Evolutionary Optimization for Decision Making under Uncertainty

01/19/2014
by   Ronald Hochreiter, et al.
0

Optimizing decision problems under uncertainty can be done using a variety of solution methods. Soft computing and heuristic approaches tend to be powerful for solving such problems. In this overview article, we survey Evolutionary Optimization techniques to solve Stochastic Programming problems - both for the single-stage and multi-stage case.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/23/2014

Modeling multi-stage decision optimization problems

Multi-stage optimization under uncertainty techniques can be used to sol...
research
06/17/2023

A Survey of Contextual Optimization Methods for Decision Making under Uncertainty

Recently there has been a surge of interest in operations research (OR) ...
research
12/08/2009

Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on...
research
09/23/2019

Compiling Stochastic Constraint Programs to And-Or Decision Diagrams

Factored stochastic constraint programming (FSCP) is a formalism to repr...
research
08/01/2017

Exact Approaches for the Travelling Thief Problem

Many evolutionary and constructive heuristic approaches have been introd...
research
10/13/2019

Global-Local Metamodel Assisted Two-Stage Optimization via Simulation

To integrate strategic, tactical and operational decisions, the two-stag...
research
09/06/2023

On the Impact of Feeding Cost Risk in Aquaculture Valuation and Decision Making

We study the effect of stochastic feeding costs on animal-based commodit...

Please sign up or login with your details

Forgot password? Click here to reset