Energy-saving building program evaluation with an integrated method under linguistic environment
Abstract
In the context of sustainable development, building energy conservation has become the development trend of the construction industry. The selection of energy-saving building program, as a multi-criteria decision-making (MCDM) problem, has a direct influence on the actual energy-saving effect. In this paper, an integrated MCDM method combining the extended best worst method (BWM) and Weighted Aggregated Sum Product Assessment (WASPAS) method is proposed to solve the energy-saving building program selection problem under the linguistic Pythagorean fuzzy environment. The Linguistic Pythagorean fuzzy sets (LPFSs) are used to model the uncertain evaluation information of experts. The extended BWM is developed to determine the weights of criteria, while the extended WASPAS method is proposed to determine the ranking of alternatives. To validate the applicability and reliability of the proposed method, this paper presents a numerical example of the selection problem for energy-saving building programs. Some managerial insights are also given for practitioners to use the proposed method.
Keyword : energy-saving building, construction industry, multi-criteria decision making, linguistic Pythagorean fuzzy set, weighted aggregated sum product assessment, best worst method
How to Cite
Huang, M., Zhang, X., Ren, R., Liao, H., Zavadskas, E. K., & Antuchevičienė , J. (2020). Energy-saving building program evaluation with an integrated method under linguistic environment. Journal of Civil Engineering and Management, 26(5), 447-458. https://doi.org/10.3846/jcem.2020.12647
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Bribian, I. Z., Capilla, A. V., & Uson, A. A. (2011). Life cycle assessment of building materials: Comparative analysis of energy and environmental impacts and evaluation of the ecoefficiency improvement potential. Building and Environment, 46(5), 1133–1140. https://doi.org/10.1016/j.buildenv.2010.12.002
Chen, L., & Pan, W. (2016). BIM-aided variable fuzzy multicriteria decision making of low-carbon building measures selection. Sustainable Cities and Society, 27, 222–232. https://doi.org/10.1016/j.scs.2016.04.008
Chen, S. M., & Tan, J. M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 67(2), 163–172. https://doi.org/10.1016/0165-0114(94)90084-1
Chen, T. Y., & Tsao, C. Y. (2008). The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, 159(11), 1410–1428. https://doi.org/10.1016/j.fss.2007.11.004
Chenari, B., Carrilho, J. D., & da Silva, M. G. (2016). Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review. Renewable & Sustainable Energy Reviews, 59, 1426–1447. https://doi.org/10.1016/j.rser.2016.01.074
Danish, M. S. S., Senjyu, T., Ibrahimi, A. M., Ahmadi, M., & Howlader, A. M. (2019). A managed framework for energyefficient building. Journal of Building Engineering, 21, 120– 128. https://doi.org/10.1016/j.jobe.2018.10.013
Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multiobjective optimization of the building energy performance: A simulation-based approach by means of Particle Swarm Optimization (PSO). Applied Energy, 170, 293–303. https://doi.org/10.1016/j.apenergy.2016.02.141
Deveci, M., Canitez, F., & Gokasar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777–791. https://doi.org/10.1016/j.scs.2018.05.034
Garg, H. (2018). Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. International Journal of Intelligent Systems, 33(6), 1234–1263. https://doi.org/10.1002/int.21979
Geng, G., Wang, Z. X., Zhao, J., & Zhu, N. (2015). Suitability assessment of building energy saving technologies for office buildings in cold areas of China based on an assessment framework. Energy Conversion and Management, 103, 650–664. https://doi.org/10.1016/j.enconman.2015.06.087
Guo, S., & Zhao, H. R. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. KnowledgeBased Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
