Decision making under risk and uncertainty - concepts, methods and applications
Project goals:
Proposing new models of uncertainty, which take some information provided with uncertainty sets into account. In particular, possibility theory and theory of imprecise probabilities will be used for this purpose. Investigating new criteria for choosing a solution, which take into account decision-makers risk aversion.
Application of various models of uncertainty to single and two-stage versions of particular problems, for example, scheduling, supply chain, production planning, and combinatorial problems. Constructing mathematical programming reformulations for these problems, implementing them and testing, by using available software. Constructing approximate solutions for hard large-scale problems.
Research team:
Adam Kasperski
Marcel Jackiewicz (PhD student)
Szymon Wróbel (PhD student)
Publications:
M. Jackiewicz, A. Kasperski, P. Zieliński. Wasserstein robust combinatorial optimization problems. arXiv:2312.12769v
M. Jackiewicz, A. Kasperski, P. Zieliński. Recoverable robust shortest path problem under interval uncertainty representations. arXiv:2401.05715v1
W. Baak, M. Goerigk, A. Kasperski, P. Zieliński. Robust min-max (regret) optimization using ordered weighted averaging. arXiv:2308.08522v2
R.Guillaume, A. Kasperski, P. Zieliński. A framework of distributionally robust possibilitic optimization. Fuzzy Optimization and Decision Making, to appear
R. Guillaume, M. Goerigk, A. Kasperski, P. Zieliński. Robust optimization with belief functions. International Journal of Approximate Reasonning 159, 108941, 2023
M. Jackiewicz, A. Kasperski, P. Zieliński. Solving Wasserstein distributionally robust combinatorial optimization problems (acceptet in proceedings of OR 2023, Hamburg)
M. Jackiewicz, A. Kasperski, P. Zieliński. Solving the recoverable robust shortest path problem in DAGs (accepted in OR 2023, Hamburg)