A fuzzy decision support system for sustainable construction project selection: an integrated FPP-FIS model
Abstract
Sustainability has become a key concern for project selection in construction industries. Determining the best sustainable project based on various sustainability attributes is a very complicated decision. Accordingly, developing a suitable decision support framework can be very helpful for decision makers to attain planned business goals and complete projects at the right time with good quality. This research develops a decision support model which helps managers to understand the concept of sustainability in construction project selection and choose the best project using a new integrated Multi-Criteria Decision Making (MCDM) approach under uncertainty by integrating Fuzzy Preference Programming (FPP) as a modification of Fuzzy Analytical Hierarchy Process (FAHP), with Fuzzy Inference System (FIS) as a fuzzy rulebased expert system. In the first phase of the research, fifteen sustainability attributes were selected. In the second phase, the final weight of each attribute was computed by using FPP. In the last phase, the most appropriate project was selected by running the weighted FIS. The results showed that Project 3 (P3) is the best project. Finally, two different evaluative tests were also applied to verify the validity and robustness of the developed model.
Keyword : sustainability, project selection, sustainable project selection, multi-criteria decision making
How to Cite
Fallahpour, A. ., Wong, K. Y. ., Rajoo, S. ., Olugu, E. U. ., Nilashi, M. ., & Turskis, Z. . (2020). A fuzzy decision support system for sustainable construction project selection: an integrated FPP-FIS model. Journal of Civil Engineering and Management, 26(3), 247-258. https://doi.org/10.3846/jcem.2020.12183
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Borujeni, M. P., & Gitinavard, H. (2017). Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method. Journal of Sustainable Mining, 16(4), 207–218. https://doi.org/10.1016/j.jsm.2017.12.006
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Doulabi, R. Z., & Asnaashari, E. (2016). Identifying success factors of healthcare facility construction projects in Iran. Procedia Engineering, 164, 409–415. https://doi.org/10.1016/j.proeng.2016.11.638
Ebrahimnejad, S., Naeini, M., Gitinavard, H., & Mousavi, S. M. (2017). Selection of IT outsourcing services’ activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach. Journal of Intelligent & Fuzzy Systems, 32(6), 4081–4093. https://doi.org/10.3233/JIFS-152520
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Heravi, G., Fathi, M., & Faeghi, S. (2017). Multi-criteria group decision-making method for optimal selection of sustainable industrial building options focused on petrochemical projects. Journal of Cleaner Production, 142, 2999–3013. https://doi.org/10.1016/j.jclepro.2016.10.168
Lambin, E. F., & Thorlakson, T. (2018). Sustainability standards: Interactions between private actors, civil society, and governments. Annual Review of Environment and Resources, 43, 369–393. https://doi.org/10.1146/annurev-environ-102017-025931
Lopes, J., Oliveira, R., & Abreu, M. I. (2017). The sustainability of the construction industry in Sub-Saharan Africa: Some new evidence from recent data. Procedia Engineering, 172, 657–664. https://doi.org/10.1016/j.proeng.2017.02.077
Lozano, R., Carpenter, A., & Huisingh, D. (2015). A review of ‘theories of the firm’and their contributions to corporate sustainability. Journal of Cleaner Production, 106, 430–442. https://doi.org/10.1016/j.jclepro.2014.05.007
Lückmann, P. (2015). Towards identifying success factors for cross-cultural project customer engagement: A literature review. Procedia Computer Science, 64, 324–333. https://doi.org/10.1016/j.procs.2015.08.496
Ma, M., Shen, L., Ren, H., Cai, W., & Ma, Z. (2017). How to measure carbon emission reduction in China’s public building sector: Retrospective decomposition analysis based on STIRPAT model in 2000–2015. Sustainability, 9(10), 1744. https://doi.org/10.3390/su9101744
Maghsoodi, A. I., & Khalilzadeh, M. (2018). Identification and evaluation of construction projects’ critical success factors employing fuzzy-topsis approach. KSCE Journal of Civil Engineering, 22(5), 1593–1605. https://doi.org/10.1007/s12205-017-1970-2
Malek Akhlagh, E., Moradi, M., Mehdizade, M., & Dorostkar Ahmadi, N. (2013). Innovation strategies, performance diversity and development: An empirical analysis in Iran construction and housing industry. Iranian Journal of Management Studies, 6(2), 31–60.
