Many decision problems in Operations Research are defined on temporal
networks, that is, workflows of time-consuming tasks whose processing
order is constrained by precedence relations. For example, temporal
networks are used to model projects, computer applications, digital
circuits and production processes. Optimization problems arise in
temporal networks when a decision maker wishes to determine a temporal
arrangement of the tasks and/or a resource assignment that optimizes
some network characteristic (e.g. the time required to complete all
tasks). The parameters of these optimization problems (e.g. the task
durations) are typically unknown at the time the decision problem
arises. This monograph investigates solution techniques for optimization
problems in temporal networks that explicitly account for this parameter
uncertainty. We study several formulations, each of which requires
different information about the uncertain problem parameters.