Share:


Embedded remote group environment through modification in MACBETH – an application of contractor’s selection in construction

    Ali Raza Khoso   Affiliation
    ; Aminah Md. Yusof Affiliation
    ; Zhen-Song Chen   Affiliation
    ; Xian-Jia Wang Affiliation
    ; Mirosław J. Skibniewski Affiliation
    ; Nafees Ahmed Memon Affiliation

Abstract

A group decision environment has profound roots in MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) analysis which indeed has plentiful advantages; however, many researchers envisage the embedded group decision system as an impediment in actual implementation. The accessibility of explicit interaction of decision makers at a single platform in the form of embedded group decision environment is a great impediment to the researchers. Accordingly, this research aims to tailor a novel alternative system of dealing with the embedded group decision under a remote group decision environment via integrating MACBETH and Exploratory Factor Analysis. The study finds that an embedded remote group decision making system could serve as an alternative system of group decision making which has plentiful perks in group decision applications. This system could help researchers to carry out research without confusing in embedded group decision environment but including all decision-makers in the model. The implication of proposed system is not only limited to MACBETH; however, due to system’s versatility, a similar approach could be fruitful for other group-related environments involving collective decisions.


Please view correction statement: Corrigendum: Embedded remote group environment through modification in MACBETH - an application of contractor’s selection in construction

Keyword : MACBETH, group decisions, exploratory factor analysis, contractors, multi-criteria decisions

How to Cite
Khoso, A. R., Yusof, A. M., Chen, Z.-S., Wang, X.-J., Skibniewski, M. J., & Memon, N. A. (2021). Embedded remote group environment through modification in MACBETH – an application of contractor’s selection in construction. Journal of Civil Engineering and Management, 27(8), 595-616. https://doi.org/10.3846/jcem.2021.15763
Published in Issue
Nov 10, 2021
Abstract Views
1131
PDF Downloads
536
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Anagnostopoulos, K. P., & Vavatsikos, A. P. (2006). An AHP model for construction contractor prequalification. Operational Research, 6(3), 333–346. https://doi.org/10.1007/BF02941261

Andrade, G. N., Alves, L. A., Andrade, F. V. S., & de Mello, J. C. C. B. S. (2016). Evaluation of power plants technologies using multicriteria methodology MACBETH. IEEE Latin America Transactions, 14(1), 188–198. https://doi.org/10.1109/tla.2016.7430079

Awwad, R., & Ammoury, M. (2019). Owner’s perspective on evolution of bid prices under various price-driven bid selection methods. Journal of Computing in Civil Engineering, 33(2), 04018061. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000803

Bachrane, M., Khaled, A., El-Alami, J., & Hanoune, M. (2016). Investment location selection based on economic intelligence and MACBETH decision aid model. Journal of Information Technology Research, 9(3), 37–48. https://doi.org/10.4018/jitr.2016070103

Bana e Costa, Carlos A., & Vansnick, J. C. (1994). MACBETH – An interactive path towards the construction of cardinal value functions. Inter-national Transactions in Operational Research, 1(4), 489–500. https://doi.org/10.1016/0969-6016(94)90010-8

Bana e Costa, C. A., Corrêa, É. C., De Corte, J. M., & Vansnick, J. C. (2002). Facilitating bid evaluation in public call for tenders: A so-cio-technical approach. Omega, 30(3), 227–242. https://doi.org/10.1016/S0305-0483(02)00029-4

Bana e Costa, C., De Corte, J.-M., & Vansnick, J.-C. (2003). MACBETH (Overview of MACBETH multicriteria decision analysis approach). International Journal of Information Technology and Decision Making, 11(1), 359–387. https://doi.org/10.1142/S0219622012400068

Bana e Costa, Carlos A., & Chagas, M. P. (2004). A career choice problem: An example of how to use MACBETH to build a quantitative value model based on qualitative value judgments. European Journal of Operational Research, 153(2), 323–331. https://doi.org/10.1016/S0377-2217(03)00155-3

Bana e Costa, C. A., De Corte, J. ‐M., & Vansnick, J. ‐C. (2005). On the mathematical foundation of MACBETH. In J. Figueira, S. Greco, & M. Ehrgott (Eds.), Multiple criteria decision analysis: state of the art surveys (pp. 409–442). Springer Science & Business Media. https://doi.org/10.1007/0-387-23081-5_10