Mavi, R. K., & Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, 194, 751–765. https://doi.org/10.1016/j.jclepro.2018.05.120
Mikhailov, L. (2004). Group prioritization in the AHP by fuzzy preference programming method. Computers & Operations Research, 31(2), 293–301. https://doi.org/10.1016/S0305-0548(03)00012-1
Mohammed, A., Harris, I., & Govindan, K. (2019a). A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. International Journal of Production Economics, 217, 171–184. https://doi.org/10.1016/j.ijpe.2019.02.003
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019b). A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design. Computers & Industrial Engineering, 127, 297–312. https://doi.org/10.1016/j.cie.2018.09.052
Mousavi, S. M., Vahdani, B., Hashemi, H., & Ebrahimnejad, S. (2015). An artificial intelligence model-based locally linear neuro-fuzzy for construction project selection. Multiple-Valued Logic and Soft Computing, 25(6), 589–604.
Mowforth, M., & Munt, I. (2015). Tourism and sustainability: Development, globalisation and new tourism in the third world. Routledge. https://doi.org/10.4324/9781315795348
Nguyen, L. D., Le-Hoai, L., Tran, D. Q., Dang, C. N., & Nguyen, C. V. (2018). Fuzzy AHP with applications in evaluating construction project complexity. In A. R. Fayek (Ed.), Fuzzy hybrid computing in construction engineering and management: Theory and applications (pp. 277–299). Bingley: Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-868-220181007
Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of Civil Engineering and Management, 23(8), 1123–1135. https://doi.org/10.3846/13923730.2017.1388278
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Serrador, P., & Turner, R. (2015). The relationship between project success and project efficiency. Project Management Journal, 46(1), 30–39. https://doi.org/10.1002/pmj.21468
Shen, B., Li, Q., Dong, C., & Perry, P. (2017). Sustainability issues in textile and apparel supply chains. Sustainability, 9, 1592. https://doi.org/10.3390/su9091592
Siew, R. Y. J. (2016). Integrating sustainability into construction project portfolio management. KSCE Journal of Civil Engineering, 20(1), 101–108. https://doi.org/10.1007/s12205-015-0520-z
Singh, S., Olugu, E., Musa, S., & Mahat, A. (2018). Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework. Journal of Intelligent Manufacturing, 29(1), 1–18. https://doi.org/10.1007/s10845-015-1081-1
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
Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105–116. https://doi.org/10.1016/j.asoc.2014.01.003
Vahdani, B. (2016). Solving robot selection problem by a new interval-valued hesitant fuzzy multi-attributes group decision method. International Journal of Industrial Mathematics, 8(3), 231–240.
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28. https://doi.org/10.1016/0165-0114(78)90029-5
Zhou, F., Wang, X., & Goh, M. (2018a). Fuzzy extended VIKORbased mobile robot selection model for hospital pharmacy. International Journal of Advanced Robotic Systems, 15(4), 1–11. https://doi.org/10.1177/1729881418787315
Zhou, F., Wang, X., & Samvedi, A. (2018b). Quality improvement pilot program selection based on dynamic hybrid MCDM approach. Industrial Management & Data Systems, 118(1), 144–163. https://doi.org/10.1108/IMDS-11-2016-0498
Zolfani, S. H., Pourhossein, M., Yazdani, M., & Zavadskas, E. K. (2018). Evaluating construction projects of hotels based on environmental sustainability with MCDM framework. Alexandria Engineering Journal, 57(1), 357–365. https://doi.org/10.1016/j.aej.2016.11.002
Alzahrani, J. I., & Emsley, M. W. (2013). The impact of contractors’ attributes on construction project success: A post construction evaluation. International Journal of Project Management, 31(2), 313–322. https://doi.org/10.1016/j.ijproman.2012.06.006
Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: A ranking model based on fuzzy inference system. Applied Soft Computing, 12(6), 1668–1677. https://doi.org/10.1016/j.asoc.2012.01.023
Amindoust, A., & Saghafinia, A. (2017). Textile supplier selection in sustainable supply chain using a modular fuzzy inference system model. The Journal of The Textile Institute, 108(7), 1250–1258.
