Evaluation Based on the Distance from the Average Solution Approach: A Derivative Model for Evaluating and Selecting a Construction Manager

Thanh Phan, Phuong and Nguyen, Phong Thanh (2022): Evaluation Based on the Distance from the Average Solution Approach: A Derivative Model for Evaluating and Selecting a Construction Manager. Published in: Technologies , Vol. 107, No. 10 (October 2022): 01-11.

Abstract

In the current market of integration and globalization, the competition between engineering and construction companies is increasing. Construction contractors can improve their competitiveness by evaluating and selecting qualified personnel for the construction engineering manager position for their company’s civil engineering projects. However, most personnel evaluation and selection models in the construction industry rely on qualitative techniques, which leads to unsuitable decisions. To overcome this problem, this paper presents evaluation criteria and proposes a new model for selecting construction managers based on the evaluation based on the distance from the average solution approach (EDASA). The research results showed that EDASA has many strengths, such as solving the problem faster when the number of evaluation criteria or the number of alternatives is increased.

• Item Type: MPRA Paper
• Language: English
• Keywords: construction manager; construction project; engineering management; EDASA; resource management; personnel selection; project management
• Subjects: C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C80 - General; E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E24 - Employment ; Unemployment ; Wages ; Intergenerational Income Distribution ; Aggregate Human Capital ; Aggregate Labor Productivity; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L74 - Construction; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M1 - Business Administration > M12 - Personnel Management ; Executives; Executive Compensation; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M5 - Personnel Economics > M54 - Labor Management; N - Economic History > N6 - Manufacturing and Construction; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O15 - Human Resources ; Human Development ; Income Distribution ; Migration

References

1. Huemann, M.; Keegan, A.; Turner, J.R. Human resource management in the project-oriented company: A review. Int. J. Proj. Manag. 2007, 25, 315–323. https://doi.org/10.1016/j.ijproman.2006.10.001.

2. Samimi, E.; Sydow, J. Human resource management in project-based organizations: Revisiting the permanency assumption. Int. J. Hum. Resour. Manag. 2020, 32, 49–83. https://doi.org/10.1080/09585192.2020.1783346.

3. Ling, F.Y.Y.; Ning, Y.; Chang, Y.H.; Zhang, Z. Human resource management practices to improve project managers’ job satisfaction. Eng. Constr. Arch. Manag. 2018, 25, 654–669. https://doi.org/10.1108/ecam-02-2017-0030.

4. Apollo, M.; Miszewska-Urbańska, E. Analysis of the Increase of Construction Costs in Urban Regeneration Projects. Adv. Sci. Technol. Res. J. 2015, 9, 68–74. https://doi.org/10.12913/22998624/60786.

5. Sulieman, H.A.; Alfaraidy, F.A.; Sulieman, H.A.; Alfaraidy, F.A. Influences of Project Management Capabilities on the Organizational Performance of the Saudi Construction Industry. Eng. Technol. Appl. Sci. Res. 2019, 9, 4144–4147. https://doi.org/10.48084/etasr.2740.

6. Belout, A.; Gauvreau, C. Factors influencing project success: The impact of human resource management. Int. J. Proj. Manag. 2004, 22, 1–11. https://doi.org/10.1016/s0263-7863(03)00003-6.

7. Loosemore, M.; Dainty, A.; Lingard, H. Human Resource Management in Construction Projects: Strategic and Operational Approaches; Routledge: New York, NY, USA, 2003.

8. Yurova, V.A.; Velikoborets, G.; Vladyko, A. Design and Implementation of an Anthropomorphic Robotic Arm Prosthesis. Technologies 2022, 10, 103. https://doi.org/10.3390/technologies10050103.

9. Gemünden, H.G. Success Factors of Global New Product Development Programs, the Definition of Project Success, Knowledge Sharing, and Special Issues of Project Management Journal®. Proj. Manag. J. 2015, 46, 2–11. https://doi.org/10.1002/pmj.21480.

10. Zhou, Z.; Zhang, J.; Gong, C. Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network. Comput. Civ. Infrastruct. Eng. 2022, 37, 762–780. https://doi.org/10.1111/mice.12836.

