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.

PROJECT SCHEDULING in the RESOURCE-CONSTRAINED situation is one of the key issues of PROJECT-oriented organizations. The aim of RESOURCE-CONSTRAINED PROJECT SCHEDULING Problem (RCPSP) is to  nd a schedule with minimum makespan by considering precedence and RESOURCE constraints. RCPSP is a combinatorial optimization problem and belongs to the NP-hard class of problems. The exact methods search the entire search space and are unable to solve a large-sized PROJECT network problem. Thus, metaheuristics are used to solve this problem in a short computational time. Due to the probabilistic nature of metaheuristics, it is a challenging problem to make a balance between exploitation and exploration phases. The literature review shows that embedding of chaos improves both the convergence speed and the local optima avoidance of metaheuristics. This paper presents a Chaotic Vibrating Particles System (CVPS) optimization algorithm for solving the RCPSP. Vibrating Particles System (VPS) is a physics-inspired metaheuristic which mimics the free vibration of single-degree-of-freedom systems with viscous damping. The performance and applicability of the CVPS are compared with the standard VPS and  ve well-known algorithms on three benchmark instances of the RCPSPs. Experimental studies reveal that the proposed optimization method is a promising alternative to assist PROJECT managers in dealing with RCPSP.

This paper presents the formulation and solution of the Combinatorial Multi-Mode RESOURCE CONSTRAINED Multi-PROJECT SCHEDULING Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the PROJECT manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the PROJECT manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. RESOURCEs can be non-consumable, consumable, or doubly CONSTRAINED. The paper also presents a real-life case study dealing with SCHEDULING of ship repair activities.