Borujeni, M. P., & Gitinavard, H. (2017). Evaluating the sustainable mining contractor selection problems: An imprecise last aggregation preference selection index method. Journal of Sustainable Mining, 16(4), 207–218. https://doi.org/10.1016/j.jsm.2017.12.006
Carr, V., & Tah, J. (2001). A fuzzy approach to construction project risk assessment and analysis: Construction project risk management system. Advances in Engineering Software, 32(10–11), 847–857. https://doi.org/10.1016/S0965-9978(01)00036-9
Castro-Alvarez, F., Marsters, P., de León Barido, D. P., & Kammen, D. M. (2018). Sustainability lessons from shale development in the United States for Mexico and other emerging unconventional oil and gas developers. Renewable and Sustainable Energy Reviews, 82, 1320–1332. https://doi.org/10.1016/j.rser.2017.08.082
Chen, S. H., & Hsieh, C. H. (1999). Optimization of fuzzy simple inventory models. In Proceedings of the IEEE International Conference on Fuzzy Systems (pp. 240–244). Seoul, Korea.
Doulabi, R. Z., & Asnaashari, E. (2016). Identifying success factors of healthcare facility construction projects in Iran. Procedia Engineering, 164, 409–415. https://doi.org/10.1016/j.proeng.2016.11.638
Ebrahimnejad, S., Naeini, M., Gitinavard, H., & Mousavi, S. M. (2017). Selection of IT outsourcing services’ activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach. Journal of Intelligent & Fuzzy Systems, 32(6), 4081–4093. https://doi.org/10.3233/JIFS-152520
Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision making in construction management: AHP and expert choice approach. Procedia Engineering, 172, 270–276. https://doi.org/10.1016/j.proeng.2017.02.111
Fallahpour, A., Olugu, E. U., Musa, S. N., Khezrimotlagh, D., & Wong, K. Y. (2016). An integrated model for green supplier selection under fuzzy environment: Application of data envelopment analysis and genetic programming approach. Neural Computing and Applications, 27(3), 707–725. https://doi.org/10.1007/s00521-015-1890-3
Frini, A., & BenAmor, S. (2015). A TOPSIS multi-criteria multiperiod approach for selecting projects in sustainable development context. In Proceedings of the International Conference on Industrial Engineering and Operations Management. Dubai, United Arab Emirates. https://doi.org/10.1109/IEOM.2015.7093900
Gitinavard, H., Pishvaee, M. S., & Jalalvand, F. (2017). A hierarchical multi-criteria group decision-making method based on TOPSIS and hesitant fuzzy information. International Journal of Applied Decision Sciences, 10(3), 213–232. https://doi.org/10.1504/IJADS.2017.085084
Govindan, K., Kadziński, M., Ehling, R., & Miebs, G. (2019). Selection of a sustainable third-party reverse logistics provider based on the robustness analysis of an outranking graph kernel conducted with ELECTRE I and SMAA. Omega, 85, 1–15. https://doi.org/10.1016/j.omega.2018.05.007
Gumusay, M. U., Koseoglu, G., & Bakirman, T. (2016). An assessment of site suitability for marina construction in Istanbul, Turkey, using GIS and AHP multicriteria decision analysis. Environmental Monitoring and Assessment, 188(12), 677. https://doi.org/10.1007/s10661-016-5677-5
Hatefi, S. M., & Tamošaitienė, J. (2018). Construction projects assessment based on the sustainable development criteria by an integrated fuzzy AHP and improved GRA model. Sustainability, 10(4), 991. https://doi.org/10.3390/su10040991
Heravi, G., Fathi, M., & Faeghi, S. (2017). Multi-criteria group decision-making method for optimal selection of sustainable industrial building options focused on petrochemical projects. Journal of Cleaner Production, 142, 2999–3013. https://doi.org/10.1016/j.jclepro.2016.10.168
Lambin, E. F., & Thorlakson, T. (2018). Sustainability standards: Interactions between private actors, civil society, and governments. Annual Review of Environment and Resources, 43, 369–393. https://doi.org/10.1146/annurev-environ-102017-025931
Lopes, J., Oliveira, R., & Abreu, M. I. (2017). The sustainability of the construction industry in Sub-Saharan Africa: Some new evidence from recent data. Procedia Engineering, 172, 657–664. https://doi.org/10.1016/j.proeng.2017.02.077
Lozano, R., Carpenter, A., & Huisingh, D. (2015). A review of ‘theories of the firm’and their contributions to corporate sustainability. Journal of Cleaner Production, 106, 430–442. https://doi.org/10.1016/j.jclepro.2014.05.007
Lückmann, P. (2015). Towards identifying success factors for cross-cultural project customer engagement: A literature review. Procedia Computer Science, 64, 324–333. https://doi.org/10.1016/j.procs.2015.08.496
Ma, M., Shen, L., Ren, H., Cai, W., & Ma, Z. (2017). How to measure carbon emission reduction in China’s public building sector: Retrospective decomposition analysis based on STIRPAT model in 2000–2015. Sustainability, 9(10), 1744. https://doi.org/10.3390/su9101744
Maghsoodi, A. I., & Khalilzadeh, M. (2018). Identification and evaluation of construction projects’ critical success factors employing fuzzy-topsis approach. KSCE Journal of Civil Engineering, 22(5), 1593–1605. https://doi.org/10.1007/s12205-017-1970-2
Malek Akhlagh, E., Moradi, M., Mehdizade, M., & Dorostkar Ahmadi, N. (2013). Innovation strategies, performance diversity and development: An empirical analysis in Iran construction and housing industry. Iranian Journal of Management Studies, 6(2), 31–60.
