Project portfolio selection for construction contractors by MCDM–GIS approach

  • A. HashemizadehEmail author
  • Y. Ju
Original Paper


In this paper, project portfolio selection (PPS) is considered as one of the main steps in project portfolio management (PPM). PPS which depends on different criteria such as strategic, financial, as well as project specifications is also suggested for construction contractors. Although it is difficult to consider all these criteria, the decision will be inaccurate when some of them are ignored. The most common multi-criteria decision-making (MCDM) methodologies and geographic information system (GIS) are combined as a method, by which the potential projects obtain a strategic score by the analytical hierarchy process (AHP). Then, they are ranked according to technical criteria by the technique for order preference by similarity to ideal solution (TOPSIS). The proposed approach includes three ordinal phases. First, scoring potential projects due to the company strategy is conducted by the AHP to determine strategic-aligned projects (SAPs). Then, SAPs are ranked by TOPSIS. Finally, the final GIS sheet is prepared by the obtained technical score and communal criteria. This paper provides a clear and comprehensive insight to accept or reject SAPs by form a weighted sheet in GIS. It suggests that CCs select project portfolio according to all the financial and non-financial criteria. This approach can satisfy the PPM principals and resolve some of the shortcomings related to MCDM methods by GIS capacities to reduce complexity so that the decision-makers form project portfolios easily and effectively. This approach is described in a case study.


Project portfolio Portfolio management Strategy–project orientation Multi-criteria decision-making Analytic hierarchy process (AHP) Effective decision-making 



The authors wish to extend their full gratitude to all who assisted in conducting this research.


