Distinguishing coefficient driven sensitivity analysis of GRA model for intelligent decisions: application in project management
Abstract
The Distinguishing Coefficient (ξ) is an important parameter of Grey Relational Analysis (GRA), a flagship multi-criteria decision making (MCDM) model of Grey System Theory, an intelligent and multifaceted field developed by Chinese scientists in 1980s. However, the scholars widely assume ξ = 0.5. The current study questions this practice. Also, some scholars have argued that the variation in ξ doesn’t influence the ranking of the factors through GRA. On contrary, the study demonstrates, the variation in ξ can influence the ranking. This has been shown through a case involving primary data concerning the perceived relative importance of Project Management Knowledge Areas (PMKAs). This study is significant for the analysts of uncertain systems, represented by grey or fuzzy systems, who intend to use GRA for intelligent multi-criteria decision making. It encourages ξ – driven sensitivity analysis of GRA model before interpreting the results. The study reveals, by tailoring the value of ξ a point can be achieved where the ranking obtained through GRA can be made most comparable to the other MCDM methods. For comparative analysis of the GRA based results the study deployed three other MCDM techniques; Analytic Hierarchy Process, Best Worst Method and Simple Additive Weighting.
Keyword : project management, knowledge areas, Grey Relational Analysis GRA, Simple Additive Weighing SAW, Analytic Hierarchy Process AHP, Best Worst Method BWM
How to Cite
Amin Mahmoudi, Javed, S. A. ., Liu, S. ., & Deng, X. . (2020). Distinguishing coefficient driven sensitivity analysis of GRA model for intelligent decisions: application in project management. Technological and Economic Development of Economy, 26(3), 621-641. https://doi.org/10.3846/tede.2020.11890
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abhang, L. B., & Hameedullah, M. (2012). Determination of optimum parameters for multi-performance characteristics in turning by using grey relational analysis. International Journal of Advanced Manufacturing Technology, 63(1–4), 13–24. https://doi.org/10.1007/s00170-011-3857-6
Afshari, A., Mojahed, M., & Yusuff, R. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511–515.
Ahmed, S., Vedagiri, P., & Krishna Rao, K. V. (2017). Prioritization of pavement maintenance sections using objective based Analytic Hierarchy Process. International Journal of Pavement Research and Technology, 10(2), 158–170. https://doi.org/10.1016/j.ijprt.2017.01.001
Ahn, B. S. (2017). The analytic hierarchy process with interval preference statements. Omega, 67, 177–185 (United Kingdom). https://doi.org/10.1016/j.omega.2016.05.004
Bakker, R. M. (2010). Taking stock of temporary organizational forms: A systematic review and research agenda. International Journal of Management Reviews, 12(4), 466–486. https://doi.org/10.1111/j.1468-2370.2010.00281.x
Bourgault, M., Drouin, N., & Hamel, E. (2008). Decision making within distributed project teams: An exploration of formalization and autonomy as determinants of success. Project Management Journal, 39(1 suppl), S97–S110. https://doi.org/10.1002/pmj.20063
Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132–145. https://doi.org/10.1016/j.ejor.2007.05.006
Crawford, L., & Pollack, J. (2007). How generic are project management knowledge and practice? Project Management Journal, 38(1), 87–96. https://doi.org/10.1177/875697280703800109
Deng, J. (1989). Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1–24.
Drouin, N., Müller, R., & Sankaran, S. (2016). Novel approaches to organizational project management research: Translational and transformational. Project Management Journal, 47(1), e2–e2. https://doi.org/10.1002/pmj.21567
Dumrak, J., Baroudi, B., & Hadjinicolaou, N. (2017). Exploring the Association between Project Management Knowledge Areas and Sustainable Outcomes. Procedia Engineering, 182, 157–164. https://doi.org/10.1016/j.proeng.2017.03.152
Eastham, J., Tucker, D. J., Varma, S., & Sutton, S. M. (2014). PLM software selection model for project management using hierarchical decision modeling with criteria from PMBOK® knowledge areas. Engineering Management Journal, 26(3), 13–24. https://doi.org/10.1080/10429247.2014.11432016
Floricel, S., Michela, J. L., & Piperca, S. (2016). Complexity, uncertainty-reduction strategies, and project performance. International Journal of Project Management, 34(7), 1360–1383. https://doi.org/10.1016/j.ijproman.2015.11.007
Garel, G. (2013). A history of project management models: From pre-models to the standard models. International Journal of Project Management, 31(5), 663–669. https://doi.org/10.1016/j.ijproman.2012.12.011
Haeri, S. A. S., & Rezaei, J. (2019). A grey-based green supplier selection model for uncertain environments. Journal of Cleaner Production, 221, 768–784. https://doi.org/10.1016/j.jclepro.2019.02.193
He, Q., Luo, L., Hu, Y., & Chan, A. P. C. (2015). Measuring the complexity of mega construction projects in China – A fuzzy analytic network process analysis. International Journal of Project Management, 33(3), 549–563. https://doi.org/10.1016/j.ijproman.2014.07.009
Hietajärvi, A. M., & Aaltonen, K. (2018). The formation of a collaborative project identity in an infrastructure alliance project. Construction Management and Economics, 36(1), 1–21. https://doi.org/10.1080/01446193.2017.1315149
Hwang, B. G., & Ng, W. J. (2013). Project management knowledge and skills for green construction: Overcoming challenges. International Journal of Project Management, 31(2), 272–284. https://doi.org/10.1016/j.ijproman.2012.05.004
Javed, S., Javed, S., & Sajid, A. S. (2015). Assessing the managerial perception of relative significance of ten Knowledge Areas on project success – A case from Pakistan. Journal of Management and Science, 5(3), 1–18.
