A numerical method, called OBSM, was recently proposed which employs overcomplete basis functions to achieve sparse representations. While the method can handle non-stationary response without the need of inverting large covariance matrices, it lacks the capability to quantify uncertainty in predictions. We address this issue by proposing a Bayesian approach which first imposes a normal prior o...

Lean manufacturing is a strategic concern for companies which conduct mass production and it has become even more significant for those producing in a project-oriented way by modularization.  In this paper, a bi-objective optimization model is proposed to design and plan a supply chain up to the final assembly centre. The delivery time and the quality in the procurement and low fluctuation of t...

The need of true random number generators for many purposes (ranging from applications in cryptography and stochastic simulation, to search heuristics and game playing) is increasing every day. Many sources of randomness possess the property of stationarity. However, while a biased die may be a good source of entropy, many applications require input in the form of unbiased bits, rather than bia...

This paper proposes a stochastic quasi-gradient (SQG) based algorithm to solve the off-line stochastic dynamic traffic assignment (DTA) problem that explicitly incorporates randomness in O–D demand, as part of a hybrid DTA deployment framework for real-time operations. The problem is formulated as a stochastic programming DTA model with multiple user classes. Due to the complexities introduced ...

The uncertainty of transportation duration between nodes is an inherent characteristic and should be concerned in the routing optimization multimodal network to guarantee reliability delivery time. interval number used deal with duration, multi-objective robust model established which covers cost. To solve combinatorial problem this study, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) de...

We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance constrained approach, thereby providing a ...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

The evaluation of the impact using Machine Learning in management softwarized networks is considered multiple research works. In this paper, we propose to evaluate robustness online learning for optimal network slice placement. A major assumption study consider that request arrivals are non-stationary. We precisely simulate unpredictable load variations and compare two Deep Reinforcement (DRL) ...

We present a method for robust input design for nonlinear state-space models. The method optimizes a scalar cost function of the Fisher information matrix over a set of marginal distributions of stationary processes. By using elements from graph theory we characterize such a set. Since the true system is unknown, the resulting optimization problem takes the uncertainty on the true value of the ...