A multistage stochastic linear program (MSLP) is a model of sequential stochastic optimization where the objective and constraints are linear. When any of the random variables used in the MSLP are continuous, the problem is infinite dimensional. In order to numerically tackle such a problem we usually replace it with a finite dimensional approximation. Even when all the random variables have fi...

Data for optimization problems often comes from (deterministic) forecasts, but it is näıve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate the distribution of forecast errors and use that to construct the scenarios. Although sampling from the ...

Abstract : This paper describes a novel technique for scenario generation aimed at closed loop stochastic nonlinear model predictive control. The key ingredient in the algorithm is the use of vector quantization methods. We also show how one can impose a tree structure on the resulting scenarios. Finally, we briefly describe how the scenarios can be used in large scale stochastic nonlinear mode...

We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network (Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks. By using a Bayesian formulation, generators can be constructed and trained to produce scenarios that capture different salient modes in ...

start taking a few most applied scenarios from a traffic control centre, analysing each component and structure of the whole, and evaluating the impact of each component and some typical combinations, based on available monitoring systems. Carrying on such initial research on best practices, we build a dynamic simulation model, including these typical scenarios and evaluate the impact on traffi...

Serious games allow for adaptive and personalised forms of training; the nature and timing of learning activities can be tailored to the trainee’s needs and interests. Autonomous game-based training requires for the automatic selection of appropriate exercises for an individual trainee. This paper presents a framework for an automated scenario generation system. The underlying notion is that a ...

This study investigates the usefulness of a scenario advisor tool which was designed to be used in the military domain where human or machine errors cause safety-critical problems. The tool provides traceability between scenario models and requirements and helps to generate new scenarios and scenario variations. Through two series of evaluation sessions, we found that the tool is useful to gene...

We broaden the theoretical basis for generating scenario trees in multi-stage stochastic programming based on stability analysis. Numerical experience for constructing trees of demand and price scenarios in electricity portfolio management of a municipal power utility is also provided.