Javed, S. A. (2019). A novel research on Grey Incidence Analysis models and its application in Project Management (Doctoral dissertation). Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
Javed, S. A., & Liu, S. (2017). Evaluation of project management knowledge areas using grey incidence model and AHP. In 2017 International Conference on Grey Systems and Intelligent Services (GSIS) (pp. 120–120). IEEE. Stockholm, Sweden. https://doi.org/10.1109/GSIS.2017.8077684
Javed, S. A., & Liu, S. (2019). Bidirectional Absolute GRA/GIA model for Uncertain Systems: Application in Project Management. IEEE Access, 7, 60885–60896. https://doi.org/10.1109/ACCESS.2019.2904632
Javed, S. A., Khan, A. M., Dong, W., Raza, A., & Liu, S. (2019c). Systems evaluation through new Grey Relational Analysis approach: An application on thermal conductivity – Petrophysical parameters’ relationships. Processes, 7(6), 348. https://doi.org/10.3390/pr7060348
Javed, S. A., Mahmoudi, A., Khan, A. M., Javed, S., & Liu, S. (2018a). A critical review: Shape optimization of welded plate heat exchangers based on grey correlation theory. Applied Thermal Engineering, 144, 593–599. https://doi.org/10.1016/j.applthermaleng.2018.08.086
Javed, S. A., Syed, A. M., & Javed, S. (2018b). Perceived organizational performance and trust in project manager and top management in project-based organizations. Grey Systems: Theory and Application, 8(3), 230–245. https://doi.org/10.1108/GS-01-2018-0009
Jiang, B. C., Tasi, S. L., & Wang, C. C. (2002). Machine vision-based gray relational theory applied to IC marking inspection. IEEE Transactions on Semiconductor Manufacturing, 15(4), 531–539. https://doi.org/10.1109/TSM.2002.804906
Kaganer, E., Carmel, E., Hirscheim, R., & Olsen, T. (2013). Managing the human cloud. MIT Sloan Management Review, 54(2), 22–32. https://sloanreview.mit.edu/wp-content/uploads/2012/12/26e19f5086.pdf
Kerzner, H. (2014). Project management best practices: Achieving global excellence (4th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118835531
Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). John Wiley & Sons. 848 p. https://www.wiley.com/en-us/Project+Management%3A+A+Systems+Approach+to+Planning%2C+Scheduling%2C+and+Controlling%2C+12th+Edition-p-9781119165354
Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567–576. https://doi.org/10.1016/j.ecolind.2016.07.032
Kuo, Y., Yang, T., & Huang, G.-W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & Industrial Engineering, 55(1), 80–93. https://doi.org/10.1016/j.cie.2007.12.002
Li, B. J., Hu, L. P., He, C. H., & Li, Y. H. (2011). Dynamical analysis on influencing factors of grain production in Henan Province based on grey systems theory. In Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services, GSIS’11 – Joint with the 15th WOSC International Congress on Cybernetics and Systems (pp. 106–110). Nanjing, China. https://doi.org/10.1109/GSIS.2011.6044017
Liang, D., Darko, A. P., & Xu, Z. (2019). Pythagorean fuzzy partitioned geometric Bonferroni mean and its application to multi-criteria group decision making with grey relational analysis. International Journal of Fuzzy Systems, 21, 115–128. https://doi.org/10.1007/s40815-018-0544-x
Liang, D., Kobina, A., & Quan, W. (2018). Grey relational analysis method for probabilistic linguistic multi-criteria group decision-making based on geometric Bonferroni mean. International Journal of Fuzzy Systems, 20, 2234–2244. https://doi.org/10.1007/s40815-017-0374-2
Liao, H. C., Yang, L. Y., & Xu, Z. S. (2018). Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Applied Soft Computing Journal, 63, 223–234. https://doi.org/10.1016/j.asoc.2017.11.049
Liao, H., & Xu, Z. (2013). A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 12, 373–392. https://doi.org/10.1007/s10700-013-9162-0
Liao, H., & Xu, Z. (2017). Hesitant fuzzy decision making methodologies and applications. Springer Singapore. https://doi.org/10.1007/978-981-10-3265-3
Liao, H., Jiang, L., Xu, Z., Xu, J., & Herrera, F. (2017). A linear programming method for multiple criteria decision making with probabilistic linguistic information. Information Sciences, 415–416, 341–355. https://doi.org/10.1016/j.ins.2017.06.035
