Share:


Sorting subcontractors’ activities in construction projects with a novel Additive-veto sorting approach

    Rachel Perez Palha   Affiliation
    ; Adiel Teixeira De Almeida   Affiliation
    ; Danielle Costa Morais   Affiliation
    ; Keith W. Hipel   Affiliation

Abstract

Selection processes in civil engineering infrastructure projects might require more time and effort than the decisionmakers involved in these projects are normally prepared to devote to running them. A novel approach is proposed to sort these activities into classes that represent their impact on the project, namely additive-veto sorting model, which should be considered before any bidding procedure. Therefore, problems regarding the client’s satisfaction caused by subcontractors can be avoided, and the decision-makers involved in the selection problem can devote to each class an effort compatible with the impact that activity might have on the project. The novelty of this method is that it was built to reflect the quasi-compensatory rationality of decision-makers in the construction industry; it provides them with insights on subcontractors’ activities, and it is grounded on and inspired by a real case study. The new parameters proposed within this model introduce the idea of vetoing an activity being assigned to a class when this activity is incompatible with the decision-maker’s preferences. By using this novel method, the authors succeeded in finding results that avoided a complete compensation amongst the factors considered, taking into account ranges that would be of significant importance in the decision process.

Keyword : subcontractor management, multiple criteria analysis, Additive-veto sorting approach, sorting

How to Cite
Palha, R. P., De Almeida, A. T., Morais, D. C., & Hipel, K. W. (2019). Sorting subcontractors’ activities in construction projects with a novel Additive-veto sorting approach. Journal of Civil Engineering and Management, 25(4), 306-321. https://doi.org/10.3846/jcem.2019.9644
Published in Issue
Apr 2, 2019
Abstract Views
1180
PDF Downloads
763
Creative Commons License

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

References

Abbasianjahromi, H., Rajaie, H., & Shakeri, E. (2013). A framework for subcontractor selection in the construction industry. Journal of Civil Engineering and Management, 19(2), 158-168. https://doi.org/10.3846/13923730.2012.743922

Abbasianjahromi, H., Rajaie, H., Shakeri, E., & Kazemi, O. (2016). A new approach for subcontractor selection in the construction industry based on portfolio theory. Journal of Civil Engineering and Management, 22(3), 346-356. https://doi.org/10.3846/13923730.2014.897983

Aguayo, E. A., Mateos, A., & Jiménez, A. (2014). A new dominance intensity method to deal with ordinal information about a DM’s preferences within MAVT. Knowledge-Based Systems, 69(1), 159-169. https://doi.org/10.1016/j.knosys.2014.05.017

Araz, C., & Ozkarahan, I. (2007). Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure. International Journal of Production Economics, 106(2), 585-606. https://doi.org/10.1016/j.ijpe.2006.08.008

Arditi, D., & Chotibhongs, R. (2005). Issues in subcontracting practice. Journal of Construction Engineering and Management, 131(8), 866-876. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:8(866)

Ballesteros-Perez, P., Skitmore, M., Pellicer, E., & Zhang, X. L. (2016). Scoring rules and competitive behavior in best-value construction auctions. Journal of Construction Engineering and Management, 142(9), 14. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001144

Biruk, S., Jaśkowski, P., & Czarnigowska, A. (2017). Minimizing project cost by integrating subcontractor selection decisions with scheduling. IOP Conference Series: Materials Science and Engineering, 245, 072007. https://doi.org/10.1088/1757-899X/245/7/072007

Bregar, A. (2018). Decision support on the basis of utility models with discordance-related preferential information: investigation of risk aversion properties. Journal of Decision Systems, 27, 236-243. https://doi.org/10.1080/12460125.2018.1468170

de Almeida, A. T. (2013). Additive-veto models for choice and ranking multicriteria decision problems. Asia-Pacific Journal of Operational Research, 30(6), 1. https://doi.org/10.1142/S0217595913500267

de Almeida, A. T., Cavalcante, C. A. V., Alencar, M. H., Ferreira, R. J. P., de Almeida-Filho, A. T., & Garcez, T. V. (2015). Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis. International series in operations research and management science (1st ed., Vol. 231). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-17969-8

Doumpos, M., & Zopounidis, C. (2004). Multicriteria decision aid classification methods. In P. M. Pardalos & D. Hearn (Eds.), Applied Optimization (Vol. 73). Boston: Kluwer Academic Publishers. https://doi.org/10.1007/b101986

Filzmoser, M., & Gettinger, J. (2013). Negotiation by veto. In B. Martinovski (Ed.). 13th International Meeting on Group Decision and Negotiation (pp. 348-350). Stockholm.

