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Abd Jamil, A H and Fathi, M S (2018) Contractual challenges for BIM-based construction projects: a systematic review. Built Environment Project and Asset Management, 8(04), 372–85.

Adefila, A, Abuzeinab, A, Whitehead, T and Oyinlola, M (2020) Bottle house: utilising appreciative inquiry to develop a user acceptance model. Built Environment Project and Asset Management, 10(04), 567–83.

Al-Gahtani, K, Alsulaihi, I, Ali, M and Marzouk, M (2017) Production of green concrete using recycled waste aggregate and byproducts. Built Environment Project and Asset Management, 7(04), 413-25.

Amadi, C, Carrillo, P and Tuuli, M (2018) Stakeholder management in PPP projects: external stakeholders’ perspective. Built Environment Project and Asset Management, 8(04), 403–14.

Carrière, S, Weigend Rodríguez, R, Pey, P, Pomponi, F and Ramakrishna, S (2020) Circular cities: the case of Singapore. Built Environment Project and Asset Management, 10(04), 491–507.

Crippa, J, Araujo, A M, Bem, D, Ugaya, C M and Scheer, S (2020) A systematic review of BIM usage for life cycle impact assessment. Built Environment Project and Asset Management, 10(04), 603–18.

Farghaly, K, Abanda, F, Vidalakis, C and Wood, G (2019) BIM-linked data integration for asset management. Built Environment Project and Asset Management, 9(04), 489–502.

Hadiwattege, C, Senaratne, S, Sandanayake, Y and Fernando, N G (2018) Academic research in emerging knowledge-based economies. Built Environment Project and Asset Management, 8(04), 415–28.

Hettige, N, Perera, B A K S and Mallawaarachchi, H (2017) A framework for adopting green leasing in developing countries: The case of Sri Lanka. Built Environment Project and Asset Management, 7(04), 377-87.

Ikediashi, D I, Ogunlana, S O and Odesola, I A (2015) Service quality and user satisfaction of outsourced facilities management services in Nigeria’s public hospitals. Built Environment Project and Asset Management, 5(04), 363-79.

Jafari, A and Akhavian, R (2019) Driving forces for the US residential housing price: a predictive analysis. Built Environment Project and Asset Management, 9(04), 515–29.

Jumas, D, Mohd-Rahim, F A, Zainon, N and Utama, W P (2018) Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects. Built Environment Project and Asset Management, 8(04), 348–57.

Khallaf, R, Naderpajouh, N and Hastak, M (2018) A systematic approach to develop risk registry frameworks for complex projects. Built Environment Project and Asset Management, 8(04), 334–47.

Kim, K P and Park, K S (2016) Primary BIM dataset for refurbishing flood risk vulnerable housing in the UK. Built Environment Project and Asset Management, 6(04), 365-78.

Kumaraswamy, M, Wong, K K W and Chung, J (2017) Focusing megaproject strategies on sustainable best value of stakeholders. Built Environment Project and Asset Management, 7(04), 441-55.

Liu, C and Li, Y (2016) Measuring eco-roof mitigation on flash floods via gis simulation. Built Environment Project and Asset Management, 6(04), 415-27.

Madanayake, U H and Egbu, C (2019) Critical analysis for big data studies in construction: significant gaps in knowledge. Built Environment Project and Asset Management, 9(04), 530–47.

Manu, P, Mahamadu, A, Booth, C, Olomolaiye, P, Ibrahim, A D and Coker, A (2018) Assessment of procurement capacity challenges inhibiting public infrastructure procurement. Built Environment Project and Asset Management, 8(04), 386–402.

Marzouk, M and Enaba, M (2019) Analyzing project data in BIM with descriptive analytics to improve project performance. Built Environment Project and Asset Management, 9(04), 476–88.

Mitra, A and Munir, K (2019) Influence of Big Data in managing cyber assets. Built Environment Project and Asset Management, 9(04), 503–14.

Nguyen, T P and Chileshe, N (2015) Revisiting the construction project failure factors in Vietnam. Built Environment Project and Asset Management, 5(04), 398-416.

Nielsen, L-H K, Akanmu, A and Anumba, C J (2015) Comparative analysis of back-to-back subcontracts in the construction and telecommunications industries. Built Environment Project and Asset Management, 5(04), 446-60.

Ofori-Boadu, A N, Shofoluwe, M A, Owusu-Manu, D-G, Holt, G D and Edwards, D (2015) Analysis of US commercial buildings’ energy efficiency programs. Built Environment Project and Asset Management, 5(04), 349-62.

Okorafor, C, Emuze, F, Das, D, Awuzie, B O and Haupt, T (2020) An artefact for improving the delivery of building energy retrofit project in South Africa. Built Environment Project and Asset Management, 10(04), 619–35.

Olatunji, O A, Orundami, A O and Ogundare, O (2018) Causal relationship between material price fluctuation and project’s outturn costs. Built Environment Project and Asset Management, 8(04), 358–71.

Omotayo, T, Olanipekun, A, Obi, L and Boateng, P (2020) A systems thinking approach for incremental reduction of non-physical waste. Built Environment Project and Asset Management, 10(04), 509–28.

Osunsanmi, T O, Aigbavboa, C O, Emmanuel Oke, A and Liphadzi, M (2020) Appraisal of stakeholders' willingness to adopt construction 4.0 technologies for construction projects. Built Environment Project and Asset Management, 10(04), 547–65.