Liao, H., Xu, Z., & Zeng, X. J. (2015). Novel correlation coefficients between hesitant fuzzy sets and their application in decision making. Knowledge-Based Systems, 82, 115–127. https://doi.org/10.1016/j.knosys.2015.02.020
Liu, S., & Lin, Y. (2010). Grey systems: Theory and applications. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-16158-2
Liu, S., Yang, Y., & Forrest, J. (2017). Grey data analysis: Methods, models and applications. Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-1841-1
MacCrimmon, K. R. (1968). Decisionmaking among multiple–attribute alternatives: A Survey and consolidated approach. Arpa Order.
Mahmoudi, A., & Feylizadeh, M. R. (2018). A grey mathematical model for crashing of projects by considering time, cost, quality, risk and law of diminishing returns. Grey Systems: Theory and Application, 8(3), 272–294. https://doi.org/10.1108/GS-12-2017-0042
Mahmoudi, A., Bagherpour, M., & Javed, S. A. (2019b). Grey earned value management: Theory and applications. IEEE Transactions on Engineering Management, 1–19 (in press). https://doi.org/10.1109/TEM.2019.2920904
Mahmoudi, A., Liu, S. F., Javed, S. A., & Abbasi, M. (2019a). A novel method of solving linear programming with grey parameters. Journal of Intelligent & Fuzzy Systems, 36(1), 161–172. https://doi.org/10.3233/JIFS-181071
Mesquida, A.-L., & Mas, A. (2014). A project management improvement program according to ISO/ IEC 29110 and PMBOK ®. Journal of Software: Evolution and Process, 26(9), 846–854. https://doi.org/10.1002/smr.1665
Mi, X., & Liao, H. (2019). An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method. Computers & Industrial Engineering, 133, 83–94. https://doi.org/10.1016/j.cie.2019.05.004
Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87, 205–225. https://doi.org/10.1016/j.omega.2019.01.009
Nguyen, L. D., Chih, Y.-Y., & García de Soto, B. (2016). Knowledge areas delivered in project management programs: Exploratory study. Journal of Management in Engineering, 33(1). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000473
Oun, T. A., Blackburn, T. D., Olson, B. A., & Blessner, P. (2016). An enterprise-wide knowledge management approach to project management. Engineering Management Journal, 28(3), 179–192. https://doi.org/10.1080/10429247.2016.1203715
Padalkar, M., & Gopinath, S. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34(7), 1305–1321. https://doi.org/10.1016/j.ijproman.2016.06.006
Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK® guide). https://doi.org/10.1002/pmj.21345
Project Management Institute. (2017). Project management body of knowledge: A guide to the project management body of knowledge. https://doi.org/10.1002/pmj.20125
Quartey-Papafio, T. K., Liu, S., & Javed, S. (2019). Grey relational evaluation of impact and control of malaria in Sub-Saharan Africa. Grey Systems: Theory and Application, 9(4), 415–431. https://doi.org/10.1108/GS-06-2019-0020
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 109(3), 1911–1938. https://doi.org/10.1016/j.omega.2015.12.001
Rocha, L., Tereso, A., & Couto, J. P. (2015). Project management: evaluation of the problems in the Portuguese construction industry. In A. Rocha, A. Correia, S. Costanzo, & L. Reis (Eds.), New contributions in information systems and technologies. Advances in intelligent systems and computing (Vol. 353, pp. 69–78). Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_7
Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841–855. https://doi.org/10.1287/mnsc.32.7.841
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1). https://doi.org/10.1504/IJSSCI.2008.017590
Salet, W., Bertolini, L., & Giezen, M. (2013). Complexity and uncertainty: Problem or asset in decision making of mega infrastructure projects? International Journal of Urban and Regional Research, 37(6), 1984–2000. https://doi.org/10.1111/j.1468-2427.2012.01133.x
Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and Program Planning, 66, 147–155. https://doi.org/10.1016/j.evalprogplan.2017.10.002
Sallehuddin, R., Shamsuddin, S. M. Hj., & Hashim, S. Z. M. (2008). Application of grey relational analysis for multivariate time series. In 2008 8th International Conference on Intelligent Systems Design and Applications. Kaohsiung, Taiwan. https://doi.org/10.1109/ISDA.2008.181
Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A fuzzy AHP–TOPSIS-based group decision-making approach to it personnel selection. International Journal of Fuzzy Systems, 20, 1576–1591. https://doi.org/10.1007/s40815-018-0474-7
Serra, C. E. M. (2017). Benefits realization management: Strategic value from portfolios, programs, and projects. Boca Raton: Taylor & Francis Group. https://www.worldcat.org/title/benefits-realizationmanagement-strategic-value-from-portfolios-programs-and-projects/oclc/956583875
Shabbir, R., & Ahmad, S. S. (2016). Water resource vulnerability assessment in Rawalpindi and Islamabad, Pakistan using Analytic Hierarchy Process (AHP). Journal of King Saud University – Science, 28(4), 293–299. https://doi.org/10.1016/j.jksus.2015.09.007
Shaverdi, M., Ramezani, I., Tahmasebi, R., & Rostamy, A. A. A. (2016). Combining fuzzy AHP and fuzzy TOPSIS with financial ratios to design a novel performance evaluation model. International Journal of Fuzzy Systems, 18(2), 248–262. https://doi.org/10.1007/s40815-016-0142-8
Song, Q., & Shepperd, M. (2011). Predicting software project effort: A grey relational analysis based method. Expert Systems with Applications, 38(6), 7302–7316. https://doi.org/10.1016/j.eswa.2010.12.005
Taha, H. A. (2014). Operations research – An introduction (Chapter 5, 9th ed.). Pearson Education, Inc.
Vanhoucke, M. (2013). Project management with dynamic scheduling – Baseline scheduling, risk analysis and project control (2nd ed.). Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40438-2_5
Winter, M., Smith, C., Morris, P., & Cicmil, S. (2006). Directions for future research in project management: The main findings of a UK government-funded research network. International Journal of Project Management, 24(8), 638–649. https://doi.org/10.1016/j.ijproman.2006.08.009
Wu, H., Xu, Z., Ren, P., & Liao, H. (2018). Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Computers and Industrial Engineering, 115, 449–458. https://doi.org/10.1016/j.cie.2017.11.023
Wu, L. F., Liu, S. F., Yao, L. G., & Yan, S. L. (2013). Grey convex relational degree and its application to evaluate regional economic sustainability. Scientia Iranica, 20(1), 44–49. https://doi.org/10.1016/j.scient.2012.11.002
Wu, W. Y., & Chen, S. P. (2005). A prediction method using the grey model GMC (1, n) combined with the grey relational analysis: A case study on Internet access population forecast. Applied Mathematics and Computation, 169(1), 198–217. https://doi.org/10.1016/j.amc.2004.10.087
Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems, 22(4), 749–761. https://doi.org/10.1109/TFUZZ.2013.2272585
Yang, X., Xu, Z., & Liao, H. (2017). Correlation coefficients of hesitant multiplicative sets and their applications in decision making and clustering analysis. Applied Soft Computing Journal, 61, 935–946. https://doi.org/10.1016/j.asoc.2017.08.011
Yue, H. (2009). Grey absolute degree of incidence analysis of citation indicators of management academic journals. In 3rd International Symposium on Intelligent Information Technology Application, IITA 2009 (pp. 19–22). Shanghai, China. IEEE. https://doi.org/10.1109/IITA.2009.258
Zahedi, F. (1986). The analytic hierarchy process – A survey of the method and its applications. INFORMS Journal on Applied Analytics, 16(4). https://doi.org/10.1287/inte.16.4.96
Zavadskas, E. K., Turskis, Z., & Tamošaitienė, J. (2008). Multicriteria selection of project managers by applying grey criteria. Technological and Economic Development of Economy, 14(4), 462–477. https://doi.org/10.3846/1392-8619.2008.14.462-477
Zavadskas, E. K., Vilutienė, T., Turskis, Z., & Tamosaitienė, J. (2010). Contractor selection for construction works by applying saw‐g and topsis grey techniques. Journal of Business Economics and Management, 11(1), 34–55. https://doi.org/10.3846/jbem.2010.03
Zhang, K., Ye, W., & Zhao, L. (2012). The absolute degree of grey incidence for grey sequence base on standard grey interval number operation. Kybernetes, 41(7/8), 934–944. https://doi.org/10.1108/03684921211257784
Zwikael, O. (2009). The relative importance of the PMBOK® guide’s nine knowledge areas during project planning. Project Management Journal, 40(4), 94–103. https://doi.org/10.1002/pmj.20116
Afshari, A., Mojahed, M., & Yusuff, R. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511–515.
