Immediately after a disruption, in order to minimize the negative impact inflicted on society, its imperative re-establish interdicted critical services enabled by infrastructure networks. In this paper, we study stochastic network recovery problem that tackles planning of restoration activities (considering limited resources) links so pre-disruption service flows can be re-established as quick...

This is a PhD thesis of Blagoj Delipetrev explaining nested dynamic programming, nested stochastic dynamic programming and nested reinforcement learning algorithms that are applied in reservoir optimization problem. Additionally there are also multi-objective version of these algorithms.

mathematical modeling of supply chain operations has proven to be one of the most complex tasks in the field of operations management and operations research. despite the abundance of several modeling proposals in the literature; for vast majority of them, no effective universal application is conceived. this issue renders the proposed mathematical models inapplicable due largely to the fact th...

Discrete-time stochastic systems are an essential modeling tool for many engineering systems. We consider control that evolving over continuous spaces. For this class of models, methods the formal verification and synthesis strategies computationally hard generally rely on use approximate abstractions. Building abstractions, we compute with lower- upper-bounds satisfying unbounded temporal logi...