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.

A common problem arising in Project management is the fact that the baseline schedule is often disrupted during the Project execution because of uncertain parameters. As a result, Project managers are often unable to meet the deadline time of the milestones. Robust Project Scheduling is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during Project execution. In order to apply robust Scheduling principles to real Projects, one should make assumptions close to the actual conditions of the Project as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust Scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust Scheduling based on the results of these experiments.

In this paper a new method based on fuzzy theory is developed to solve the Project Scheduling problem under fuzzy environment. Assuming that the duration of activities are trapezoidal fuzzy numbers (TFN), in this method we compute several Project characteristics such as earliest times, latest times, and, slack times in term of TFN. In this method, we introduce a new approach which we call modified backward pass (MBP). This approach, based on a linear programming (LP) problem, removes negative and infeasible solutions which can be generated by other methods in the backward pass calculation. We drive the general form of the optimal solution of the LP problem which enables practitioners to obtain the optimal solution by a simple recursive relation without solving any LP problem. Through a numerical example, calculation steps in this method and the results are illustrated.