Ahmed, S., Vedagiri, P., & Krishna Rao, K. V. (2017). Prioritization of pavement maintenance sections using objective based Analytic Hierarchy Process. International Journal of Pavement Research and Technology, 10(2), 158–170. https://doi.org/10.1016/j.ijprt.2017.01.001
Ahn, B. S. (2017). The analytic hierarchy process with interval preference statements. Omega, 67, 177–185 (United Kingdom). https://doi.org/10.1016/j.omega.2016.05.004
Bakker, R. M. (2010). Taking stock of temporary organizational forms: A systematic review and research agenda. International Journal of Management Reviews, 12(4), 466–486. https://doi.org/10.1111/j.1468-2370.2010.00281.x
Bourgault, M., Drouin, N., & Hamel, E. (2008). Decision making within distributed project teams: An exploration of formalization and autonomy as determinants of success. Project Management Journal, 39(1 suppl), S97–S110. https://doi.org/10.1002/pmj.20063
Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132–145. https://doi.org/10.1016/j.ejor.2007.05.006
Crawford, L., & Pollack, J. (2007). How generic are project management knowledge and practice? Project Management Journal, 38(1), 87–96. https://doi.org/10.1177/875697280703800109
Deng, J. (1989). Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1–24.
Drouin, N., Müller, R., & Sankaran, S. (2016). Novel approaches to organizational project management research: Translational and transformational. Project Management Journal, 47(1), e2–e2. https://doi.org/10.1002/pmj.21567
Dumrak, J., Baroudi, B., & Hadjinicolaou, N. (2017). Exploring the Association between Project Management Knowledge Areas and Sustainable Outcomes. Procedia Engineering, 182, 157–164. https://doi.org/10.1016/j.proeng.2017.03.152
Eastham, J., Tucker, D. J., Varma, S., & Sutton, S. M. (2014). PLM software selection model for project management using hierarchical decision modeling with criteria from PMBOK® knowledge areas. Engineering Management Journal, 26(3), 13–24. https://doi.org/10.1080/10429247.2014.11432016
Floricel, S., Michela, J. L., & Piperca, S. (2016). Complexity, uncertainty-reduction strategies, and project performance. International Journal of Project Management, 34(7), 1360–1383. https://doi.org/10.1016/j.ijproman.2015.11.007
Garel, G. (2013). A history of project management models: From pre-models to the standard models. International Journal of Project Management, 31(5), 663–669. https://doi.org/10.1016/j.ijproman.2012.12.011
Haeri, S. A. S., & Rezaei, J. (2019). A grey-based green supplier selection model for uncertain environments. Journal of Cleaner Production, 221, 768–784. https://doi.org/10.1016/j.jclepro.2019.02.193
He, Q., Luo, L., Hu, Y., & Chan, A. P. C. (2015). Measuring the complexity of mega construction projects in China – A fuzzy analytic network process analysis. International Journal of Project Management, 33(3), 549–563. https://doi.org/10.1016/j.ijproman.2014.07.009
Hietajärvi, A. M., & Aaltonen, K. (2018). The formation of a collaborative project identity in an infrastructure alliance project. Construction Management and Economics, 36(1), 1–21. https://doi.org/10.1080/01446193.2017.1315149
Hwang, B. G., & Ng, W. J. (2013). Project management knowledge and skills for green construction: Overcoming challenges. International Journal of Project Management, 31(2), 272–284. https://doi.org/10.1016/j.ijproman.2012.05.004
Javed, S., Javed, S., & Sajid, A. S. (2015). Assessing the managerial perception of relative significance of ten Knowledge Areas on project success – A case from Pakistan. Journal of Management and Science, 5(3), 1–18.
Javed, S. A. (2019). A novel research on Grey Incidence Analysis models and its application in Project Management (Doctoral dissertation). Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.