Oyewobi, L O, Windapo, A O and James, R O B (2015) An empirical analysis of construction organisations’ competitive strategies and performance. Built Environment Project and Asset Management, 5(04), 417-31.

Ozcan, D G (2017) An analytic network process model to examine LEED-certified buildings’ operational performance. Built Environment Project and Asset Management, 7(04), 366-76.

Pathirage, C and Al-Khaili, K (2016) Disaster vulnerability of Emirati energy sector and barriers to enhance resilience. Built Environment Project and Asset Management, 6(04), 403-14.

Ram, J, Afridi, N K and Khan, K A (2019) Adoption of Big Data analytics in construction: development of a conceptual model. Built Environment Project and Asset Management, 9(04), 564–79.

Rose, J and Jayawickrama, J (2016) Capacity building of institutions for disaster risk reduction: Learning from communities as first responders. Built Environment Project and Asset Management, 6(04), 391-402.

Seneviratne, K, Amaratunga, D and Haigh, R (2015) Post-conflict housing reconstruction: Exploring the challenges of addressing housing needs in Sri Lanka. Built Environment Project and Asset Management, 5(04), 432-45.

Waidyasekara, K G A S, De Silva, L and Rameezdeen, R (2017) Application of “r” principles to enhance the efficiency of water usage in construction sites. Built Environment Project and Asset Management, 7(04), 400-12.

Walimuni, P C, Samaraweera, A and De Silva, L (2017) Payment mechanisms for contractors for better environmental hazard controlling in road construction projects. Built Environment Project and Asset Management, 7(04), 426-40.

Wedawatta, G and Ingirige, B (2016) A conceptual framework for understanding resilience of construction SMEs to extreme weather events. Built Environment Project and Asset Management, 6(04), 428-43.

Wedawatta, G, Kulatunga, U, Amaratunga, D and Parvez, A (2016) Disaster risk reduction infrastructure requirements for south-western Bangladesh: Perspectives of local communities. Built Environment Project and Asset Management, 6(04), 379-90.

Weigend Rodríguez, R, Pomponi, F, Webster, K and D'Amico, B (2020) The future of the circular economy and the circular economy of the future. Built Environment Project and Asset Management, 10(04), 529–46.

Windapo, A O and Moghayedi, A (2020) Adoption of smart technologies and circular economy performance of buildings. Built Environment Project and Asset Management, 10(04), 585–601.

Yap, J Y L, Ho, C C and Ting, C (2019) A systematic review of the applications of multi-criteria decision-making methods in site selection problems. Built Environment Project and Asset Management, 9(04), 548–63.

  • Type: Journal Article
  • Keywords: Logistics; TOPSIS; Analytical hierarchy process; Retail; Public service; ELECTRE; Site selection; Energy generation; Multi-criteria decision-making; PROMTHEE;
  • ISBN/ISSN: 2044-124X
  • URL: https://doi.org/10.1108/BEPAM-05-2018-0078
  • Abstract:
    The purpose of this paper is to perform a systematic review on the application of different multi-criteria decision-making (MCDM) methods in solving the site selection problem across multiple problem domains. The domains are energy generation, logistics, public services and retail facilities. This study aims to answer the following research questions: Which evaluating criteria were used for each site selection problem domain? Which MCDM methods were frequently applied in a particular site selection problem domain? Design/methodology/approach The goals of the systematic review were to identify the evaluating criteria as well as the MCDM method used for each problem domain. A total of 81 recent papers (2014–2018) including 32 papers published in conference proceedings and 49 journal articles from various databases including IEEE Xplore, PubMed, Springer, Taylor and Francis as well as ScienceDirect were evaluated. Findings This study has shown that site selection for energy generation facilities is the most active site selection problem domain, and that the analytic hierarchy process (AHP) method is the most commonly used MCDM method for site selection. For energy generation, the criteria which were most used were geographical elements, land use, cost and environmental impact. For logistics, frequently used criteria were geographical elements and distance, while for public services population density, supply and demand, geographical layout and cost were the criteria most used. Criteria useful for retail facilities were the size (space) of the store, demographics of the site, the site characteristics and rental of the site (cost). Research limitations/implications This study is limited to reviewing papers which were published in the years 2014–2018 only, and only covers the domains of energy generation, logistics, public services and retail facilities. Practical implications MCDM is a viable tool to be used for solving the site selection problem across the domains of energy generation, logistics, public services and retail facilities. The usage of MCDM continues to be relevant as a complement to machine learning, even as data originating from embedded IoT devices in built environments becomes increasingly Big Data like. Originality/value Previous systematic review studies for MDCM and built environments have either focused on studying the MCDM techniques itself, or have focused on the application of MCDM for site selection in a single problem domain. In this study, a critical review of MCDM techniques used for site selection as well as the critical criteria used during the MCDM process of site selection was performed on four different built environment domains.

Zeb, J (2017) An eco asset ontology towards effective eco asset management. Built Environment Project and Asset Management, 7(04), 388-99.

Zeb, J, Froese, T and Vanier, D (2015) An ontology-supported asset information integrator system in infrastructure management. Built Environment Project and Asset Management, 5(04), 380-97.

Zhao, X and Pan, W (2017) Co-productive interrelations between business model and zero-carbon building: A conceptual model. Built Environment Project and Asset Management, 7(04), 353-65.