A Novel Decision Support System for Discrete Cost-CO2 Emission Trade-off in Construction Projects: The Usage of Imitate Genetic Algorithm

Document Type : Research Paper


1 Assistant Prof., Faculty of Material and Industrial Engineering, University of Semnan, Semnan, Iran

2 MSc. Student in MBA, Faculty of Material and Industrial Engineering, University of Semnan, Semnan, Iran


Under an intensive competitive environment, the construction industry is facing pressure to meet the higher customer expectations under a tighter budget. On the other hand, construction is one of the main sectors generating greenhouse gases. According to published statistics, construction industry is one of the most important resources of greenhouse emissions in the world. Therefore, based on the growing attention to the environment situation and according to the enabler capabilities of the decision support systems, in this paper, a genetic algorithm-based decision support system for solving a trade-off problem between the cost and the amount of CO2 emissions in construction projects is proposed. The genetic algorithm proposing here, is the new algorithm, named “imitate genetic algorithm”. For showing the reliability of the proposed algorithm, the results of its application have been compared with the results of classic genetic algorithm for 108 problems with different sizes. The results show the excellence of the proposed algorithm in comparison with classic genetic algorithm.


Main Subjects

Acquaye, A. A. & Duffy, A. P. (2010). Input–output analysis of Irish construction sector greenhouse gas emissions. Building and Environment, 45 (3): 784-791.
Agarwal, V., Upadhyay, R.K. & Pathak, B.K. (2013). A State of Art Review on Time Cost Trade off Problems in Project Scheduling. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2 (5): 36-43.
Buchanan, A. H. & Honey, B. G. (1994). Energy and carbon dioxide implications of building construction. Energy and Buildings, 20 (3): 205-217.
Calhoun, C. J. (2010). Robert K. Merton: Sociology of Science and Sociology as Science, New York: Columbia UP. ISBN 978-0-231-15112-2.
Cole, R. J. (1999). Energy and greenhouse gas emissions associated with the construction of alternative structural systems. Building and Environment, 34 (3): 335-348.
Dawson, E. M. & Chatman, E. A. (2001). Reference group theory with implications for information studies: a theoretical essay. Information Research, 6(3). Retrieved from http://InformationR.net/6-3/paper105.html.
Dimoudi, A. & Tompa, C. (2008). Energy and environmental indicators related to construction of office buildings. Resources, Conservation and Recycling, 53(1–2): 86-95.
Ding, G.K.C. (2008). Sustainable construction-the role of environmental assessment tools. Journal of Environmental Management, 86 (3): 451-464.
EPA (2009). Potential for reducing greenhouse gas emissions in the construction sector, Washington D.C.: Environmental Protection Agency.
Feng, C. W., Liu, L. & Burns, S. A. (1997). Using genetic algorithms to solve construction time-cost trade-off problems. Journal of Computing in Civil Engineering, 11 (3):184-189.
Goldberg, DE. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, M.A., USA: Addison-Wesley.
González, M. J. & Navarro, J. G. (2006). Assessment of the decrease of CO2 emissions in the construction field through the selection of materials: practical case study of three houses of low environmental impact, Building and Environment, 41 (7): 902-909.
Holton, G. (2004). Robert K. Merton - Biographical Memoirs, proceedings of the American philosophical society, 148 (4): 505-517.
Kalhor, Khanzadi, M., Eshtehardian, E. & Afshar, A. (2011). Stochastic time–cost optimization using non-dominated archiving ant colony approach. Automation in Construction, 20 (8): 1193-1203.
Ke, H. & Ma, J. (2014). Modeling project time–cost trade off in fuzzy random environment. Applied Soft Computing, (19): 80–85.
Ke, H. (2014). A genetic algorithm-based optimizing approach for project time-cost trade-off with uncertain measure. Journal of Uncertainty Analysis and Applications, 2(1):8. DOI: 10.1186/2195-5468-2-8.
Ke, H., Maa, W., Ni, Y. (2009). Optimization models and a GA-based algorithm for stochastic time-cost trade-off problem, Applied Mathematics and Computation, 215(1): 308-313.
Klanšek, U. & Pšunder, M. (2012). MINLP optimization model for the nonlinear discrete time–cost trade-off problem. Advances in Engineering Software,48 (1): 6–16.
Lit, H. & Love, P. (1997). Using improved genetic algorithms to facilitate time-cost optimization. journal of construction engineering and management, 123 (3): 233-237.
Liu, L., Burns, S.A. & Feng, C.W. (1995). Construction time-cost trade-off analysis using LP/IP hybrid method, Journal of construction engineering and management, 121(4): 446-454.
Liu, S., Tao, R. & Ming Tam, C. (2013). Optimizing cost and CO2 emission for construction projects using particle swarm optimization, Habitat International, 37 (1): 155-162.
Mokhtari, H., Baradaran Kazemzadeh, R. & Salmasnia A. (2011). Time-Cost Tradeoff Analysis in Project Management: An Ant System Approach, IEEE Transactions on Engineering Management, 58 (1): 36-43.
Monghasemi, S., Nikoo, M. R., Khaksar Fasaee, M. A. & Adamowski, J. (2014). A Novel Multi Criteria Decision Making Model for Optimizing Time-Cost-Quality Trade-off Problems in Construction Projects, Expert Systems with Applications. http://dx.doi.org/10.1016/j.eswa.2014.11.032.
Nabipoor Afruzi, Roghanian, E., Najafi, A.A. & Mazinani, M. (2013). A multi-mode resource-constrained discrete time–cost tradeoff problem solving using an adjusted fuzzy dominance genetic algorithm. Scientia Iranica, 20 (3): 931–944.
Pathak, B. K. & Srivastava, S. (2014). Integrated Fuzzy–HMH for project uncertainties in time–cost tradeoff problem, Applied Soft Computing, 21: 320-329.
Rahimi, M. & Iranmanesh, H. (2008). Multi Objective Particle Swarm Optimization for a Discrete Time, Cost and Quality Trade -off Problem, World Applied Sciences Journal, 4 (2): 270-276.
Singh, G. & Ernst, A. T. (2011). Resource constraint scheduling with a fractional shared resource. Operations Research Letters, 39 (5): 363–368.
Sonmez, R. & Bettemir, Ö. H. (2012). A hybrid genetic algorithm for the discrete time–cost trade-off problem. Expert Systems with Applications, 39 (13): 11428–11434.
Tavana, M., Abtahi, A.R. & Khalili-Damghani, K. (2014). A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert Systems with Applications, 41 (4): 1830–1846.
Wuliang, P. & Chengen, W. (2009). A multi-mode resource-constrained discrete time–cost tradeoff problem and its genetic algorithm based solution. International Journal of Project Management, 27 (6): 600–609.
Xu, J., Zheng, H., Zeng, Z., Wu, S. & Shen, M. (2012). Discrete time–cost–environment trade-off problem for large-scale construction systems with multiple modes under fuzzy uncertainty and its application to Jinping-II Hydroelectric Project. International Journal of Project Management, 30 (8): 950-966.
Yan, H., Shen, Q., Fan, L.C.H., Wang, Y. & Zhang, L. (2010). Greenhouse gas emissions in building construction: A case study of One Peking in Hong Kong. Building and Environment, 45 (4): 949-955.