Mavi, R. K., & Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, 194, 751–765. https://doi.org/10.1016/j.jclepro.2018.05.120
Mikhailov, L. (2004). Group prioritization in the AHP by fuzzy preference programming method. Computers & Operations Research, 31(2), 293–301. https://doi.org/10.1016/S0305-0548(03)00012-1
Mohammed, A., Harris, I., & Govindan, K. (2019a). A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation. International Journal of Production Economics, 217, 171–184. https://doi.org/10.1016/j.ijpe.2019.02.003
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019b). A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design. Computers & Industrial Engineering, 127, 297–312. https://doi.org/10.1016/j.cie.2018.09.052
Mousavi, S. M., Vahdani, B., Hashemi, H., & Ebrahimnejad, S. (2015). An artificial intelligence model-based locally linear neuro-fuzzy for construction project selection. Multiple-Valued Logic and Soft Computing, 25(6), 589–604.
Mowforth, M., & Munt, I. (2015). Tourism and sustainability: Development, globalisation and new tourism in the third world. Routledge. https://doi.org/10.4324/9781315795348
Nguyen, L. D., Le-Hoai, L., Tran, D. Q., Dang, C. N., & Nguyen, C. V. (2018). Fuzzy AHP with applications in evaluating construction project complexity. In A. R. Fayek (Ed.), Fuzzy hybrid computing in construction engineering and management: Theory and applications (pp. 277–299). Bingley: Emerald Publishing Limited. https://doi.org/10.1108/978-1-78743-868-220181007
Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of Civil Engineering and Management, 23(8), 1123–1135. https://doi.org/10.3846/13923730.2017.1388278
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Serrador, P., & Turner, R. (2015). The relationship between project success and project efficiency. Project Management Journal, 46(1), 30–39. https://doi.org/10.1002/pmj.21468
Shen, B., Li, Q., Dong, C., & Perry, P. (2017). Sustainability issues in textile and apparel supply chains. Sustainability, 9, 1592. https://doi.org/10.3390/su9091592
Siew, R. Y. J. (2016). Integrating sustainability into construction project portfolio management. KSCE Journal of Civil Engineering, 20(1), 101–108. https://doi.org/10.1007/s12205-015-0520-z
Singh, S., Olugu, E., Musa, S., & Mahat, A. (2018). Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework. Journal of Intelligent Manufacturing, 29(1), 1–18. https://doi.org/10.1007/s10845-015-1081-1
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
Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105–116. https://doi.org/10.1016/j.asoc.2014.01.003
Vahdani, B. (2016). Solving robot selection problem by a new interval-valued hesitant fuzzy multi-attributes group decision method. International Journal of Industrial Mathematics, 8(3), 231–240.
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28. https://doi.org/10.1016/0165-0114(78)90029-5
Zhou, F., Wang, X., & Goh, M. (2018a). Fuzzy extended VIKORbased mobile robot selection model for hospital pharmacy. International Journal of Advanced Robotic Systems, 15(4), 1–11. https://doi.org/10.1177/1729881418787315
Zhou, F., Wang, X., & Samvedi, A. (2018b). Quality improvement pilot program selection based on dynamic hybrid MCDM approach. Industrial Management & Data Systems, 118(1), 144–163. https://doi.org/10.1108/IMDS-11-2016-0498
Zolfani, S. H., Pourhossein, M., Yazdani, M., & Zavadskas, E. K. (2018). Evaluating construction projects of hotels based on environmental sustainability with MCDM framework. Alexandria Engineering Journal, 57(1), 357–365. https://doi.org/10.1016/j.aej.2016.11.002