2 citations · 12 across the 27 of their papers we have counts for
33 papers
Recoverable robust representatives selection problem under interval continuous budgeted uncertainty
Marcel Jackiewicz, Adam Kasperski, Pawel Zielinski
In this paper, the recoverable robust representative selection problem is considered, where uncertain second-stage costs are modeled using interval uncertainty with a continuous bu…
Single-item lot sizing problem under budgeted lead-time uncertainty
Romain Guillaume, Adam Kasperski, Szymon Wrobel +1
In this paper, a single-item lot sizing problem with backordering is discussed. The time horizon is divided into planning periods, characterized by fixed and variable production co…
An extension of Ordered Weighted Averaging over intervals with application to optimization under risk
Werner Baak, Marc Goerigk, Adam Kasperski +1
The Ordered Weighted Averaging (OWA) operator is a traditional and commonly used criterion for aggregating discrete values of uncertain quantities. In this paper, it is shown that…
Computational complexity of the recoverable robust shortest path problem in acyclic digraphs under interval budgeted uncertainty
Adam Kasperski, Marcel Jackiewicz, Pawel Zielinski
In this paper, we consider the recoverable robust shortest path problem in acyclic digraphs, employing interval budgeted uncertainty to model uncertain second-stage costs. For the…
Computational Complexity of the Recoverable Robust Shortest Path Problem with Discrete Recourse
Marcel Jackiewicz, Adam Kasperski, Paweł Zieliński
In this paper the recoverable robust shortest path problem is investigated. Discrete budgeted interval uncertainty representation is used to model uncertain second-stage arc costs.…
Recoverable robust shortest path problem under interval budgeted uncertainty representations
Marcel Jackiewicz, Adam Kasperski, Pawel Zielinski
In this paper, the recoverable robust shortest path problem under interval uncertainty representations is discussed. This problem is known to be strongly NP-hard and also hard to a…