11. Nuwan, P.M.M.C.; Perera, B.A.K.S.; Dewagoda, K.G. Development of Core Competencies of Construction Managers: The Effect of Training and Education. Technol. Knowl. Learn. 2020, 26, 945–984. https://doi.org/10.1007/s10758-020-09474-2.

12. Abdullah, A.H.; Yaman, S.K.; Mohammad, H.; Hassan, P.F. Construction manager’s technical competencies in Malaysian construction projects. Eng. Constr. Arch. Manag. 2018, 25, 153–177. https://doi.org/10.1108/ecam-07-2016-0176.

13. Mohammad, H.; Tun, U.; Onn, H.; Hassan, P.F.; Khalijah, Y.S.; Tun, U.; Onn, H. Quantitative Significance Analysis for Technical Competency of Malaysian Construction Managers. Issues Built Environ. 2018, 77–107.

14. H. Liu, H. Zhang, R. Zhang, H. Jiang, and Q. Ju, "Competence Model of Construction Project Manager in the Digital Era—The Case from China," Buildings, vol. 12, no. 9, p. 1385, 2022.

15. Mohammad, H.; Hassan, F.; Abd Rashid, R.; Yaman, S.K. Dimensionality analysis of technical competency for Malaysian construction managers. In Proceedings of the International UNIMAS STEM Engineering Conference, Malaysia, 24–27 October 2016.

16. Chai, A.H.R. Competencies of Construction Manager. Universiti Tunku Abdul Rahman, UTAR, 2016.

17. Arditi, D.; Balci, G. Managerial Competencies of Female and Male Construction Managers. J. Constr. Eng. Manag. 2009, 135, 1275–1278. https://doi.org/10.1061/(asce)co.1943-7862.0000100.

18. Gharehbaghi, K.; McManus, K. The construction manager as a leader. Leadersh. Manag. Eng. 2003, 3, 56–58.

19. Sajjad, A.; Ahmad, W.; Hussain, S. Decision-Making Process Development for Industry 4.0 Transformation. Adv. Sci. Technol. Res. J. 2022, 16, 1–11.

20. Zavadskas, E.K.; Turskis, Z.; Tamošaitienė, J. Multicriteria Selection of Project Managers by Applying Grey Criteria/Projektų Valdytojo Parinkimo Daugiatikslio Vertinimo Modelis. Technol. Econ. Dev. Econ. 2008, 14, 462–477. https://doi.org/10.3846/1392-8619.2008.14.462-477.

21. Moradi, S.; Kähkönen, K.; Aaltonen, K. Comparison of research and industry views on project managers’ competencies. Int. J. Manag. Proj. Bus. 2019, 13, 543–572. https://doi.org/10.1108/ijmpb-04-2019-0085.

22. Cartwright, C.; Yinger, M. Project management competency development framework. In Proceedings of the PMI Global Congress, Budapest, Hungary, 14 through 16 May, 2007.

23. Edum-Fotwe, F.; McCaffer, R. Developing project management competency: Perspectives from the construction industry. Int. J. Proj. Manag. 2000, 18, 111–124. https://doi.org/10.1016/s0263-7863(98)90075-8.

24. El-Sabaa, S. The skills and career path of an effective project manager. Int. J. Proj. Manag. 2001, 19, 1–7. https://doi.org/10.1016/s0263-7863(99)00034-4.

25. Dainty, A.R.J.; Cheng, M.-I.; Moore, D.R. Competency-Based Model for Predicting Construction Project Managers’ Performance. J. Manag. Eng. 2005, 21, 2–9. https://doi.org/10.1061/(asce)0742-597x(2005)21:1(2).

26. Styhre, A.; Josephson, P.E. Revisiting site manager work: Stuck in the middle? Constr. Manag. Econ. 2006, 24, 521–528.

27. Fisher, E. What practitioners consider to be the skills and behaviours of an effective people project manager. Int. J. Proj. Manag. 2011, 29, 994–1002. https://doi.org/10.1016/j.ijproman.2010.09.002.

28. Zulch, B. Leadership Communication in Project Management. Procedia Soc. Behav. Sci. 2014, 119, 172–181. https://doi.org/10.1016/j.sbspro.2014.03.021.

29. IPMA. Individual Competence Baseline; International Project Management Association Nijkerk, The Netherlands, 2015; p. 432.

