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.

This paper discusses the problem of allocation of constrained renewable Resource to splittable activities of a single project. If the activities of Stochastic projects can be split, these projects may be completed in shorter time when the available Resource is constrained. It is assumed that the Resource amount required to accomplish each activity is a discrete quantity and deterministic. The activity duration time is assumed to be a discrete random variable with arbitrary experimental distribution. Solving Stochastic mathematical programming model of problem is very hard. So, here some existing methods for deterministic problems have been generalized for Stochastic case. Solutions of generalized methods are relatively better than random solutions. However, the authors developed the new algorithm that may improve the solutions of generalized methods and project Completion Time Distribution Function (CTDF). Comparison of solution of a method with random solutions is a common assessment method in literature research. Hence, the efficiency of the proposed algorithm represented using this method.

Many real projects complete through the realization of one and only one path of various possible network paths.Here, these networks are called alternative Stochastic networks (ASNs). It is supposed that the nodes of considered network are probabilistic with exclusive-or receiver and exclusive-or emitter. First, an analytical approach is proposed to simplify the structure of the network. This approach transforms the network into a simpler equivalent one. This paper discusses the constrained consumable Resource allocation problem in an ASN. Many recent researchers apply heuristic and simulation methods to solve the constrained Resource allocation in these problems. In this paper, we propose an analytical approach based on multi-objective modeling. The objective functions of this model are the cumulative distribution function of the completion time of ASN paths. These functions must be maximized within the desired network completion time. Lexicographic method is used to solve the proposed multi-objective model.The proposed method is illustrated by an example.