Javed, S. A., & Liu, S. (2017). Evaluation of project management knowledge areas using grey incidence model and AHP. In 2017 International Conference on Grey Systems and Intelligent Services (GSIS) (pp. 120–120). IEEE. Stockholm, Sweden. https://doi.org/10.1109/GSIS.2017.8077684
Javed, S. A., & Liu, S. (2019). Bidirectional Absolute GRA/GIA model for Uncertain Systems: Application in Project Management. IEEE Access, 7, 60885–60896. https://doi.org/10.1109/ACCESS.2019.2904632
Javed, S. A., Khan, A. M., Dong, W., Raza, A., & Liu, S. (2019c). Systems evaluation through new Grey Relational Analysis approach: An application on thermal conductivity – Petrophysical parameters’ relationships. Processes, 7(6), 348. https://doi.org/10.3390/pr7060348
Javed, S. A., Mahmoudi, A., Khan, A. M., Javed, S., & Liu, S. (2018a). A critical review: Shape optimization of welded plate heat exchangers based on grey correlation theory. Applied Thermal Engineering, 144, 593–599. https://doi.org/10.1016/j.applthermaleng.2018.08.086
Javed, S. A., Syed, A. M., & Javed, S. (2018b). Perceived organizational performance and trust in project manager and top management in project-based organizations. Grey Systems: Theory and Application, 8(3), 230–245. https://doi.org/10.1108/GS-01-2018-0009
Jiang, B. C., Tasi, S. L., & Wang, C. C. (2002). Machine vision-based gray relational theory applied to IC marking inspection. IEEE Transactions on Semiconductor Manufacturing, 15(4), 531–539. https://doi.org/10.1109/TSM.2002.804906
Kaganer, E., Carmel, E., Hirscheim, R., & Olsen, T. (2013). Managing the human cloud. MIT Sloan Management Review, 54(2), 22–32. https://sloanreview.mit.edu/wp-content/uploads/2012/12/26e19f5086.pdf
Kerzner, H. (2014). Project management best practices: Achieving global excellence (4th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118835531
Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). John Wiley & Sons. 848 p. https://www.wiley.com/en-us/Project+Management%3A+A+Systems+Approach+to+Planning%2C+Scheduling%2C+and+Controlling%2C+12th+Edition-p-9781119165354
Khalil, N., Kamaruzzaman, S. N., & Baharum, M. R. (2016). Ranking the indicators of building performance and the users’ risk via Analytical Hierarchy Process (AHP): Case of Malaysia. Ecological Indicators, 71, 567–576. https://doi.org/10.1016/j.ecolind.2016.07.032
Kuo, Y., Yang, T., & Huang, G.-W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & Industrial Engineering, 55(1), 80–93. https://doi.org/10.1016/j.cie.2007.12.002
Li, B. J., Hu, L. P., He, C. H., & Li, Y. H. (2011). Dynamical analysis on influencing factors of grain production in Henan Province based on grey systems theory. In Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services, GSIS’11 – Joint with the 15th WOSC International Congress on Cybernetics and Systems (pp. 106–110). Nanjing, China. https://doi.org/10.1109/GSIS.2011.6044017
Liang, D., Darko, A. P., & Xu, Z. (2019). Pythagorean fuzzy partitioned geometric Bonferroni mean and its application to multi-criteria group decision making with grey relational analysis. International Journal of Fuzzy Systems, 21, 115–128. https://doi.org/10.1007/s40815-018-0544-x
Liang, D., Kobina, A., & Quan, W. (2018). Grey relational analysis method for probabilistic linguistic multi-criteria group decision-making based on geometric Bonferroni mean. International Journal of Fuzzy Systems, 20, 2234–2244. https://doi.org/10.1007/s40815-017-0374-2
Liao, H. C., Yang, L. Y., & Xu, Z. S. (2018). Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Applied Soft Computing Journal, 63, 223–234. https://doi.org/10.1016/j.asoc.2017.11.049
Liao, H., & Xu, Z. (2013). A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 12, 373–392. https://doi.org/10.1007/s10700-013-9162-0
Liao, H., & Xu, Z. (2017). Hesitant fuzzy decision making methodologies and applications. Springer Singapore. https://doi.org/10.1007/978-981-10-3265-3
Liao, H., Jiang, L., Xu, Z., Xu, J., & Herrera, F. (2017). A linear programming method for multiple criteria decision making with probabilistic linguistic information. Information Sciences, 415–416, 341–355. https://doi.org/10.1016/j.ins.2017.06.035
Liao, H., Xu, Z., & Zeng, X. J. (2015). Novel correlation coefficients between hesitant fuzzy sets and their application in decision making. Knowledge-Based Systems, 82, 115–127. https://doi.org/10.1016/j.knosys.2015.02.020
Liu, S., & Lin, Y. (2010). Grey systems: Theory and applications. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-16158-2
Liu, S., Yang, Y., & Forrest, J. (2017). Grey data analysis: Methods, models and applications. Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-1841-1
MacCrimmon, K. R. (1968). Decisionmaking among multiple–attribute alternatives: A Survey and consolidated approach. Arpa Order.
