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This thesis focuses on the development of stochastic models for project and production planning. Based on these quantitative models we provide managerial insights that support the decision making process within an organization. The thesis is divided into two parts. The first part focuses on project planning with uncertain durations of individual tasks. We model the projects via activity on nodes networks. The minimization problem balances holding cost and penalty cost, and optimality equations are derived for different cost structures. In the optimal solution safety time is assigned where it is most effective. In the second part, we develop models that support go/no go decision making in projects. We model a project as a Markov Decision Process. We take into account stochastic demand and capacity constraints.

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