  1. Abdel-malak FF, Issa UH, Miky YH, Osman EA (2017) Applying decision-making techniques to civil engineering projects. Beni-Suef Univ J Basic Appl Sci 6(4):326–331CrossRefGoogle Scholar
  2. Al Garni HZ, Awasthi A (2017) Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Appl Energy 206:1225–1240CrossRefGoogle Scholar
  3. Alami Merrouni A, Elwali Elalaoui F, Ghennioui A, Mezrhab A, Mezrhab A (2018a) A GIS-AHP combination for the sites assessment of large-scale CSP plants with dry and wet cooling systems. Case study: Eastern Morocco. Sol Energy 166:2–12CrossRefGoogle Scholar
  4. Alami Merrouni A, Elwali Elalaoui F, Mezrhab A, Mezrhab A, Ghennioui A (2018b) Large scale PV sites selection by combining GIS and analytical hierarchy process. Case study: Eastern Morocco. Renew Energy 119:863–873CrossRefGoogle Scholar
  5. Archer NP, Ghasemzadeh F (1999) An integrated framework for project portfolio selection. Int J Project Manage 17(4):207–216CrossRefGoogle Scholar
  6. Church RL (2002) Geographical information systems and location science. Comput Oper Res 29(6):541–562CrossRefGoogle Scholar
  7. Costantino F, Di Gravio G, Nonino F (2015) Project selection in project portfolio management: an artificial neural network model based on critical success factors. Int J Proj Manag 33(8):1744–1754CrossRefGoogle Scholar
  8. da Silva CG, Meidanis J, Moura AV, Souza MA, Viadanna P, de Oliveira MR, de Oliveira MR et al (2017) An improved visualization-based approach for project portfolio selection. Comput Hum Behav 73:685–696CrossRefGoogle Scholar
  9. Dhiman R, Kalbar P, Inamdar AB (2018) GIS coupled multiple criteria decision making approach for classifying urban coastal areas in India. Habitat Int 71:125–134CrossRefGoogle Scholar
  10. Gao R, Nam HO, Ko W Il, Jang H (2018) Integrated system evaluation of nuclear fuel cycle options in China combined with an analytical MCDM framework. Energy Policy 114:221–233CrossRefGoogle Scholar
  11. Gudiel Pineda PJ, Liou JJH, Hsu C-C, Chuang Y-C (2018) An integrated MCDM model for improving airline operational and financial performance. J Air Transp Manag 68:103–117CrossRefGoogle Scholar
  12. Jeng DJ-F, Huang K-H (2015) Strategic project portfolio selection for national research institutes. J Bus Res 68(11):2305–2311CrossRefGoogle Scholar
  13. Jerbrant A, Karrbom Gustavsson T (2013) Managing project portfolios: balancing flexibility and structure by improvising. International Journal of Managing Projects in Business 6(1):152–172CrossRefGoogle Scholar
  14. Ju Y, Wang A (2012) Emergency alternative evaluation under group decision makers: a method of incorporating DS/AHP with extended TOPSIS. Expert Syst Appl 39(1):1315–1323CrossRefGoogle Scholar
  15. Khan MI (2018) Evaluating the strategies of compressed natural gas industry using an integrated SWOT and MCDM approach. J Clean Prod 172:1035–1052CrossRefGoogle Scholar
  16. Kiefer RW, Robbins ML (1973) Computer-based land use suitability maps. J Surv Mapp Div 11:39–62Google Scholar
  17. Killen CP (2017) Managing portfolio interdependencies: the effects of visual data representations on project portfolio decision making. Int J Manag Proj Bus 10(4):856–879CrossRefGoogle Scholar
  18. Lidelöw H, Simu K (2015) Understanding construction contractors and their operations strategies. Proc Econ Finance 21:48–56CrossRefGoogle Scholar
  19. Liu Y, Liu Y-K (2017) Distributionally robust fuzzy project portfolio optimization problem with interactive returns. Appl Soft Comput 56:655–668CrossRefGoogle Scholar
  20. Nanda S, Annadurai R, Barik KK (2017) Geospatial decipherment of groundwater potential of Kattankolathur block of Tamil Nadu using MCDM techniques. Remote Sens Appl Soc Environ 8:240–250Google Scholar
  21. Nassereddine M, Eskandari H (2017) An integrated MCDM approach to evaluate public transportation systems in Tehran. Transp Res Part A Policy Pract 106:427–439CrossRefGoogle Scholar
  22. Pérez F, Gómez T, Caballero R, Liern V (2018) Project portfolio selection and planning with fuzzy constraints. Technol Forecast Soc Change 131:117–129CrossRefGoogle Scholar
  23. PMI (2017) The standard for Portfolio management. Project Management Institute Inc., PennsylvaniaGoogle Scholar
  24. Relich M, Pawlewski P (2017) A fuzzy weighted average approach for selecting portfolio of new product development projects. Neurocomputing 231:19–27CrossRefGoogle Scholar
  25. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, LondonGoogle Scholar
  26. Sánchez-Lozano JM, Bernal-Conesa JA (2017) Environmental management of Natura 2000 network areas through the combination of Geographic Information Systems (GIS) with Multi-Criteria Decision Making (MCDM) methods. Case study in south-eastern Spain. Land Use Policy 63:86–97CrossRefGoogle Scholar
  27. Shafahi A, Haghani A (2014) Modeling contractors’ project selection and markup decisions influenced by eminence. Int J Proj Manage 32(8):1481–1493CrossRefGoogle Scholar
  28. Sivaraja CM, Sakthivel G (2017) Compression ignition engine performance modelling using hybrid MCDM techniques for the selection of optimum fish oil biodiesel blend at different injection timings. Energy 139:118–141CrossRefGoogle Scholar
  29. Villacreses G, Gaona G, Martínez-Gómez J, Jijón DJDJ (2017) Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: the case of continental Ecuador. Renew Energy 109:275–286CrossRefGoogle Scholar
  30. Wu Y, Xu C, Ke Y, Chen K, Sun X (2018) An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: case study in Zhejiang, China. Energy 143:295–309CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2019

Authors and Affiliations

  1. 1.School of Management and EconomicsBeijing Institute of TechnologyBeijingChina

Personalised recommendations