Bana e Costa, C. A., Oliveira, C. S., & Vieira, V. (2008). Prioritization of bridges and tunnels in earthquake risk mitigation using multicriteria deci-sion analysis: Application to Lisbon. Omega, 36, 442–450. https://doi.org/10.1016/j.omega.2006.05.008

Bana e Costa, C. A., Carnero, M. C., & Oliveira, M. D. (2012). A multi-criteria model for auditing a Predictive Maintenance Programme. European Journal of Operational Research, 217(2), 381–393. https://doi.org/10.1016/j.ejor.2011.09.019

Barfod, M. B., & Salling, K. B. (2015). A new composite decision support framework for strategic and sustainable transport appraisals. Transpor-tation Research Part A: Policy and Practice, 72, 1–15. https://doi.org/10.1016/j.tra.2014.12.001

Benson, N. F., Kranzler, J. H., & Floyd, R. G. (2016). Examining the integrity of measurement of cognitive abilities in the prediction of achieve-ment: Comparisons and contrasts across variables from higher-order and bifactor models. Journal of School Psychology, 58, 1–19. https://doi.org/10.1016/j.jsp.2016.06.001

Bernard, H. R. (2006). Research methods in anthropology (2nd ed.). Lanham.

Birjandi, A. K., Akhyani, F., Sheikh, R., & Sana, S. S. (2019). Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method. Soft Computing, 23(20), 10569–10585. https://doi.org/10.1007/s00500-019-04050-y

Carnero, M. C., & Gómez, A. (2016). A multicriteria decision making approach applied to improving maintenance policies in healthcare organiza-tions. BMC Medical Informatics and Decision Making, 16(1), 47. https://doi.org/10.1186/s12911-016-0282-7

Chen, Z. S., Zhang, X., Pedrycz, W., Wang, X. J., & Skibniewski, M. J. (2020). Bid evaluation in civil construction under uncertainty: A two-stage LSP-ELECTRE III-based approach. Engineering Applications of Artificial Intelligence, 94, 103835. https://doi.org/10.1016/j.engappai.2020.103835

Chen, Z. S., Zhang, X., Rodríguez, R. M., Pedrycz, W., & Martínez, L. (2021). Expertise-based bid evaluation for construction-contractor selection with generalized comparative linguistic ELECTRE III. Automation in Construction, 125, 103578. https://doi.org/10.1016/j.autcon.2021.103578

Cheng, E. W. L., & Li, H. (2004). Contractor selection using the analytic network process. Construction Management and Economics, 22(10), 1021–1032. https://doi.org/10.1080/0144619042000202852

Clivillé, V., & Berrah, L. (2012). Overall performance measurement in a supply chain: Towards a supplier-prime manufacturer based model. Jour-nal of Intelligent Manufacturing, 23(6), 2459–2469. https://doi.org/10.1007/s10845-011-0512-x

Cox, R., Sanchez, J., & Revie, C. W. (2013). Multi-criteria decision analysis tools for prioritising emerging or re-emerging infectious diseases associated with climate change in Canada. PLoS ONE, 8(8), e68338. https://doi.org/10.1371/journal.pone.0068338

Dabrowski, M. (2014). The simple multi attribute rating technique (SMART). In Multi-criteria decision analysis for use in transport decision making. DTU Transport Compendium Series (Part 2).

Danielson, M., & Ekenberg, L. (2019). An improvement to swing techniques for elicitation in MCDM methods. Knowledge-Based Systems, 168, 70–79. https://doi.org/10.1016/j.knosys.2019.01.001

Darvish, M., Yasaei, M., & Saeedi, A. (2009). Application of the graph theory and matrix methods to contractor ranking. International Journal of Project Management, 27(6), 610–619. https://doi.org/10.1016/j.ijproman.2008.10.004

Demesouka, O. E., Vavatsikos, A. P., & Anagnostopoulos, K. P. (2016). Using MACBETH multicriteria technique for GIS-Based landfill suita-bility analysis. Journal of Environmental Engineering, 142(10), 04016042. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001109

DiStefano, C., Zhu, M., & Mîndrilǎ, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical As-sessment, Research and Evaluation, 14(20), 20. https://doi.org/10.7275/da8t-4g52

Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organization-al Behavior and Human Decision Processes, 60(3), 306–325. https://doi.org/10.1006/obhd.1994.1087

El-Abbasy, M. S., Zayed, T., Ahmed, M., Alzraiee, H., & Abouhamad, M. (2013). Contractor selection model for highway projects using inte-grated simulation and Analytic Network Process. Journal of Construction Engineering and Management, 139(7), 755–767. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000647

Elsayah, O. S. (2016). A framework for improvement of contractor selection procedures on major construction project in Libya. https://www.napier.ac.uk/~/media/worktribe/output-453191/a-framework-for-improvement-of-contractor-selection.pdf

Ertay, T., Kahraman, C., & Kaya, İ. (2013). Evaluation of renewable energy alternatives using MACBETH and Fuzzy AHP multicriteria methods: The case of Turkey. Technological and Economic Development of Economy, 19(1), 38–62. https://doi.org/10.3846/20294913.2012.762950

Ferreira, F. A. F., Spahr, R. W., Santos, S. P., & Rodrigues, P. M. M. (2012). A multiple criteria framework to evaluate bank branch potential attractiveness. International Journal of Strategic Property Management, 16(3), 254–276. https://doi.org/10.3846/1648715x.2012.707629

Ferreira, F. A. F., Spahr, R. W., & Sunderman, M. A. (2016). Using multiple criteria decision analysis (MCDA) to assist in estimating residential housing values. International Journal of Strategic Property Management, 20(4), 354–370. https://doi.org/10.3846/1648715x.2015.1122668

Foster, J., Barkus, E., & Yavorsky, C. (2006). Understanding and using advanced statistics. SAGE Publications Ltd. https://doi.org/10.4135/9780857020154

Gonçalves, J. M., Ferreira, F. A. F., Ferreira, J. J. M., & Farinha, L. M. C. (2019). A multiple criteria group decision-making approach for the assessment of small and medium-sized enterprise competitiveness. Management Decision, 57(2), 480–500. https://doi.org/10.1108/MD-02-2018-0203

Gurbuz, T. (2010). Multiple criteria human performance evaluation using Choquet integral. International Journal of Computational Intelligence Systems, 3(3), 290–300. https://doi.org/10.2991/ijcis.2010.3.3.5

Holt, G. (2010). Contractor selection innovation: Examination of two decades’ published research. Construction Innovation, 10(3), 304–328. https://doi.org/10.1108/14714171011060097

James, S. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, socie-ties and nations. Random House Large Print.

Jaskowski, P., Biruk, S., & Bucon, R. (2010). Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment. Automation in Construction, 19(2), 120–126. https://doi.org/10.1016/j.autcon.2009.12.014

Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., & Canteras-Jordana, J. C. (2014). A review of application of multi-criteria decision making methods in construction. Automation in Construction, 45, 151–162. https://doi.org/10.1016/j.autcon.2014.05.013

Joerin, F., Cool, G., Rodriguez, M. J., Gignac, M., & Bouchard, C. (2010). Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities. Environmental Monitoring and Assessment, 166(1), 313–330. https://doi.org/10.1007/s10661-009-1004-8

Keeney, R. L., & Howard, R. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Cambridge University Press. https://doi.org/10.1017/CBO9781139174084

Khoso, A. R., & Yusof, A. M. (2020). Extended review of contractor selection in construction projects. Canadian Journal of Civil Engineering, 47(7), 771–789. https://doi.org/10.1139/cjce-2019-0258

Khoso, A. R., Yusof, A. M., Chai, C., & Laghari, M. A. (2021a). Robust contractor evaluation criteria classification for modern technology public construction projects. Journal of Public Procurement, 21(1), 53–74. https://doi.org/10.1108/JOPP-06-2020-0053

Khoso, A. R., Yusof, A. M., Khahro, S. H., Abidin, N. I. A. B., & Memon, N. A. (2021b). Automated two-stage continuous decision support model using exploratory factor analysis-MACBETH-SMART: an application of contractor selection in public sector construction. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03186-w

Khoso, A. R., Yusof, A. M., Chen, Z. S., Skibniewski, M. J., Chin, K. S., Khahro, S. H., & Sohu, S. (2021c). Comprehensive analysis of state-of-the-art contractor selection models in construction environment-A critical review and future call. Socio-Economic Planning Sciences, 101137. https://doi.org/10.1016/j.seps.2021.101137

