optimization under uncertainty: generality and application to multimodal transport

In a realistic environment, operational decision problems involve several sources of uncertainty, due to measurement errors, approximate parameters, or simply the unavailability of information at the time of decision-making. these disturbances are important in the optimization process and must be taken into consideration. to meet these needs, optimization under uncertainty has emerged as an important area of modern operations research and has gained increasing popularity in recent years by tackling complex optimization problems, such as multimodal chain management and container terminals management. in this regard, the present article provides a general overview of the different optimization paradigms and approaches used in the literature to support decision-making in the face of uncertainty. in particular, this article aims to present a state of the art on application of optimization under uncertainty in multimodal transport problems, with a particular focus on the application of robust optimization.