Decision Programming: A Stochastic Optimization Framework for Multi-Stage Decision Problems

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Γ-opt
Γ-opt
Group of Applied Mathematical Modelling and Optimisation

Group of Applied Mathematical Modeling and Optimization

Publications

Solving decision problems with endogenous uncertainty and conditional information revelation using influence diagrams
Mathematical programming formulations of influence diagrams can bridge the gap between representing and solving decision problems. …
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Solving decision problems with endogenous uncertainty and conditional information revelation using influence diagrams
Risk-averse decision strategies for influence diagrams using rooted junction trees
This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted …
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Risk-averse decision strategies for influence diagrams using rooted junction trees
Risk-averse decision strategies for influence diagrams using rooted junction trees
This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted …
PDF Cite Project DOI
Risk-averse decision strategies for influence diagrams using rooted junction trees
A decomposition strategy for decision problems with endogenous uncertainty using mixed-integer programming
Despite methodological advances for modeling decision problems under uncertainty, faithfully representing endogenous uncertainty still …
Project DOI
Decision programming for mixed-integer multi-stage optimization under uncertainty
Influence diagrams are widely employed to represent multi-stage decision problems in which each decision is a choice from a discrete …
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Decision programming for mixed-integer multi-stage optimization under uncertainty