Stochastic scheduling plays a fundamental role in understanding how uncertainty propagates in machine logistics. However, existing research lacks explicit treatment of exact gradients for the project duration with respect to specific task parameters. This thesis develops a framework to calculate the sensitivity of the project duration distribution in stochastic directed acyclic graphs (DAGs).

Initially, closed-form analytical expressions for the gradients of the expected project duration are derived under Gaussian assumptions. The research then extends this foundation through a Generalized Sensitivity Theorem, accommodating a broader class of probability distributions and enabling the use of shared parameters across multiple nodes. Furthermore, the framework is applied to analyze internal machine logistics and component waiting times. By introducing parameterized artificial delays, the study formulates an optimization approach to approximate Just In Time (JIT) behavior within stochastic environments. ... Read more

The influence of the reactive DC sputtering parameters on the superconducting properties of NbReN ultrathin films was investigated. A detailed study of the current-voltage characteristics of the plasma was performed to optimize the superconducting critical temperature, T_c. The thickness dependence of T c for the films deposited under different conditions was analyzed down to the ultrathin limit. Optimized films were used to fabricate superconducting nanowire single photon detectors which, at T = 3.5 K, show saturated internal detection efficiency (IDE) up to a wavelength of 1301 nm and 95% IDE at 1548 nm with recovery times and timing jitter of about 8 ns and 28 ps, respectively. ... Read more