Konidari, P., & Mavrakis, D. (2007). A multi-criteria evaluation method for climate change mitigation policy instruments. Energy Policy, 35(12), 6235–6257. https://doi.org/10.1016/j.enpol.2007.07.007

Kundakcı, N. (2019). An integrated method using MACBETH and EDAS methods for evaluating steam boiler alternatives. Journal of Mul-ti-Criteria Decision Analysis, 26(1), 27–34. https://doi.org/10.1002/mcda.1656

Lam, K. C., & Yu, C. Y. (2011). A multiple Kernel learning-based decision support model for contractor pre-qualification. Automation in Con-struction, 20(5), 531–536. https://doi.org/10.1016/j.autcon.2010.11.019

Lauras, M., Marques, G., & Gourc, D. (2010). Towards a multi-dimensional project performance measurement system. Decision Support Systems, 48(2), 342–353. https://doi.org/10.1016/j.dss.2009.09.002

Liu, B., Huo, T., Meng, J., Gong, J., Shen, Q., & Sun, T. (2015). Identification of key contractor characteristic factors that affect project success under different project delivery systems. Journal of Management in Engineering, 32(1), 05015003. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000388

Liu, B., Huo, T., Liao, P., Yuan, J., Sun, J., & Hu, X. (2017). A special Partial Least Squares (PLS) path decision modeling for bid evaluation of large construction projects. KSCE Journal of Civil Engineering, 21(3), 579–592. https://doi.org/10.1007/s12205-016-0702-3

Lourenço, J. C., Morton, A., & Bana e Costa, C. A. (2012). PROBE - A multicriteria decision support system for portfolio robustness evaluation. Decision Support Systems, 54(1), 534–550. https://doi.org/10.1016/j.dss.2012.08.001

Madeira, A. G., Cardoso, M. M., Belderrain, M. C. N., Correia, A. R., & Schwanz, S. H. (2012). Multicriteria and multivariate analysis for port performance evaluation. International Journal of Production Economics, 140(1), 450–456. https://doi.org/10.1016/j.ijpe.2012.06.028

Marcarelli, G., & Nappi, A. (2019). Multicriteria approach to select the most economically advantageous tender: The application of AHP in Italian public procurement. Journal of Public Procurement, 19(3), 201–223. https://doi.org/10.1108/JOPP-05-2018-0020

Marović, I., Perić, M., & Hanak, T. (2021). A multi-criteria decision support concept for selecting the optimal contractor. Applied Sciences, 11(4), 1660. https://doi.org/10.3390/app11041660

Marques, G., Gourc, D., & Lauras, M. (2011). Multi-criteria performance analysis for decision making in project management. International Jour-nal of Project Management, 29(8), 1057–1069. https://doi.org/10.1016/j.ijproman.2010.10.002

Mateus, R. J. G., Bana e Costa, J. C., & Matos, P. V. (2017). Supporting multicriteria group decisions with MACBETH tools: Selection of sus-tainable brownfield redevelopment actions. Group Decision and Negotiation, 26(3), 495–521. https://doi.org/10.1007/s10726-016-9501-y

Minchin Jr., R. E., & Smith, G. R. (2005). Quality-based contractor rating model for qualification and bidding purposes. Journal of Management in Engineering, 21(1), 38–43. https://doi.org/10.1061/(ASCE)0742-597X(2005)21:1(38)

Mohemad, R., Hamdan, A. R., Othman, Z. A., & Noor, N. M. M. (2010). Decision support systems (DSS) in construction tendering processes. International Journal of Computer Science Issues, 7(2), 35–45. https://doi.org/10.1109/ICSSSM.2008.4598482

Monat, J. P. (2009). The benefits of global scaling in multi-criteria decision analysis. Judgment and Decision Making, 4(6), 492–508.

Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning (47th ed.). University of Illinois Press.