Harish, V., & Kumar, A. (2016). A review on modeling and simulation of building energy systems. Renewable & Sustainable Energy Reviews, 56, 1272–1292. https://doi.org/10.1016/j.rser.2015.12.040
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
Huang, M. Q., & Wang, B. (2014). Evaluating green performance of building products based on gray relational analysis and analytic hierarchy process. Environment Progress & Sustainable Energy, 33(4), 1389–1395. https://doi.org/10.1002/ep.11884
Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Esmaeili, A. (2016). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type2 fuzzy sets. Journal of Cleaner Production, 137, 213–229. https://doi.org/10.1016/j.jclepro.2016.07.031
Khoshnava, S. M., Rostami, R., Valipour, A., Ismail, M., & Rahmat, A. R. (2018). Rank of green building material criteria based on the three pillars of sustainability using the hybrid multi criteria decision making method. Journal of Cleaner Production, 173, 82–99. https://doi.org/10.1016/j.jclepro.2016.10.066
Kim, J. J. (2017). Economic analysis on energy saving technologies for complex manufacturing building. Resources Conservation and Recycling, 123, 249–254. https://doi.org/10.1016/j.resconrec.2016.03.018
Lee, B., Trcka, M., & Hensen, J. L. M. (2011). Embodied energy of building materials and green building rating systems – A case study for industrial halls. Sustainable Cities and Society, 1(2), 67–71. https://doi.org/10.1016/j.scs.2011.02.002
Li, J., Wang, J. Q., & Hu, J. H. (2019). Multi-criteria decisionmaking method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics, 10(7), 1671–1685. https://doi.org/10.1007/s13042-018-0845-2
Liao, H. C., Qin, R., Gao, C. Y., Wu, X.L., Hafezalkotob, A., & Herrera, F. (2019). Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Information Fusion, 48, 39–54. https://doi.org/10.1016/j.inffus.2018.08.006
Liao, H. C., & Wu, X. L. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94, 102058. https://doi.org/10.1016/j.omega.2019.04.001
Liu, H. C., Quan, M. Y., Li, Z. W., & Wang, Z. L. (2019). A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment. Information Sciences, 486, 254–270. https://doi.org/10.1016/j.ins.2019.02.056
Liu, Y., Yang, L., Zheng, W. X., Liu, T., Zhang, X. R., & Liu, J. P. (2018). A novel building energy efficiency evaluation index: Establishment of calculation model and application. Energy Conversion and Management, 166, 522–533. https://doi.org/10.1016/j.enconman.2018.03.090
Lu, S. L., Fan M. C., & Zhao, Y. Q. (2018). A system to preevaluate the suitability of energy-saving technology for green buildings. Sustainability, 10(10), 3777. https://doi.org/10.3390/su10103777
Mathiyazhagan, K., Gnanavelbabu, A., & Lokesh Prabhuraj, B. (2019). A sustainable assessment model for material selection in construction industries perspective using hybrid MCDM approaches. Journal of Advances in Management Research, 16(2), 234–259. https://doi.org/10.1108/JAMR-09-2018-0085
Mi, X. M., & Liao, H. C. (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. M., Tang, M., Liao, H. C., Shen, W. J., & 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
Mishra, A. R., & Rani, P. (2018). Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy. Group Decision and Negotiation, 27(6), 1047–1078. https://doi.org/10.1007/s10726-018-9593-7
Mou, Q., Xu, Z. S., & Liao, H. C. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224–239. https://doi.org/10.1016/j.ins.2016.08.074
Mou, Q., Xu, Z. S., & Liao, H. C. (2017). A graph based group decision making approach with intuitionistic fuzzy preference relations. Computer and Industrial Engineering, 110, 138–150. https://doi.org/10.1016/j.cie.2017.05.033
Ren, R. X., Liao, H. C., Al-Barakati, A., & Cavallaro, F. (2019). Electric vehicle charging station site selection by an integrated SWARA-WASPAS method with hesitant fuzzy linguistic information. Transformations in Business & Economics, 18(2), 103–123.