30. De Rezende, L.B.; Blackwell, P. Project management competency framework. Iberoam. J. Proj. Manag. 2019, 10, 34–59.

31. Turskis, Z.; Morkunaite, Z.; Kutut, V. A Hybrid Multiple Criteria Evaluation Method of Ranking of Cultural Heritage Structures for Renovation Projects. Int. J. Strat. Prop. Manag. 2017, 21, 318–329. https://doi.org/10.3846/1648715x.2017.1325782.

32. Ghorabaee, M.K.; Zavadskas, E.K.; Olfat, L.; Turskis, Z. Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica 2015, 26, 435–451. https://doi.org/10.15388/informatica.2015.57.

33. Ghorabaee, M.K.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria. J. Air Transp. Manag. 2017, 63, 45–60. https://doi.org/10.1016/j.jairtraman.2017.05.008.

34. Kikomba, M.; Mabela, R.; Ntantu, D. Applying EDAS method to solve air traffic problems. Int. J. Sci. Innov. Math. Res. (IJSIMR) 2016, 4, 15–23.

35. Stanujkic, D.; Popovic, G.; Brzakovic, M. An approach to personnel selection in the IT industry based on the EDAS method. Transform. Bus. Econ. 2018, 17, 44.

36. Wei, G.; Wei, C.; Guo, Y. EDAS method for probabilistic linguistic multiple attribute group decision making and their application to green supplier selection. Soft Comput. 2021, 25, 9045–9053. https://doi.org/10.1007/s00500-021-05842-x.

37. Kumar, R.; Dubey, R.; Singh, S.; Singh, S.; Prakash, C.; Nirsanametla, Y.; Królczyk, G.; Chudy, R. Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component. Materials 2021, 14, 2084. https://doi.org/10.3390/ma14082084.

38. Adalı, E.A.; Tuş, A. Hospital site selection with distance-based multi-criteria decision-making methods. Int. J. Health Manag. 2019, 14, 534–544. https://doi.org/10.1080/20479700.2019.1674005.

39. Stanujkic, D.; Zavadskas, E.K.; Ghorabaee, M.K.; Turskis, Z. An Extension of the EDAS Method Based on the Use of Interval Grey Numbers. Stud. Inform. Control 2017, 26. https://doi.org/10.24846/v26i1y201701.

40. Stević, Z.; Vasiljević, M.; Zavadskas, E.K.; Sremac, S.; Turskis, Z. Selection of carpenter manufacturer using fuzzy EDAS method. Eng. Econ. 2018, 29, 281–290. https://doi.org/10.5755/j01.ee.29.3.16818.

41. Maksimović, M.; Brzakovic, M.; Grahovac, M.; Jovanovic, I. An approach for evaluation the safety and quality of transport at the open pit mines, based on the EDAS method. Min. Met. Eng. Bor 2017, 139–144. https://doi.org/10.5937/mmeb1704139m.

42. Zavadskas, E.K.; Kaklauskas, A.; Turskis, Z.; Tamošaitienė, J. Multi-Attribute Decision-Making Model by Applying Grey Numbers. Informatica 2009, 20, 305–320. https://doi.org/10.15388/informatica.2009.252.

43. Vesković, S.; Stević, Z.; Karabašević, D.; Rajilić, S.; Milinković, S.; Stojić, G. A New Integrated Fuzzy Approach to Selecting the Best Solution for Business Balance of Passenger Rail Operator: Fuzzy PIPRECIA-Fuzzy EDAS Model. Symmetry 2020, 12, 743. https://doi.org/10.3390/sym12050743.

44. Keshavarz-Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. A Dynamic Fuzzy Approach Based on the EDAS Method for Multi-Criteria Subcontractor Evaluation. Information 2018, 9, 68. https://doi.org/10.3390/info9030068.

45. Ghorabaee, M.K.; Zavadskas, E.K.; Amiri, M.; Turskis, Z. Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection. Int. J. Comput. Commun. CONTROL 2016, 11, 358–371. https://doi.org/10.15837/ijccc.2016.3.2557.

46. Naik, G.; Kishore, R.; Dehmourdi, S.A.M. Modeling a Multi-Criteria Decision Support System for Prequalification Assessment of Construction Contractors using CRITIC and EDAS Models. Oper. Res. Eng. Sci. Theory Appl. 2021, 4, 79-101. https://doi.org/10.31181/oresta20402079n.

