While methods for optimization under uncertainty have been studied intensely over the past
decades, the explicit consideration of the interplay between uncertainty and time has gained …

Passengers traveling by train may need to change trains on their route. If the focal train of a
passenger is late, the passenger might miss his connection and has to decide how to …

Robust optimization considers optimization problems with uncertainty in the data. The
common data model assumes that the uncertainty can be represented by an uncertainty set …

Liner ship** is vulnerable to many disruptive factors such as port congestion or harsh
weather, which could result to delay in arriving at the ports. It could result in both financial …

The goal of robust optimization is to hedge against uncertainties: in most real-world
applications, the specific problem instance depends on uncertain data and is hence not …

This work deals with a facility location problem in which location and allocation
(transportation) policy is defined in two stages such that a first-stage solution should be …

Context The quality of business process models (ie, software artifacts that capture the
relations between the organizational units of a business) is essential for enhancing the …

Transport companies often have a published timetable. To maintain timetable reliability
despite delays, companies include buffer times during timetable development and adjust the …

In this paper, we consider the Robust Periodic Timetabling Problem (RPTP), the problem of
designing a periodic timetable that can easily be adjusted in case of small periodic …

Should connecting trains wait for delayed feeder trains? Or is it better for the passengers if
trains depart on time? Questions of this type are the subject of delay management, which will …