Mahmoudi, A., & Feylizadeh, M. R. (2018). A grey mathematical model for crashing of projects by considering time, cost, quality, risk and law of diminishing returns. Grey Systems: Theory and Application, 8(3), 272–294. https://doi.org/10.1108/GS-12-2017-0042
Mahmoudi, A., Bagherpour, M., & Javed, S. A. (2019b). Grey earned value management: Theory and applications. IEEE Transactions on Engineering Management, 1–19 (in press). https://doi.org/10.1109/TEM.2019.2920904
Mahmoudi, A., Liu, S. F., Javed, S. A., & Abbasi, M. (2019a). A novel method of solving linear programming with grey parameters. Journal of Intelligent & Fuzzy Systems, 36(1), 161–172. https://doi.org/10.3233/JIFS-181071
Mesquida, A.-L., & Mas, A. (2014). A project management improvement program according to ISO/ IEC 29110 and PMBOK ®. Journal of Software: Evolution and Process, 26(9), 846–854. https://doi.org/10.1002/smr.1665
Mi, X., & Liao, H. (2019). An integrated approach to multiple criteria decision making based on the average solution and normalized weights of criteria deduced by the hesitant fuzzy best worst method. Computers & Industrial Engineering, 133, 83–94. https://doi.org/10.1016/j.cie.2019.05.004
Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87, 205–225. https://doi.org/10.1016/j.omega.2019.01.009
Nguyen, L. D., Chih, Y.-Y., & García de Soto, B. (2016). Knowledge areas delivered in project management programs: Exploratory study. Journal of Management in Engineering, 33(1). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000473
Oun, T. A., Blackburn, T. D., Olson, B. A., & Blessner, P. (2016). An enterprise-wide knowledge management approach to project management. Engineering Management Journal, 28(3), 179–192. https://doi.org/10.1080/10429247.2016.1203715
Padalkar, M., & Gopinath, S. (2016). Six decades of project management research: Thematic trends and future opportunities. International Journal of Project Management, 34(7), 1305–1321. https://doi.org/10.1016/j.ijproman.2016.06.006
Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK® guide). https://doi.org/10.1002/pmj.21345
Project Management Institute. (2017). Project management body of knowledge: A guide to the project management body of knowledge. https://doi.org/10.1002/pmj.20125
Quartey-Papafio, T. K., Liu, S., & Javed, S. (2019). Grey relational evaluation of impact and control of malaria in Sub-Saharan Africa. Grey Systems: Theory and Application, 9(4), 415–431. https://doi.org/10.1108/GS-06-2019-0020
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 109(3), 1911–1938. https://doi.org/10.1016/j.omega.2015.12.001
Rocha, L., Tereso, A., & Couto, J. P. (2015). Project management: evaluation of the problems in the Portuguese construction industry. In A. Rocha, A. Correia, S. Costanzo, & L. Reis (Eds.), New contributions in information systems and technologies. Advances in intelligent systems and computing (Vol. 353, pp. 69–78). Springer, Cham. https://doi.org/10.1007/978-3-319-16486-1_7
Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841–855. https://doi.org/10.1287/mnsc.32.7.841
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1). https://doi.org/10.1504/IJSSCI.2008.017590
Salet, W., Bertolini, L., & Giezen, M. (2013). Complexity and uncertainty: Problem or asset in decision making of mega infrastructure projects? International Journal of Urban and Regional Research, 37(6), 1984–2000. https://doi.org/10.1111/j.1468-2427.2012.01133.x
Salimi, N., & Rezaei, J. (2018). Evaluating firms’ R&D performance using best worst method. Evaluation and Program Planning, 66, 147–155. https://doi.org/10.1016/j.evalprogplan.2017.10.002
Sallehuddin, R., Shamsuddin, S. M. Hj., & Hashim, S. Z. M. (2008). Application of grey relational analysis for multivariate time series. In 2008 8th International Conference on Intelligent Systems Design and Applications. Kaohsiung, Taiwan. https://doi.org/10.1109/ISDA.2008.181
Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A fuzzy AHP–TOPSIS-based group decision-making approach to it personnel selection. International Journal of Fuzzy Systems, 20, 1576–1591. https://doi.org/10.1007/s40815-018-0474-7
Serra, C. E. M. (2017). Benefits realization management: Strategic value from portfolios, programs, and projects. Boca Raton: Taylor & Francis Group. https://www.worldcat.org/title/benefits-realizationmanagement-strategic-value-from-portfolios-programs-and-projects/oclc/956583875
Shabbir, R., & Ahmad, S. S. (2016). Water resource vulnerability assessment in Rawalpindi and Islamabad, Pakistan using Analytic Hierarchy Process (AHP). Journal of King Saud University – Science, 28(4), 293–299. https://doi.org/10.1016/j.jksus.2015.09.007
Shaverdi, M., Ramezani, I., Tahmasebi, R., & Rostamy, A. A. A. (2016). Combining fuzzy AHP and fuzzy TOPSIS with financial ratios to design a novel performance evaluation model. International Journal of Fuzzy Systems, 18(2), 248–262. https://doi.org/10.1007/s40815-016-0142-8
Song, Q., & Shepperd, M. (2011). Predicting software project effort: A grey relational analysis based method. Expert Systems with Applications, 38(6), 7302–7316. https://doi.org/10.1016/j.eswa.2010.12.005
Taha, H. A. (2014). Operations research – An introduction (Chapter 5, 9th ed.). Pearson Education, Inc.
