A Heuristic for Moment-Matching Scenario Generation
Computational Optimization and Applications
Comment on "Generating Scenario Trees for Multistage Decision Problems"
Management Science
Proceedings of the 35th conference on Winter simulation: driving innovation
Optimal capacity allocation in multi-auction electricity markets under uncertainty
Computers and Operations Research
The Scenario Generation Algorithm for Multistage Stochastic Linear Programming
Mathematics of Operations Research
A Stochastic Programming Approach to Power Portfolio Optimization
Operations Research
Short-term hydropower production planning by stochastic programming
Computers and Operations Research
Recent Advances in Reinforcement Learning
A Study of Demand Stochasticity in Service Network Design
Transportation Science
A Q-learning approach to derive optimal consumption and investment strategies
IEEE Transactions on Neural Networks
Algorithmic Aspects of Scenario-Based Multi-stage Decision Process Optimization
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Generating scenario trees: A parallel integrated simulation-optimization approach
Journal of Computational and Applied Mathematics
A DSS for water resources management under uncertainty by scenario analysis
Environmental Modelling & Software
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Expert Systems with Applications: An International Journal
A review of scenario generation methods
International Journal of Computing Science and Mathematics
An open multi-agent platform for price strategy optimization of generators in market environment
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
A decision support system for strategic asset allocation
Decision Support Systems
Massively parallel asset and liability management
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Evolutionary multi-stage financial scenario tree generation
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
A new moment matching algorithm for sampling from partially specified symmetric distributions
Operations Research Letters
Scenario tree generation approaches using K-means and LP moment matching methods
Journal of Computational and Applied Mathematics
Scenario construction and reduction applied to stochastic power generation expansion planning
Computers and Operations Research
A Stochastic Mixed-Integer Programming approach to the energy-technology management problem
Computers and Industrial Engineering
SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy
INFORMS Journal on Computing
Bayesian-based scenario generation method for human activities
Proceedings of the 2013 ACM SIGSIM conference on Principles of advanced discrete simulation
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
Impact of the shape of demand distribution in decision models for operations management
Computers in Industry
Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
Computers and Operations Research
An advanced system for portfolio optimisation
International Journal of Grid and Utility Computing
A stochastic programming approach to multicriteria portfolio optimization
Journal of Global Optimization
Modeling the Impact of Biometric Security on Millennials' Protection Motivation
Journal of Organizational and End User Computing
Hi-index | 0.01 |
In models of decision making under uncertainty we often are faced with the problem of representing the uncertainties in a form suitable for quantitative models. If the uncertainties are expressed in terms of multivariate continuous distributions, or a discrete distribution with far too many outcomes, we normally face two possibilities: either creating a decision model with internal sampling, or trying to find a simple discrete approximation of the given distribution that serves as input to the model. This paper presents a method based on nonlinear programming that can be used to generate a limited number of discrete outcomes that satisfy specified statistical properties. Users are free to specify any statistical properties they find relevant, and the method can handle inconsistencies in the specifications. The basic idea is to minimize some measure of distance between the statistical properties of the generated outcomes and the specified properties. We illustrate the method by single- and multiple-period problems. The results are encouraging in that a limited number of generated outcomes indeed have statistical properties that are close to or equal to the specifications. We discuss how to verify that the relevant statistical properties are captured in these specifications, and argue that what are the relevant properties, will be problem dependent.