We address in this article the tactical planning and scheduling of chemical process networks consisting of both dedicated and flexible processes under demand and supply uncertainty. To integrate the stochastic inventory control decisions with the production planning and scheduling, we propose a mixed-integer nonlinear programming (MINLP) model that captures the stochastic nature of the demand variations and supply delays by using the guaranteed-service approach (GSA). The model takes into account multiple tradeoffs and simultaneously determines the optimal selection of production schemes, purchase/sales and production levels, cyclic production schedules for flexible processes, as well as working and safety inventory levels of all chemicals involved in the process network. To globally optimize the resulting non-convex MINLP problems with modest computational times, we exploit the model properties, propose a tailored branch-and-refine algorithm and introduce three symmetry breaking cuts. The application is illustrated through a large-scale example with 25 chemicals and 16 processes including at most 8 production schemes for each flexible process.