Add to ORKGA common approach in coping with multiperiod optimization problems under uncertainty where statistical information is not really strong enough to support a stochastic programming model, has been to set up and analyze a number of scenarios. The aim then is to identify trends and essential features on which a robust decision policy can be based. This paper develops for the first time a rigorous algorithmic procedure for determining such a policy in response to any weighting of the scenarios. The scenarios are bundled at various levels to reflect the availability of information, and iterative adjustments are made to the decision policy to adapt to this structure and remove the dependence on hindsight
Scenario optimization is a broad scheme for data-driven decision-making in which experimental observ...
For many real-world problems, optimization could only be formulated with partial information or subj...
This paper considers model uncertainty for multistage stochastic programs. The data and information ...
Dynamic decision-making under uncertainty has a long and distinguished history in operations researc...
In the settings of decision-making-under-uncertainty problems, an agent takes an action on the envir...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
In this thesis several approaches for optimization and decision-making under uncertainty with a stro...
International audienceOptimization under uncertainty is a key problem in order to solve complex syst...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
Markov decision processes model stochastic uncertainty in systems and allow one to construct strateg...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
• Optimization models for real-world applications are expected to generate “robust ” decisions in th...
peer reviewedIn this chapter, we present the multistage stochastic programming framework for sequent...
Dynamic decision problems affected by uncertain data are notoriously hard to solve due to the prese...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
Scenario optimization is a broad scheme for data-driven decision-making in which experimental observ...
For many real-world problems, optimization could only be formulated with partial information or subj...
This paper considers model uncertainty for multistage stochastic programs. The data and information ...
Dynamic decision-making under uncertainty has a long and distinguished history in operations researc...
In the settings of decision-making-under-uncertainty problems, an agent takes an action on the envir...
A wide variety of decision problems in engineering, science and economics involve uncertain paramete...
In this thesis several approaches for optimization and decision-making under uncertainty with a stro...
International audienceOptimization under uncertainty is a key problem in order to solve complex syst...
Recent advances in decision making have incorporated both risk and ambiguity in decision theory and ...
Markov decision processes model stochastic uncertainty in systems and allow one to construct strateg...
Abstract. A central issue arising in financial, engineering and, more generally, in many applicative...
• Optimization models for real-world applications are expected to generate “robust ” decisions in th...
peer reviewedIn this chapter, we present the multistage stochastic programming framework for sequent...
Dynamic decision problems affected by uncertain data are notoriously hard to solve due to the prese...
Uncertainty is a facet of many decision environments and might arise for various reasons, such as un...
Scenario optimization is a broad scheme for data-driven decision-making in which experimental observ...
For many real-world problems, optimization could only be formulated with partial information or subj...
This paper considers model uncertainty for multistage stochastic programs. The data and information ...