Аббасов Зафар Думан

DISTRIBUTIONALLY ROBUST HAMILTONIAN CYCLE OPTIMIZATION ON STOCHASTIC GRAPHS VIA WASSERSTEIN-METRIC GENETIC ALGORITHMS: EMPIRICAL EVIDENCE FROM ALMATY AND ASTANA

This paper introduces a data-driven Distributionally Robust Optimization (DRO) framework to identify risk-averse Hamiltonian cycles in non-stationary stochastic networks subject to severe probabilistic uncertainty. Rather than relying on static nominal travel times, which suffer heavily from the optimizer's curse, we c...