Fishburn, P. C. (1976). Noncompensatory preferences. Synthese, 33(1), 393-403. https://doi.org/10.1007/BF00485453

Holt, G. D., Olomolaiye, P. O., & Harris, F. C. (1994). Evaluating prequalification criteria in contractor selection. Building and Environment, 29(4), 437-448. https://doi.org/10.1016/0360-1323(94)90003-5

Holt, G. D., Olomolaiye, P. O., & Harris, F. C. (1995). A review of contractor selection practice in the U.K. construction industry. Building and Environment, 30(4), 553-561. https://doi.org/10.1016/0360-1323(95)00008-T

Iooss, B., & Lemaître, P. (2015). A review on global sensitivity analysis methods. In G. Dellino & C. Meloni (Eds.). Uncertainty management in simulation-optimization of complex systems (Vol. 59, pp. 101-122). Boston: Springer. https://doi.org/10.1007/978-1-4899-7547-8_5

Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. https://doi.org/10.1080/00207543.2012.657966

Kadziński, M., Ciomek, K., & Słowiński, R. (2015). Modeling assignment-based pairwise comparisons within integrated framework for value-driven multiple criteria sorting. European Journal of Operational Research, 241(3), 830-841. https://doi.org/10.1016/j.ejor.2014.09.050

Keeney, R. L., & Raiffa, H. (1993). Decision with multiple objectives: preferences and value trade-offs. New York: Cambridge University Press. https://doi.org/10.1017/CBO9781139174084

Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert Systems with Applications, 37(7), 4999-5008. https://doi.org/10.1016/j.eswa.2009.12.013

Keshavarz Ghorabaee, M., Amiri, M., Salehi Sadaghiani, J., & Hassani Goodarzi, G. (2014). Multiple criteria group decision-making for supplier selection based on COPRAS method with interval type-2 fuzzy sets. International Journal of Advanced Manufacturing Technology, 75(5-8), 1115-1130. https://doi.org/10.1007/s00170-014-6142-7

Kumaraswamy, M. M., & Matthews, J. D. (2000). Improved subcontractor selection employing partnering principles. Journal of Management in Engineering, 16(3), 47-57. https://doi.org/10.1061/(ASCE)0742-597X(2000)16:3(47)

Love, P. E. D., Edwards, D. J., Smith, J., & Walker, D. H. T. (2009). Divergence or congruence? A path model of rework for building and civil engineering projects. Journal of Performance of Constructed Facilities, 23(6), 480-488. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000054

Martel, J.-M., & Matarazzo, B. (2016). Other outranking approaches. In Multiple criteria decision analysis: State of the art surveys (pp. 221-282). New York: Springer Science + Business Media. https://doi.org/10.1007/978-1-4939-3094-4_7

Medeiros, C. P., Alencar, M. H., & de Almeida, A. T. (2017). Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis. Reliability Engineering and System Safety, 165, 268-276. https://doi.org/10.1016/j.ress.2017.04.002

Moulin, H. (1981). The proportional veto principle. The Review of Economic Studies, 48(3), 407-416. https://doi.org/10.2307/2297154

Munda, G. (2016). Multiple criteria decision analysis and sustainable development. In S. Greco, M. Ehrgott, & J. Figueira (Eds.), Multiple criteria decision analysis. International Series in Operations Research & Management Science (Vol. 233, pp. 1235-1267). New York: Springer. https://doi.org/10.1007/978-1-4939-3094-4_27

Mungle, S., Benyoucef, L., Son, Y. J., & Tiwari, M. K. (2013). A fuzzy clustering-based genetic algorithm approach for time-cost-quality trade-off problems: A case study of highway construction project. Engineering Applications of Artificial Intelligence, 26(8), 1953-1966. https://doi.org/10.1016/j.engappai.2013.05.006