47. Das, P.P.; Chakraborty, S. Multi-response Optimization of Hybrid Machining Processes Using Evaluation Based on Distance from Average Solution Method in Intuitionistic Fuzzy Environment. Process Integr. Optim. Sustain. 2020, 4, 481–495. https://doi.org/10.1007/s41660-020-00135-6.

48. Zhang, S.; Wei, G.; Gao, H.; Wei, C.; Wei, Y. Edas Method for Multiple Criteria Group Decision Making with Picture Fuzzy Information and Its Application to Green Suppliers Selections. Technol. Econ. Dev. Econ. 2019, 25, 1123–1138. https://doi.org/10.3846/tede.2019.10714.

49. Schitea, D.; Deveci, M.; Iordache, M.; Bilgili, K.; Akyurt, I.Z.; Iordache, I. Hydrogen mobility roll-up site selection using intuitionistic fuzzy sets based WASPAS, COPRAS and EDAS. Int. J. Hydrog. Energy 2019, 44, 8585–8600. https://doi.org/10.1016/j.ijhydene.2019.02.011.

50. Karaşan, A.; Kahraman, C. A novel interval-valued neutrosophic EDAS method: Prioritization of the United Nations national sustainable development goals. Soft Comput. 2018, 22, 4891–4906. https://doi.org/10.1007/s00500-018-3088-y.

51. Ecer, F. Third-Party Logistics (3pls) Provider Selection Via Fuzzy Ahp and Edas Integrated Model. Technol. Econ. Dev. Econ. 2017, 24, 615–634. https://doi.org/10.3846/20294913.2016.1213207.

52. Peng, X.; Liu, C. Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set. J. Intell. Fuzzy Syst. 2017, 32, 955–968. https://doi.org/10.3233/JIFS-161548.

53. Peng, X.; Dai, J.; Yuan, H. Interval-valued Fuzzy Soft Decision Making Methods Based on MABAC, Similarity Measure and EDAS. Fundam. Inform. 2017, 152, 373–396. https://doi.org/10.3233/FI-2017-1525.

54. Ghorabaee, M.K.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. Stochastic EDAS method for multi-criteria decision-making with normally distributed data. J. Intell. Fuzzy Syst. 2017, 33, 1627–1638. https://doi.org/10.3233/JIFS-17184.

55. Keshavarz-Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Comput. Ind. Eng. 2017, 112, 156–174. https://doi.org/10.1016/j.cie.2017.08.017.

56. Keshavarz-Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Turskis, Z. Multi-criteria group decision-making using an extended edas method with interval type-2 fuzzy sets. E+M Èkon. A Manag. 2017, 20, 48–68. https://doi.org/10.15240/tul/001/2017-1-004.

57. Kahraman, C.; Keshavarz-Ghorabaee, M.; Zavadskas, E.K.; Onar, S.C.; Yazdani, M.; Oztaysi, B. Intuitionistic Fuzzy Edas Method: An Application to Solid Waste Disposal Site Selection. J. Environ. Eng. Landsc. Manag. 2017, 25, 1–12. https://doi.org/10.3846/16486897.2017.1281139.

58. Ahn, C.W.; An, J.; Yoo, J.-C. Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs. Inf. Sci. 2012, 192, 109–119. https://doi.org/10.1016/j.ins.2010.07.014.

59. Zhang, X. Criteria for Selecting the Private-Sector Partner in Public–Private Partnerships. J. Constr. Eng. Manag. 2005, 131, 631–644. https://doi.org/10.1061/(asce)0733-9364(2005)131:6(631).

60. Sun, H.; Wei, G.-W.; Chen, X.-D.; Mo, Z.-W. Extended EDAS method for multiple attribute decision making in mixture z-number environment based on CRITIC method. J. Intell. Fuzzy Syst. 2022, 43, 2777–2788. https://doi.org/10.3233/jifs-212954.

61. Stević, Z.; Vasiljević, M.; Puška, A.; Tanackov, I.; Junevičius, R.; Vesković, S. Evaluation of Suppliers under Uncertainty: A Multiphase Approach Based on Fuzzy Ahp and Fuzzy Edas. Transport 2019, 34, 52–66. https://doi.org/10.3846/transport.2019.7275.