Optimization and analytics for stochastic and robust project scheduling

Project scheduling and other large-scale planning problems often involve high degrees of uncertainty. Important attributes of individual activities such as processing times, resource requirements and costs can often only be estimated stochastically and their actual values at the time of execution may deviate significantly from these estimates. The aim of this research project is to advance the methodology for anticipating and counteracting such uncertainties. In particular, we plan to develop efficient computational methods for aiding decision makers by solving the following tasks: (i) evaluating the performance of a policy for a given project by using a mix of simulation and optimization algorithms; (ii) identifying weaknesses of a proposed schedule and critical activities of a project whose delay has crucial influence on the overall success; (iii) analyzing the dependence of the final cost and completion time on external parameters.