Pang, J., & Liang, J. (2012). Evaluation of the results of multi-attribute group decision-making with linguistic information. Omega, 40(3), 294–301. https://doi.org/10.1016/j.omega.2011.07.006

Phogat, S., & Gupta, A. K. (2019). Evaluating the elements of just in time (JIT) for implementation in maintenance by exploratory and confirmatory factor analysis. International Journal of Quality and Reliability Management, 36(1), 7–24. https://doi.org/10.1108/IJQRM-12-2017-0279

Russell, J. S., & Skibniewski, M. J. (1988). Decision criteria in contractor prequalification. Journal of Management in Engineering, 4(2), 148–164. https://doi.org/10.1061/(asce)9742-597x(1988)4:2(148)

Russell, J. S., & Skibniewski, M. J. (1990). QUALIFIER‐1: Contractor prequalification model. Journal of Computing in Civil Engineering, 4(1), 77–90. https://doi.org/10.1061/(ASCE)0887-3801(1990)4:1(77)

Russell, J. S., Skibniewski, M. J., & Cozier, D. R. (1990). Qualifier‐2: Knowledge‐based system for contractor prequalification. Journal of Con-struction Engineering and Management, 116(1), 157–171. https://doi.org/10.1061/(ASCE)0733-9364(1990)116:1(157)

San Cristóbal, J. R. (2012). Contractor selection using multicriteria decision-making methods. Journal of Construction Engineering and Manage-ment, 138(6), 751–758. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000488

Semaan, N., & Salem, M. (2017). A deterministic contractor selection decision support system for competitive bidding. Engineering, Construction and Architectural Management, 24(1), 61–77. https://doi.org/10.1108/ECAM-06-2015-0094

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Pearson Education.

Taylan, O., Kabli, M. R., Porcel, C., & Herrera-Viedma, E. (2017). Contractor selection for construction projects using consensus tools and Big Data. International Journal of Fuzzy Systems, 20(4), 1267–1281. https://doi.org/10.1007/s40815-017-0312-3

Topcu, Y. I. (2004). A decision model proposal for construction contractor selection in Turkey. Building and Environment, 39(4), 469–481. https://doi.org/10.1016/j.buildenv.2003.09.009

Tosun, Ö. (2017). Using MACBETH method for technology selection in production environment. American Journal of Data Mining and Knowledge Discovery, 2(1), 37–41.

Vahdani, B., Mousavi, S. M., Hashemi, H., Mousakhani, M., & Tavakkoli-Moghaddam, R. (2013). A new compromise solution method for fuzzy group decision-making problems with an application to the contractor selection. Engineering Applications of Artificial Intelligence, 26(2), 779–788. https://doi.org/10.1016/j.engappai.2012.11.005

Vieira, A. C. L., Oliveira, M. D., & Bana e Costa, C. A. (2020). Enhancing knowledge construction processes within multicriteria decision analy-sis: The Collaborative Value Modelling framework. Omega, 94, 102047. https://doi.org/10.1016/j.omega.2019.03.005

Wang, W., Yu, W., Yang, I., Lin, C., Lee, M., & Cheng, Y.-Y. (2013). Applying the AHP to support the best-value contractor selection – lessons learned from two case studies in Taiwan. Journal of Civil Engineering and Management, 19(1), 24–36. https://doi.org/10.3846/13923730.2012.734851

Watt, D. J., Kayis, B., & Willey, K. (2010). The relative importance of tender evaluation and contractor selection criteria. International Journal of Project Management, 28(1), 51–60. https://doi.org/10.1016/j.ijproman.2009.04.003

Winterfeldt, V., & D., Edwards, R. (1986). Decision analysis and behavioral research. Cambridge University Press.

Xiao, L., Chen, Z. S., Zhang, X., Chang, J. P., Pedrycz, W., & Chin, K. S. (2020). Bid evaluation for major construction projects under large-scale group decision-making environment and characterized expertise levels. International Journal of Computational Intelligence Systems, 13(1), 1227–1242. https://doi.org/10.2991/ijcis.d.200801.002

Yang, J.-B., Wang, H.-H., Wang, W.-C., & Ma, S.-M. (2016). Using data envelopment analysis to support best-value contractor selection. Jour-nal of Civil Engineering and Management, 22(2), 199–209. https://doi.org/10.3846/13923730.2014.897984

Zhao, L., Liu, W., & Wu, Y. (2020). Bid evaluation decision for major project based on analytic hierarchy process and data envelopment analysis cross-efficiency model. Journal of Ambient Intelligence and Humanized Computing, 11(9), 3639–3647. https://doi.org/10.1007/s12652-019-01564-z