Science, 27(2), 174-189. https://www.jstor.org/stable/2631285
Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177. https://doi.org/10.1016/j.inffus.2018.12.002
Hassanzadeh, F., Nemati, H., & Sun, M. (2014). Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection. European Journal of Operational Research, 238(1), 41-53. https://doi.org/10.1016/j.ejor.2014.03.023
Huang, C. C., Chu, P. Y., & Chiang, Y. H. (2008). A fuzzy AHP application in government-sponsored R&D project selection. Omega, 36(6), 1038-1052. https://doi.org/10.1016/j.omega.2006.05.003
Jafarzadeh, H., Akbari, P., & Abedin, B. (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems with Applications, 110, 237-249. https://doi.org/10.1016/j.eswa.2018.05.028
Keeney, R.L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press.
Khalili-Damghani, K., Sadi-Nezhad, S., & Tavana, M. (2013). Solving multi-period project selection problems with fuzzy goal programming based on TOPSIS and a fuzzy preference relation. Information Sciences, 252, 42-61. https://doi.org/10.1016/j.ins.2013.05.005
Koçak, A., & Arun, Ö. (2006). İçerik analizi çalışmalarında örneklem sorunu [Sampling problem in content analysis studies]. Selçuk iletişim, 4(3), 21-28. https://dergipark.org.tr/tr/pub/josc/issue/19013/200754
Kumar, M., Mittal, M. L., Soni, G., & Joshi, D. (2018). A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects. Computers & Industrial Engineering, 119, 121-130. https://doi.org/10.1016/j.cie.2018.03.029
Mohagheghi, V., Mousavi, S. M., Antuchevičienė, J., & Mojtahed, M. (2019). Project portfolio selection problems: a review of models, uncertainty approaches, solution techniques, and case studies. Technological and economic development of economy., 25(6), 1380-1412. https://doi.org/10.3846/tede.2019.11410
Mohagheghi, V., & Mousavi, S. M. (2019). A new framework for high-technology project evaluation and project portfolio selection based on Pythagorean fuzzy WASPAS, MOORA and mathematical modeling. Iranian Journal of Fuzzy Systems, 16(6), 89-106. https://doi.org/10.22111/IJFS.2019.5022
Mohagheghi, V., Mousavi, S. M., Mojtahedi, M., & Newton, S. (2021). Introducing a multi-criteria evaluation method using Pythagorean fuzzy sets: A case study focusing on resilient construction project selection. Kybernetes, 50(1), 118-146. https://doi.org/10.1108/K-04-2019-0225
Mohammadi, A., Omidvar, M. N., & Li, X. (2012, June). Reference point based multi-objective optimization through decomposition. In 2012 IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
Perez, F., & Gomez, T. (2016). Multiobjective project portfolio selection with fuzzy constraints. Annals of Operations Research, 245, 7-29. https://doi.org/10.1007/s10479-014-1556-z
Rabbani, M., Bajestani, M. A., & Khoshkhou, G. B. (2010). A multi-objective particle swarm optimization for project selection problem. Expert systems with applications, 37(1), 315-321. https://doi.org/10.1016/j.eswa.2009.05.056
Relich, M., & Pawlewski, P. (2017). A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing, 231, 19-27. https://doi.org/10.1016/j.neucom.2016.05.104
Daneshvar Rouyendegh, B., & Erol, S. (2012). Selecting the best project using the fuzzy ELECTRE method. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/790142
Shaaban, M., & Scheffran, J. (2017). Selection of sustainable development indicators for the assessment of electricity production in Egypt. Sustainable Energy Technologies and Assessments, 22, 65-73. https://doi.org/10.1016/j.seta.2017.07.003
Song, S., Ang, S., Yang, F., & Xia, Q. (2019). An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information. Expert Systems, 36(5), e12447. https://doi.org/10.1111/exsy.12447
Tavana, M., Keramatpour, M., Santos-Arteaga, F. J., & Ghorbaniane, E. (2015). A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert Systems with Applications, 42(22), 8432-8444. https://doi.org/10.1016/j.eswa.2015.06.057
Wang, J., & Hwang, W. L. (2007). A fuzzy set approach for R&D portfolio selection using a real options valuation model. Omega, 35(3), 247-257.https://doi.org/10.1016/j.omega.2005.06.002
Yan, S., & Ji, X. (2018). Portfolio selection model of oil projects under uncertain environment. Soft Computing, 22, 5725-5734. https://doi.org/10.1007/s00500-017-2619-2
Yang, F., Song, S., Huang, W., & Xia, Q. (2015). SMAA-PO: project portfolio optimization problems based on stochastic multicriteria acceptability analysis. Annals of Operations Research, 233, 535-547. https://doi.org/10.1007/s10479-014-1583-9
Yu, L., Wang, S., Wen, F., & Lai, K. K. (2012). Genetic algorithm-based multi-criteria project portfolio selection. Annals of operations research, 197, 71-86. https://doi.org/10.1007/s10479-010-0819-6