Vanhoucke, M. (2013). Project management with dynamic scheduling – Baseline scheduling, risk analysis and project control (2nd ed.). Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40438-2_5
Winter, M., Smith, C., Morris, P., & Cicmil, S. (2006). Directions for future research in project management: The main findings of a UK government-funded research network. International Journal of Project Management, 24(8), 638–649. https://doi.org/10.1016/j.ijproman.2006.08.009
Wu, H., Xu, Z., Ren, P., & Liao, H. (2018). Hesitant fuzzy linguistic projection model to multi-criteria decision making for hospital decision support systems. Computers and Industrial Engineering, 115, 449–458. https://doi.org/10.1016/j.cie.2017.11.023
Wu, L. F., Liu, S. F., Yao, L. G., & Yan, S. L. (2013). Grey convex relational degree and its application to evaluate regional economic sustainability. Scientia Iranica, 20(1), 44–49. https://doi.org/10.1016/j.scient.2012.11.002
Wu, W. Y., & Chen, S. P. (2005). A prediction method using the grey model GMC (1, n) combined with the grey relational analysis: A case study on Internet access population forecast. Applied Mathematics and Computation, 169(1), 198–217. https://doi.org/10.1016/j.amc.2004.10.087
Xu, Z., & Liao, H. (2014). Intuitionistic fuzzy analytic hierarchy process. IEEE Transactions on Fuzzy Systems, 22(4), 749–761. https://doi.org/10.1109/TFUZZ.2013.2272585
Yang, X., Xu, Z., & Liao, H. (2017). Correlation coefficients of hesitant multiplicative sets and their applications in decision making and clustering analysis. Applied Soft Computing Journal, 61, 935–946. https://doi.org/10.1016/j.asoc.2017.08.011
Yue, H. (2009). Grey absolute degree of incidence analysis of citation indicators of management academic journals. In 3rd International Symposium on Intelligent Information Technology Application, IITA 2009 (pp. 19–22). Shanghai, China. IEEE. https://doi.org/10.1109/IITA.2009.258
Zahedi, F. (1986). The analytic hierarchy process – A survey of the method and its applications. INFORMS Journal on Applied Analytics, 16(4). https://doi.org/10.1287/inte.16.4.96
Zavadskas, E. K., Turskis, Z., & Tamošaitienė, J. (2008). Multicriteria selection of project managers by applying grey criteria. Technological and Economic Development of Economy, 14(4), 462–477. https://doi.org/10.3846/1392-8619.2008.14.462-477
Zavadskas, E. K., Vilutienė, T., Turskis, Z., & Tamosaitienė, J. (2010). Contractor selection for construction works by applying saw‐g and topsis grey techniques. Journal of Business Economics and Management, 11(1), 34–55. https://doi.org/10.3846/jbem.2010.03
Zhang, K., Ye, W., & Zhao, L. (2012). The absolute degree of grey incidence for grey sequence base on standard grey interval number operation. Kybernetes, 41(7/8), 934–944. https://doi.org/10.1108/03684921211257784
Zwikael, O. (2009). The relative importance of the PMBOK® guide’s nine knowledge areas during project planning. Project Management Journal, 40(4), 94–103. https://doi.org/10.1002/pmj.20116