Ng, S. T., & Skitmore, M. (2014). Developing a framework for subcontractor appraisal using a balanced scorecard. Journal of Civil Engineering and Management, 20(2), 149-158. https://doi.org/10.3846/13923730.2013.802705

Palha, R. P. (2019). Negotiation throughout flexible and interactive tradeoffs applied to construction procurement. Automation in Construction, 99, 39-51. https://doi.org/10.1016/j.autcon.2018.12.002

Palha, R. P., de Almeida, A. T., & Alencar, L. H. (2016). A model for sorting activities to be outsourced in civil construction based on ROR-UTADIS. Mathematical Problems in Engineering, ID 9236414, 1-15. https://doi.org/10.1155/2016/9236414

Polat, G. (2016). Subcontractor selection using the integration of the AHP and PROMETHEE methods. Journal of Civil Engineering and Management, 22(8), 1042-1054. https://doi.org/10.3846/13923730.2014.948910

Raoufi, M., Seresht, N. G., & Fayek, A. R. (2017). Overview of fuzzy simulation techniques in construction engineering and management. In 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS), El Paso, TX, USA. https://doi.org/10.1109/NAFIPS.2016.7851610

Roszkowska, E., & Wachowicz, T. (2015). Application of fuzzy TOPSIS to scoring the negotiation offers in ill-structured negotiation problems. European Journal of Operational Research, 242(3), 920-932. https://doi.org/10.1016/j.ejor.2014.10.050

Roy, B., & Bouyssou, D. (1993). Aide multicritère à la decision: méthodes et cas. Paris: Economica.

Sabio, P., Jiménez-Martín, A., & Mateos, A. (2015). Veto values within MAUT for group decision making on the basis of dominance measuring methods with fuzzy weights. In B. Kamiski, G. E. Kersten, & T. Wachowicz (Eds.), Oultooks and insights on group decision and negotiation (pp. 119-130). Warsaw: Springer International Publishing. https://doi.org/10.1007/978-3-319-19515-5_10

Sabokbar, H. F., Hosseini, A., Banaitis, A., & Banaitiene, N. (2016). A novel sorting method topsis-sort: An application for Tehran environmental quality evaluation. E a M: Ekonomie a Management, 19(2), 87-104. https://doi.org/10.15240/tul/001/2016-2-006

Schöttle, A., & Arroyo, P. (2017). Comparison of weighting-rating-calculating, best value, and choosing by advantages for bidder selection. Journal of Construction Engineering and Management, 143(8), 05017015. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001342

Singh, D., & Tiong, R. (2005). A fuzzy decision framework for contractor selection. Journal of Construction Engineering and Management, 131(1), 62-70. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:1(62)

Sönmez, M., Holt, G. D., Yang, J. B., & Graham, G. (2002). Applying evidential reasoning to prequalifying construction contractors. Journal of Management in Engineering, 18(3), 111-119. https://doi.org/10.1061/(ASCE)0742-597X(2002)18:3(111)

Tan, Y., Xue, B., & Cheung, Y. T. (2017). Relationships between main contractors and subcontractors and their impacts on main contractor competitiveness: An empirical study in Hong Kong. Journal of Construction Engineering and Management, 143(7), 05017007. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001311

Ulubeyli, S., Manisali, E., & Kazaz, A. (2010). Subcontractor selection practices in international construction projects. Journal of Civil Engineering and Management, 16(1), 47-56. https://doi.org/10.3846/jcem.2010.04

Vetschera, R., Chen, Y., Hipel, K. W., & Kilgour, D. M. (2010). Robustness and information levels in case-based multiple criteria sorting. European Journal of Operational Research, 202(3), 841-852. https://doi.org/10.1016/j.ejor.2009.06.026

Wachowicz, T., & Blaszczyk, P. (2013). TOPSIS based approach to scoring negotiating offers in negotiation support systems. Group Decision and Negotiation, 22(6), 1021-1050. https://doi.org/10.1007/s10726-012-9299-1

Wan, S. P., & Li, D. F. (2013). Fuzzy LINMAP approach to heterogeneous MADM considering comparisons of alternatives with hesitation degrees. Omega, 41(6), 925-940. https://doi.org/10.1016/j.omega.2012.12.002

Zopounidis, C., & Doumpos, M. (2002). Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2), 229-246. https://doi.org/10.1016/S0377-2217(01)00243-0