An integration of spreadsheet and project management software for cost optimal time scheduling in construction

Open access


Successful performance and completion of construction projects highly depend on an adequate time scheduling of the project activities. On implementation of time scheduling, the execution modes of activities are most often required to be set in a manner that enables in achieving the minimum total project cost. This paper presents an approach to cost optimal time scheduling, which integrates a spreadsheet application and data transfer to project management software (PMS). At this point, the optimization problem of project time scheduling is modelled employing Microsoft Excel and solved to optimality using Solver while organization of data is dealt by macros. Thereupon, Microsoft Project software is utilized for further managing and presentation of optimized time scheduling solution. In this way, the data flow between programs is automated and possibilities of error occurrence during scheduling process are reduced to a minimum. Moreover, integration of spreadsheet and PMS for cost optimal time scheduling in construction is performed within well-known program environment that increases the possibilities of its wider use in practice. An application example is shown in this paper to demonstrate the advantages of proposed approach.

Adeli, H., & Karim, A. (1997). Scheduling/cost optimization and neural dynamics model for construction. Journal of Construction Engineering and Management, 123(4), pp. 450-458. doi:

Al Haj, R., & El-Sayegh, S. (2015). Time-cost optimization model considering float-consumption Impact. Journal of Construction Engineering and Management, 141(5), 04015001. doi:

Biafore, B. (2013). Microsoft Project 2013: The Missing Manual. O’Reilly Media, Sebastopol, CA.

Crnković, D., & Vukomanović, M. (2016). Comparison of trends in risk management theory and practices within the construction industry. E-GFOS, 7(13), pp. 1-11. doi:

Eshtehardian, E., Afshar, A., & Abbasnia, R. (2009). Fuzzy-based MOGA approach to stochastic time-cost trade-off problem. Automation in Construction, 18(5), pp. 692-701. doi:

Ezeldin, A. S., & Soliman, A. (2009). Hybrid time-cost optimization of nonserial repetitive construction projects. Journal of Construction Engineering and Management, 135(1), pp. 42-55. doi:

Frontline Systems. (2017). Frontline Solvers Optimization and Simulation User Guide. Available at on 07 November, 2017).

Galić, M., Završki, I., & Dolaček-Alduk, Z. (2016a). Scenario simulation model for optimized allocation of construction machinery. Građevinar, 68(2), pp. 105-112. doi:

Galić, M., Završki, I., & Dolaček-Alduk, Z. (2016b). Methodology and algorithm for asphalt supply chain optimization. Tehnicki Vjesnik-Technical Gazette, 23(4), pp. 1193-1200. doi:

Galić, M., Barišić, I., & Ištoka Otković, I. (2017). Route reliability based simulation model for HMA delivery in urban areas. Procedia Engineering, 187, pp. 378-386. doi:

Geem, Z. W. (2010). Multiobjective optimization of time-cost trade-off using harmony search. Journal of Construction Engineering and Management, 136(6), pp. 711-716. doi:

Harris, P. E. (2016). Planning and Control Using Microsoft Project 2013 and 2016. Eastwood Harris Pty Ltd, Doncaster Heights, VIC.

Hazir, Ö., Haouari, M., & Erel, E. (2010). Robust scheduling and robustness measures for the discrete time/cost trade-off problem. European Journal of Operational Research, 207(2), pp. 633-643. doi:

Hazir, Ö., Erel, E., & Günalay, Y. (2011). Robust optimization models for the discrete time/cost trade-off problem. International Journal of Production Economics, 130(1), pp. 87-95. doi:

He, Z., Wang, N., Jia, T., & Xu, Y. (2009). Simulated annealing and tabu search for multi-mode project payment scheduling. European Journal of Operational Research, 198(3), pp. 688-696. doi:

Hillier, F. S., & Lieberman, G. J. (2014). Introduction to Operations Research, 10th edn. McGraw-Hill Higher Education, New York, NY.

Kalhor, E., Khanzadi, M., Eshtehardian, E., & Afshar, A. (2011). Stochastic time-cost optimization using non-dominated archiving ant colony approach. Automation in Construction, 20(8), pp. 1193-1203. doi:

Kažović, D., & Valenčić, D. (2013, May). Using Microsoft Project for project management in non-governmental organisations. In: Information & Communication Technology Electronics & Microelectronics (MIPRO), 2013 36th International Convention on 20-24 May 2013, Opatija, Croatia. IEEE, pp. 681-684.

Klanšek, U. (2016). Mixed-Integer nonlinear programming model for nonlinear discrete optimization of project schedules under restricted costs. Journal of Construction Engineering and Management, 142(3), 04015088. doi:

Kostalova, J., & Tetrevova, L. (2014). Project management and its tools in practice in the Czech Republic. Procedia-Social and Behavioral Sciences, 150, pp. 678-689. doi:

Marmel, E. (2013), Project 2010 Bible. John Wiley & Sons, Indianapolis, IN.

Mokhtari, H., Aghaie, A., Rahimi, J., & Mozdgir, A. (2010). Project time-cost trade-off scheduling: A hybrid optimization approach. International Journal of Advanced Manufacturing Technology, 50, 5-8(2010), pp. 811-822. doi:

Nearchou, A. C. (2010). Scheduling with controllable processing times and compression costs using population-based heuristics. International Journal of Production Research, 48(23), pp. 7043-7062. doi:

Petlíková, K., & Jarský, Č. (2017). Modeling of the time structure of construction processes using neural networks. Organization, Technology and Management in Construction: An International Journal, 9(1), pp. 1559-1564. doi:

Sakellaropoulos, S., & Chassiakos, A. P. (2004). Project time-cost analysis under generalised precedence relations. Advances in Engineering Software, 35(10-11), pp. 715-724. doi:

Silva Filho, O. S., Cezarino, W., & Ratto, J. (2010). Aggregate production planning: Modeling and solution via Excel spreadsheet and solver. IFAC Proceedings Volumes, 43(17), pp. 89-94. doi:

Sonmez, R., & Bettemir, Ö. H. (2012). A hybrid genetic algorithm for the discrete time-cost trade-off problem. Expert Systems with Applications. 39(13), pp. 11428-11434. doi:

Trautmann, N., & Gnägi, M. (2015, December). On an application of Microsoft Excel’s evolutionary solver to the resource-constrained project scheduling problem RCPSP. In: Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference on 6-9 Dec. 2015, Singapore. IEEE, pp. 646-650. doi:

Valenko, T., & Klanšek, U. (2017, September). Cost optimal time scheduling integrating spreadsheet and project management software. In: 13th International Conference Organization, Technology and Management in Construction, 2017. Croatian Association for Construction Management: University of Zagreb, Faculty of Civil Engineering, pp. 42-53.

Vanhoucke, M. (2005). New computational results for the discrete time/cost trade-off problem with time-switch constraints. European Journal of Operational Research, 165(2), pp. 359-374. doi:

Von Laszewski, G., & Dilmanian, L. E. (2008, November). e-Science project and experiment management with Microsoft Project. In: Grid Computing Environments Workshop, 2008. GCE’08. IEEE, pp. 1-8. doi:

Yang, I.-T. (2007). Using elitist particle swarm optimization to facilitate bicriterion time-cost trade-off analysis. Journal of Construction Engineering and Management, 133(7), pp. 498-505. doi:

Organization, Technology and Management in Construction: an International Journal

Co-published with University of Zagreb, Faculty of Civil Engineering

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 319 319 29
PDF Downloads 219 219 29