Baležentis, A., Baležentis, T., & Brauers, W. K. (2012a). MULTIMOORA-FG: a multi-objective decision-making method for linguistic reasoning with an application to personnel selection. Informatica, 23(2), 173-190. https://doi.org/10.15388/Informatica.2012.355
Baležentis, A., Baležentis, T., & Brauers, W. K. (2012b). Personnel selection based on computing with words and fuzzy MULTIMOORA. Expert Systems with applications, 39(9), 7961-7967. https://doi.org/10.1016/j.eswa.2012.01.100
Baležentis, T., & Baležentis, A. (2014). A survey on development and applications of the multi‐criteria decision making method MULTIMOORA. Journal of multi‐criteria decision analysis, 21(3-4), 209-222. https://doi.org/10.1002/mcda.1501
Baležentis, T., & Baležentis, A. (2016). Group decision making procedure based on trapezoidal intuitionistic fuzzy numbers: MULTIMOORA methodology. Economic computation and economic cybernetics studies and research, 50(1), 103-122. https://api.semanticscholar.org/CorpusID:124604168
Bilgin, N. (2014). Sosyal Bilimlerde İçerik Analizi Teknikler ve Örnek Çalışmalar [Content Analysis Techniques and Case Studies in Social Sciences], Siyasal Kitabevi, Ankara, 3.
Brauers, W. K., Baležentis, A., & Baležentis, T. (2011). MULTIMOORA for the EU Member States updated with fuzzy number theory. Technological and Economic Development of Economy, 17(2), 259-290. https://doi.org/10.3846/20294913.2011.580566
Brauers, W. K., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and cybernetics, 35(2), 445-469. http://eudml.org/doc/209425
Brauers, W. K. M., Zavadskas, E. K., Peldschus, F., & Turskis, Z. (2008). Multi‐objective decision‐making for road design. Transport, 23(3), 183-193. https://doi.org/10.3846/1648-4142.2008.23.183-193
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and economic development of economy, 16(1), 5-24. https://doi.org/10.3846/tede.2010.01
Brauers, W. K. M., & Zavadskas, E. K. (2011). MULTIMOORA optimization used to decide on a bank loan to buy property. Technological and economic development of economy, 17(1), 174-188. https://doi.org/10.3846/13928619.2011.560632
Brauers, W. K. M., & Zavadskas, E. K. (2012). Robustness of MULTIMOORA: a method for multi-objective optimization. Informatica, 23(1), 1-25. https://doi.org/10.15388/Informatica.2012.346
Brealey, R.A., & Myers, S.C. (1991). Principles of Corporate Finance. McGraw-Hill, 4.
Carlsson, C., Fuller, R., Heikkilä, M., & Majlender, P. (2007). A fuzzy approach to R&D project portfolio selection. International Journal of Approximate Reasoning, 44(2), 93-105. https://doi.org/10.1016/j.ijar.2006.07.003
Collan, M., & Luukka, P. (2013). 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. https://doi.org/10.1109/TFUZZ.2013.2260758
Ding, C., Ferro, A., Fitzgibbon, T., & Szabat, P. Refining in the energy transition through 2040. Last Modified November 3, 2022. Accessed May 5, 2023. https://www.mckinsey.com/industries/oil-and-gas/our-insights/refining-in-the-energy-transition-through-2040
Doerner, K., Gutjahr, W. J., Hartl, R. F., Strauss, C., & Stummer, C. (2004). Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Annals of operations research, 131, 79-99. https://doi.org/10.1023/B:ANOR.0000039513.99038.c6
Enea, M., & Piazza, T. (2004). Project selection by constrained fuzzy AHP. Fuzzy optimization and decision making, 3, 39-62. https://doi.org/10.1023/B:FODM.0000013071.63614.3d
Ghasemzadeh, F., Archer, N., & Iyogun, P. (1999). A zero-one model for project portfolio selection and scheduling. Journal of the operational Research Society, 50(7), 745-755. https://doi.org/10.1057/palgrave.jors.2600767
Golabi, K., Kirkwood, C. W., & Sicherman, A. (1981). Selecting a portfolio of solar energy projects using multiattribute preference theory. Management Science, 27(2), 174-189. https://www.jstor.org/stable/2631285
Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177. https://doi.org/10.1016/j.inffus.2018.12.002
Hassanzadeh, F., Nemati, H., & Sun, M. (2014). Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection. European Journal of Operational Research, 238(1), 41-53. https://doi.org/10.1016/j.ejor.2014.03.023
Huang, C. C., Chu, P. Y., & Chiang, Y. H. (2008). A fuzzy AHP application in government-sponsored R&D project selection. Omega, 36(6), 1038-1052. https://doi.org/10.1016/j.omega.2006.05.003
Jafarzadeh, H., Akbari, P., & Abedin, B. (2018). A methodology for project portfolio selection under criteria prioritisation, uncertainty and projects interdependency–combination of fuzzy QFD and DEA. Expert Systems with Applications, 110, 237-249. https://doi.org/10.1016/j.eswa.2018.05.028
Keeney, R.L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press.
