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...

In this paper we develop and test scenario generation methods for asset liability management models. We propose a multi-stage stochastic programming model for a Dutch pension fund. Both randomly sampled event trees and event trees tting the mean and the covariance of the return distribution are used for generating the coeecients of the stochastic program. In order to investigate the performance...

This work presents an empirical analysis of popular scenario generation methods for stochastic optimization, including quasi-Monte Carlo, moment matching, and methods based on probability metrics, as well as a new method referred to as Voronoi cell sampling. Solution quality is assessed by measuring the error that arises from using scenarios to solve a multi-dimensional newsvendor problem, for ...

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.

Efficient generation of scenarios is a central problem in evaluating the expected value of a random function in the stochastic optimization. We study the use of sparse grid scenario generation method for this purpose. We show that this method is uniformly convergent, hence, also epi-convergent. We numerically compare the performance of the sparse grid method with several Quasi Monte Carlo (QMC)...

Scenario generation is an important step in the operation and planning of power systems with high renewable penetrations. In this work, we proposed a data-driven approach for scenario generation using generative adversarial networks, which is based on two interconnected deep neural networks. Compared with existing methods based on probabilistic models that are often hard to scale or sample from...

This paper presents a method for test scenario generation based on formal specifications and usage profiles. It is a major component of a framework for testing object-oriented programs. In this framework, the requirements of a software system are formally specified. The anticipated application of the system is expressed in a usage profile, which is a state model that indicates the dynamic behav...

The Geometric Brownian motion (GBM) is a standard method for modeling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, GBM has been considered unable to properly capture important features, like extreme behaviour or volatility clustering. We propose an approach by which, the parameters of the GBM follow...

Scenarios, in most situations, are descriptions of required interactions between a desired system and its environment which detail normative system behaviour. There is considerable current interest in the use of scenarios for acquisition, elaboration and validation of system requirements. However, despite this interest, there remains a lack of methods and software tools to generate and use scen...

We provide a new approach to scenario generation for the purpose of risk management in the banking industry. We connect ideas from standard techniques – like historical and Monte Carlo simulation – to a hybrid technique that shares the advantages of standard procedures but reduces several of their drawbacks. Instead of considering the static problem of constructing one or ten day ahead distribu...