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, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
Sanayei, A., Mousavi, S. F., & Yazdankhah, A. (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37(1), 24–30. https://doi.org/10.1016/j.eswa.2009.04.063
Si, J., & Marjanovic-Halburd, L. (2018). Criteria weighting for green technology selection as part of retrofit decision-making process for existing non-domestic buildings. Sustainable Cities and Society, 41, 625–638. https://doi.org/10.1016/j.scs.2018.05.051
Si, J., Marjanovic-Halburd, L., Nasiri, F., & Bell, S. (2016). Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method. Sustainable Cities and Society, 27, 106–115. https://doi.org/10.1016/j.scs.2016.06.013
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers Communication & Control, 10(6), 873–888. https://doi.org/10.15837/ijccc.2015.6.2078
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Wang, J. Q., Wu, J. T., Wang, J., Zhang, H. Y., & Chen, X. H. (2014). Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Information Sciences, 288, 55–72. https://doi.org/10.1016/j.ins.2014.07.034
Wang, X., Feng, W., Cai, W. G., Ren, H., Ding, C., & Zhou, N. (2019). Do residential building energy efficiency standards reduce energy consumption in China? – A data-driven method to validate the actual performance of building energy efficiency standards. Energy Policy, 131, 82–89. https://doi.org/10.1016/j.enpol.2019.04.022
Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transaction on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yu, X. H., Zhang, S. J., Liao, X. L., & Qi, X. L. (2018). ELECTRE methods in prioritized MCDM environment. Information Sciences, 424, 301–316. https://doi.org/10.1016/j.ins.2017.09.061
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810
Bribian, I. Z., Capilla, A. V., & Uson, A. A. (2011). Life cycle assessment of building materials: Comparative analysis of energy and environmental impacts and evaluation of the ecoefficiency improvement potential. Building and Environment, 46(5), 1133–1140. https://doi.org/10.1016/j.buildenv.2010.12.002
Chen, L., & Pan, W. (2016). BIM-aided variable fuzzy multicriteria decision making of low-carbon building measures selection. Sustainable Cities and Society, 27, 222–232. https://doi.org/10.1016/j.scs.2016.04.008
Chen, S. M., & Tan, J. M. (1994). Handling multicriteria fuzzy decision-making problems based on vague set theory. Fuzzy Sets and Systems, 67(2), 163–172. https://doi.org/10.1016/0165-0114(94)90084-1
Chen, T. Y., & Tsao, C. Y. (2008). The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, 159(11), 1410–1428. https://doi.org/10.1016/j.fss.2007.11.004
Chenari, B., Carrilho, J. D., & da Silva, M. G. (2016). Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review. Renewable & Sustainable Energy Reviews, 59, 1426–1447. https://doi.org/10.1016/j.rser.2016.01.074
Danish, M. S. S., Senjyu, T., Ibrahimi, A. M., Ahmadi, M., & Howlader, A. M. (2019). A managed framework for energyefficient building. Journal of Building Engineering, 21, 120– 128. https://doi.org/10.1016/j.jobe.2018.10.013
Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multiobjective optimization of the building energy performance: A simulation-based approach by means of Particle Swarm Optimization (PSO). Applied Energy, 170, 293–303. https://doi.org/10.1016/j.apenergy.2016.02.141
Deveci, M., Canitez, F., & Gokasar, I. (2018). WASPAS and TOPSIS based interval type-2 fuzzy MCDM method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777–791. https://doi.org/10.1016/j.scs.2018.05.034
Garg, H. (2018). Linguistic Pythagorean fuzzy sets and its applications in multiattribute decision-making process. International Journal of Intelligent Systems, 33(6), 1234–1263. https://doi.org/10.1002/int.21979
Geng, G., Wang, Z. X., Zhao, J., & Zhu, N. (2015). Suitability assessment of building energy saving technologies for office buildings in cold areas of China based on an assessment framework. Energy Conversion and Management, 103, 650–664. https://doi.org/10.1016/j.enconman.2015.06.087
Guo, S., & Zhao, H. R. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. KnowledgeBased Systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
Harish, V., & Kumar, A. (2016). A review on modeling and simulation of building energy systems. Renewable & Sustainable Energy Reviews, 56, 1272–1292. https://doi.org/10.1016/j.rser.2015.12.040
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
Huang, M. Q., & Wang, B. (2014). Evaluating green performance of building products based on gray relational analysis and analytic hierarchy process. Environment Progress & Sustainable Energy, 33(4), 1389–1395. https://doi.org/10.1002/ep.11884
Keshavarz Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Esmaeili, A. (2016). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type2 fuzzy sets. Journal of Cleaner Production, 137, 213–229. https://doi.org/10.1016/j.jclepro.2016.07.031
Khoshnava, S. M., Rostami, R., Valipour, A., Ismail, M., & Rahmat, A. R. (2018). Rank of green building material criteria based on the three pillars of sustainability using the hybrid multi criteria decision making method. Journal of Cleaner Production, 173, 82–99. https://doi.org/10.1016/j.jclepro.2016.10.066