Khalili-Damghani, K., Sadi-Nezhad, S., & Tavana, M. (2013). Solving multi-period project selection problems with fuzzy goal programming based on TOPSIS and a fuzzy preference relation. Information Sciences, 252, 42-61. https://doi.org/10.1016/j.ins.2013.05.005
Koçak, A., & Arun, Ö. (2006). İçerik analizi çalışmalarında örneklem sorunu [Sampling problem in content analysis studies]. Selçuk iletişim, 4(3), 21-28. https://dergipark.org.tr/tr/pub/josc/issue/19013/200754
Kumar, M., Mittal, M. L., Soni, G., & Joshi, D. (2018). A hybrid TLBO-TS algorithm for integrated selection and scheduling of projects. Computers & Industrial Engineering, 119, 121-130. https://doi.org/10.1016/j.cie.2018.03.029
Mohagheghi, V., Mousavi, S. M., Antuchevičienė, J., & Mojtahed, M. (2019). Project portfolio selection problems: a review of models, uncertainty approaches, solution techniques, and case studies. Technological and economic development of economy., 25(6), 1380-1412. https://doi.org/10.3846/tede.2019.11410
Mohagheghi, V., & Mousavi, S. M. (2019). A new framework for high-technology project evaluation and project portfolio selection based on Pythagorean fuzzy WASPAS, MOORA and mathematical modeling. Iranian Journal of Fuzzy Systems, 16(6), 89-106. https://doi.org/10.22111/IJFS.2019.5022
Mohagheghi, V., Mousavi, S. M., Mojtahedi, M., & Newton, S. (2021). Introducing a multi-criteria evaluation method using Pythagorean fuzzy sets: A case study focusing on resilient construction project selection. Kybernetes, 50(1), 118-146. https://doi.org/10.1108/K-04-2019-0225
Mohammadi, A., Omidvar, M. N., & Li, X. (2012, June). Reference point based multi-objective optimization through decomposition. In 2012 IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE.
Perez, F., & Gomez, T. (2016). Multiobjective project portfolio selection with fuzzy constraints. Annals of Operations Research, 245, 7-29. https://doi.org/10.1007/s10479-014-1556-z
Rabbani, M., Bajestani, M. A., & Khoshkhou, G. B. (2010). A multi-objective particle swarm optimization for project selection problem. Expert systems with applications, 37(1), 315-321. https://doi.org/10.1016/j.eswa.2009.05.056
Relich, M., & Pawlewski, P. (2017). A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing, 231, 19-27. https://doi.org/10.1016/j.neucom.2016.05.104
Daneshvar Rouyendegh, B., & Erol, S. (2012). Selecting the best project using the fuzzy ELECTRE method. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/790142
Shaaban, M., & Scheffran, J. (2017). Selection of sustainable development indicators for the assessment of electricity production in Egypt. Sustainable Energy Technologies and Assessments, 22, 65-73. https://doi.org/10.1016/j.seta.2017.07.003
Song, S., Ang, S., Yang, F., & Xia, Q. (2019). An stochastic multiattribute acceptability analysis‐based method for the multiattribute project portfolio selection problem with rank‐level information. Expert Systems, 36(5), e12447. https://doi.org/10.1111/exsy.12447
Tavana, M., Keramatpour, M., Santos-Arteaga, F. J., & Ghorbaniane, E. (2015). A fuzzy hybrid project portfolio selection method using data envelopment analysis, TOPSIS and integer programming. Expert Systems with Applications, 42(22), 8432-8444. https://doi.org/10.1016/j.eswa.2015.06.057
Wang, J., & Hwang, W. L. (2007). A fuzzy set approach for R&D portfolio selection using a real options valuation model. Omega, 35(3), 247-257.https://doi.org/10.1016/j.omega.2005.06.002
Yan, S., & Ji, X. (2018). Portfolio selection model of oil projects under uncertain environment. Soft Computing, 22, 5725-5734. https://doi.org/10.1007/s00500-017-2619-2
Yang, F., Song, S., Huang, W., & Xia, Q. (2015). SMAA-PO: project portfolio optimization problems based on stochastic multicriteria acceptability analysis. Annals of Operations Research, 233, 535-547. https://doi.org/10.1007/s10479-014-1583-9
Yu, L., Wang, S., Wen, F., & Lai, K. K. (2012). Genetic algorithm-based multi-criteria project portfolio selection. Annals of operations research, 197, 71-86. https://doi.org/10.1007/s10479-010-0819-6