Document Type : Research Paper


1 Ministry of Development, Turkey.

2 Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara, Turkey.

3 Department of Industrial Engineering, Faculty of Engineering, Gaziantep University, Gaziantep, Turkey.


Despite the dynamic nature of real life scheduling problems, few studies focus on stochastic resource constrained project scheduling problem and its variants. In this study, we consider stochastic resource possibilities and propose a new chance constraint, piecewise-linear and mixed integer programming model. Model is tested and verified with known project instances. One of the main strengths of the proposed model is it can be used to construct baseline schedules with a predetermined confidence interval. This gives scheduler an opportunity to construct proactive actions in order to minimize disruptions.


Main Subjects

[1]     Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics5(1), 11-24.
[2]     Ballestin, F., & Leus, R. (2009). Resource‐Constrained Project Scheduling for Timely Project Completion with Stochastic Activity Durations. Production and operations management18(4), 459-474.
[3]     Tavakkoli-Moghaddam, R., Jolai, F., Vaziri, F., Ahmed, P. K., & Azaron, A. (2005). A hybrid method for solving stochastic job shop scheduling problems. Applied mathematics and computation170(1), 185-206.
[4]     Wang, Y., He, Z., Kerkhove, L. P., & Vanhoucke, M. (2017). On the performance of priority rules for the stochastic resource constrained multi-project scheduling problem. Computers & industrial engineering114, 223-234.
[5]     Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2008). Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities. Journal of scheduling11(2), 121-136.
[6]     Valls, V., Laguna, M., Lino, P., Pérez, A., & Quintanilla, S. (1999). Project scheduling with stochastic activity interruptions. Project scheduling (pp. 333-353). Springer, Boston, MA.
[7]     Wang, L., Huang, H., & Ke, H. (2015). Chance-constrained model for RCPSP with uncertain durations. Journal of uncertainty analysis and applications3(1), 12.
[8]     Uysal, F., Isleyen, S., Cetinkaya, C., & Celik, N. (2015). Resource Constrained Project Scheduling Problem: An Analytical Approach. V international conference industrial engineering and environmental protection, 357-362.
[9]     Hartmann, S., & Briskorn, D. (2008). A survey of deterministic modeling approaches for project scheduling under resource constraints. European journal of operational research207, 1-14.
[10] Kolisch, R., & Sprecher, A. (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European journal of operational research96(1), 205-216.
[11] Dadfar, H., & Gustavsson, P. (1992). Competition by effective management of cultural diversity: the case of international construction projects. International studies of management & organization22(4), 81-92.
[12] Williams, T. M. (1992). Practical use of distributions in network analysis. Journal of the operational research society43(3), 265-270.
[13] Golenko-Ginzburg, D., & Gonik, A. (1997). Stochastic network project scheduling with non-consumable limited resources. International journal of production economics48(1), 29-37.
[14] Shou, Y., & Wang, W. (2012). Robust optimization-based genetic algorithm for project scheduling with stochastic activity durations. International information institute (Tokyo)15(10), 4049- 4064.
[15] Yang, I. T., & Chang, C. Y. (2005). Stochastic resource-constrained scheduling for repetitive construction projects with uncertain supply of resources and funding. International journal of project management23(7), 546-553.
[16]  Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European journal of operational research165(2), 289-306.
[17] Kis, T. (2005). A branch-and-cut algorithm for scheduling of projects with variable-intensity activities. Mathematical programming103(3), 515-539.
[18] Long, L. D., & Ohsato, A. (2008). Fuzzy critical chain method for project scheduling under resource constraints and uncertainty. International journal of project management26(6), 688-698.
[19] Schonberger, R. J. (1981). Why projects are “always” late: a rationale based on manual simulation of a PERT/CPM network. Interfaces11(5), 66-70.
[20] Golenko-Ginzburg, D., Gonik, A., & Laslo, Z. (2003). Resource constrained scheduling simulation model for alternative stochastic network projects. Mathematics and computers in simulation63(2), 105-117.
[21] Li, S., Jia, Y., & Wang, J. (2012). A discrete-event simulation approach with multiple-comparison procedure for stochastic resource-constrained project scheduling. The international journal of advanced manufacturing technology63(1-4), 65-76.
[22] Erdogan, S. A., & Denton, B. (2013). Dynamic appointment scheduling of a stochastic server with uncertain demand. INFORMS journal on computing25(1), 116-132.
[23] Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science6(1), 73-79.
[24] Charnes, A., & Cooper, W. W. (1962). Chance constraints and normal deviates. Journal of the American statistical association57(297), 134-148.
[25] Charnes, A., & Cooper, W. W. (1963). Deterministic equivalents for optimizing and satisficing under chance constraints. Operations research11(1), 18-39.
[26] Williams, H. P. (2013). Model building in mathematical programming. John Wiley & Sons.