Document Type : Original Article


1 MSc Student, Iran University of Science and Technology.

2 Associate Professor, Iran University of Science and Technology.

3 Assistant Professor, Iran University of Science and Technology.

4 Ph.D Student, Iran University of Science and Technology.


The construction supply chain encounters with a lot of challenges that one of the most significant can refer to the higher sources wasting in project site and also the high pollution on amount of this type of supply chains. On the other hand, the most managers of supply chains need to the integration of supply chain such as project sources and time specifics, determining the production and inventory level, and also determining numbers and type of vehicles that are included, in order to its cost is calculated in an optimal form. For this purpose, in this article an integrated model has been proposed which first objective function is maximizing the profit, and the second objective amount is minimizing the emission of the carbon dioxide gas, whereas a solution way for the prevention of sources waste in project site is presented.  With assumption of determining project network and also the durations and daily demand of each activity, this supply chain model pans the different periods of time. To consider this bi-objective and nonlinear model, the model has been first linearized, and by using the epsilon-constraint method and also coding in GSMS software has been solved and its results have been finally analyzed with two numerical examples. 


1. Chen, Y. (2012). Study on the Application of Lean Construction Supply Chain Management in EPC Project. In Applied Mechanics and Materials (Vol. 201, pp. 1207-1212). Trans Tech Publications.
2. Dekker, R., Fleischmann, M., Inderfurth, K., & van Wassenhove, L. N. (Eds.). (2013). Reverse logistics: quantitative models for closed-loop supply chains. Springer Science & Business Media.
3. Dowlatshahi, S. (2000). Developing a theory of reverse logisticsInterfaces30(3), 143-155.
4. Dulaimi, M. Khalfan, M. M., & McDermott, P. (2006). Innovating for supply chain integration within construction. Construction Innovation6(3), 143-157.
5. Elhedhli, S., & Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment17(5), 370-379.
6. Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management10(2), 156-173.
7. Gangolells, M., Casals, M., Gasso, S., Forcada, N., Roca, X., & Fuertes, A. (2009). A methodology for predicting the severity of environmental impacts related to the construction process of residential buildings. Building and Environment44(3), 558-571.
8. Green, S. D., Fernie, S., & Weller, S. (2005). Making sense of supply chain management: a comparative study of aerospace and construction. Construction Management and Economics23(6), 579-593.
9. Gupte, A., Ahmed, S., Cheon, M. S., & Dey, S. (2013). Solving mixed integer bilinear problems using MILP formulations. SIAM Journal on Optimization23(2), 721-744.
10. Koné, O., Artigues, C., Lopez, P., & Mongeau, M. (2011). Event-based MILP models for resource-constrained project scheduling problems. Computers & Operations Research38(1), 3-13.
11. Krikke, H. R., Van Harten, A., & Schuur, P. C. (1999). Business case Roteb: recovery strategies for monitors. Computers & Industrial Engineering36(4), 739-757.
12. Kumar, V., & Viswanadham, N. (2007, September). A CBR-based decision support system framework for construction supply chain risk management. In Automation Science and Engineering, 2007. CASE 2007. IEEE International Conference on (pp. 980-985). IEEE.
13. Jaskowski, P., Sobotka, A., & Czarnigowska, A. (2014). Decision model for selecting supply sources of road construction aggregates. Engineering Economics25(1), 13-20.
14. Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation213(2), 455-465.
15. Meng, X. (2010). Assessment framework for construction supply chain relationships: Development and evaluation. International Journal of Project Management28(7), 695-707.
16. Morris, P., & Pinto, J. K. (Eds.). (2010). The Wiley guide to project technology, supply chain, and procurement management (Vol. 7). John Wiley & Sons.
17. Mosleh Shirazi, A. Khalifeh, M. (2015). Measuring Efficiency of Iran Global Competitiveness Index Compared with Selected Countries using Two-Stage Data Envelopment Analysis Model. Journal of Industrial Management Perspective, 5(19), 95-110
18. Naber, A., & Kolisch, R. (2014). MIP models for resource-constrained project scheduling with flexible resource profiles. European Journal of Operational Research239(2), 335-348.
19. O’brien, W. J. (1999, August). Construction Supply-Chain Management: a vision for advanced coordination, costing, and control. In NSF Berkeley-Stanford Construction Research Workshop (Vol. 6). California: Stanford Univ.
20. Rabieh, M. Fadaei, A. (2015).Fuzzy Robust Mathematical Model for Project Portfolio Selection and its Solving through Multi Objective Differential Evolutionary Algorithm, Journal of Industrial Management Perspective, 5(19), 65-90
21. Rogers, D. S., & Tibben‐Lembke, R. (2001). An examination of reverse logistics practices. Journal of business logistics22(2), 129-148.
22. Rubio, S., Chamorro, A., & Miranda, F. J. (2008). Characteristics of the research on reverse logistics (1995–2005). International journal of production research46(4), 1099-1120.
23. Scholl, A. Amiri, B., Olfat, L., Khalili Damghani, K., (2012). Multi-period and multi-product supply chain network design using a combination of mathematical programming, multi-objective approach and DEA. Journal of Industrial Management Perspective, 4(14), 26-51
24. Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International journal of management reviews9(1), 53-80.
25. Testa, F., & Iraldo, F. (2010). Shadows and lights of GSCM (Green Supply Chain Management): determinants and effects of these practices based on a multi-national study. Journal of Cleaner Production18(10), 953-962.
26. Tserng, H. P., Yin, S. Y., & Li, S. (2006). Developing a resource supply chain planning system for construction projects. Journal of Construction Engineering and Management132(4), 393-407.
27. Vrijhoef, R., & Koskela, L. (2000). The four roles of supply chain management in construction. European journal of purchasing & supply management6(3), 169-178.
28. Xue, X., Wang, Y., Shen, Q., & Yu, X. (2007). Coordination mechanisms for construction supply chain management in the Internet environment. International Journal of project management25(2), 150-157.
29. Zhou, P., Chen, D., & Wang, Q. (2013). Network design and operational modelling for construction green supply chain management. International Journal of Industrial Engineering Computations4(1), 13-28.