Kim, J. J. (2017). Economic analysis on energy saving technologies for complex manufacturing building. Resources Conservation and Recycling, 123, 249–254. https://doi.org/10.1016/j.resconrec.2016.03.018
Lee, B., Trcka, M., & Hensen, J. L. M. (2011). Embodied energy of building materials and green building rating systems – A case study for industrial halls. Sustainable Cities and Society, 1(2), 67–71. https://doi.org/10.1016/j.scs.2011.02.002
Li, J., Wang, J. Q., & Hu, J. H. (2019). Multi-criteria decisionmaking method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics, 10(7), 1671–1685. https://doi.org/10.1007/s13042-018-0845-2
Liao, H. C., Qin, R., Gao, C. Y., Wu, X.L., Hafezalkotob, A., & Herrera, F. (2019). Score-HeDLiSF: A score function of hesitant fuzzy linguistic term set based on hesitant degrees and linguistic scale functions: an application to unbalanced hesitant fuzzy linguistic MULTIMOORA. Information Fusion, 48, 39–54. https://doi.org/10.1016/j.inffus.2018.08.006
Liao, H. C., & Wu, X. L. (2020). DNMA: A double normalization-based multiple aggregation method for multi-expert multi-criteria decision making. Omega, 94, 102058. https://doi.org/10.1016/j.omega.2019.04.001
Liu, H. C., Quan, M. Y., Li, Z. W., & Wang, Z. L. (2019). A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment. Information Sciences, 486, 254–270. https://doi.org/10.1016/j.ins.2019.02.056
Liu, Y., Yang, L., Zheng, W. X., Liu, T., Zhang, X. R., & Liu, J. P. (2018). A novel building energy efficiency evaluation index: Establishment of calculation model and application. Energy Conversion and Management, 166, 522–533. https://doi.org/10.1016/j.enconman.2018.03.090
Lu, S. L., Fan M. C., & Zhao, Y. Q. (2018). A system to preevaluate the suitability of energy-saving technology for green buildings. Sustainability, 10(10), 3777. https://doi.org/10.3390/su10103777
Mathiyazhagan, K., Gnanavelbabu, A., & Lokesh Prabhuraj, B. (2019). A sustainable assessment model for material selection in construction industries perspective using hybrid MCDM approaches. Journal of Advances in Management Research, 16(2), 234–259. https://doi.org/10.1108/JAMR-09-2018-0085
Mi, X. M., & Liao, H. C. (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. M., Tang, M., Liao, H. C., Shen, W. J., & 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
Mishra, A. R., & Rani, P. (2018). Interval-valued intuitionistic fuzzy WASPAS method: application in reservoir flood control management policy. Group Decision and Negotiation, 27(6), 1047–1078. https://doi.org/10.1007/s10726-018-9593-7
Mou, Q., Xu, Z. S., & Liao, H. C. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224–239. https://doi.org/10.1016/j.ins.2016.08.074
Mou, Q., Xu, Z. S., & Liao, H. C. (2017). A graph based group decision making approach with intuitionistic fuzzy preference relations. Computer and Industrial Engineering, 110, 138–150. https://doi.org/10.1016/j.cie.2017.05.033
Ren, R. X., Liao, H. C., Al-Barakati, A., & Cavallaro, F. (2019). Electric vehicle charging station site selection by an integrated SWARA-WASPAS method with hesitant fuzzy linguistic information. Transformations in Business & Economics, 18(2), 103–123.
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, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
Sanayei, A., Mousavi, S. F., & Yazdankhah, A. (2010). Group decision making process for supplier selection with VIKOR under fuzzy environment. Expert Systems with Applications, 37(1), 24–30. https://doi.org/10.1016/j.eswa.2009.04.063
Si, J., & Marjanovic-Halburd, L. (2018). Criteria weighting for green technology selection as part of retrofit decision-making process for existing non-domestic buildings. Sustainable Cities and Society, 41, 625–638. https://doi.org/10.1016/j.scs.2018.05.051
Si, J., Marjanovic-Halburd, L., Nasiri, F., & Bell, S. (2016). Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method. Sustainable Cities and Society, 27, 106–115. https://doi.org/10.1016/j.scs.2016.06.013
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers Communication & Control, 10(6), 873–888. https://doi.org/10.15837/ijccc.2015.6.2078
Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Wang, J. Q., Wu, J. T., Wang, J., Zhang, H. Y., & Chen, X. H. (2014). Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Information Sciences, 288, 55–72. https://doi.org/10.1016/j.ins.2014.07.034
Wang, X., Feng, W., Cai, W. G., Ren, H., Ding, C., & Zhou, N. (2019). Do residential building energy efficiency standards reduce energy consumption in China? – A data-driven method to validate the actual performance of building energy efficiency standards. Energy Policy, 131, 82–89. https://doi.org/10.1016/j.enpol.2019.04.022
Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transaction on Fuzzy Systems, 22(4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989
Yu, X. H., Zhang, S. J., Liao, X. L., & Qi, X. L. (2018). ELECTRE methods in prioritized MCDM environment. Information Sciences, 424, 301–316. https://doi.org/10.1016/j.ins.2017.09.061
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810