Mean-conditional value at risk model for the stochastic project scheduling problem

Every project faces different opportunities and risks during its lifecycle. Risks are the factors that can disrupt the successful implementation of projects and cause failure in achieving project goals. Advancing the project while considering its risks is one of the most essential aspects of project management. Planning and scheduling can be applied in a way that reduces the risks in the management of projects. In this paper, a new scenario-based mean-conditional value-at-risk (CVaR) model is developed to minimize the risk of the project's net present value (NPV). Moreover, the trade-off between expected NPV and the risk of NPV is considered in this study. Start time policies are also used to specify the start times of project activities. Two multi-objective optimization algorithms including Non-dominated Sorting Genetic Algorithms (NSGA-II), and Multi-Objective Vibration Damping Optimization (MOVDO) are applied to identify the Pareto optimal solution. The efficiency of the algorithms is assessed based on some performance criteria. The results of the computational experiments show that at identical run time MOVDO functions better in terms of hypervolume indicator, while NSGA- II better results in other performance metrics.