Modelling decision-making and uncertainty

ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
Two-stage stochastic programming (2SP) offers a basic framework for modelling decision-making under uncertainty, yet scalability …
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ICNN-enhanced 2SP: Leveraging input convex neural networks for solving two-stage stochastic programming
Input convex neural networks as surrogates in mathematical optimisation
Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research. The …
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Input convex neural networks as surrogates in mathematical optimisation
A Robust Optimization Approach to Flow Decomposition
In this paper, we generalize the minimum flow decomposition problem (MFD) to incorporate uncertain edge capacities and tackle it from …
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A Robust Optimization Approach to Flow Decomposition
An efficient mixed-integer linear programming formulation for solving influence diagrams
Influence diagrams represent decision-making problems with interdependencies between random events, decisions, and consequences. …
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An efficient mixed-integer linear programming formulation for solving influence diagrams
Solving Influence Diagrams: Efficient Mixed-Integer Programming Formulation and Heuristic
In this paper, we propose novel mixed-integer linear programming (MIP) formulations to model decision problems posed as influence …
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Solving Influence Diagrams: Efficient Mixed-Integer Programming Formulation and Heuristic
Simulator-based surrogate optimisation employing adaptive uncertainty-aware sampling
Optimisation problems involving computationally expensive, black-box functions derived from high-fidelity engineering simulations …
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Simulator-based surrogate optimisation employing adaptive uncertainty-aware sampling
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
Streamlining emergency response: A K-adaptable model and a column-and-constraint-generation algorithm
Emergency response refers to the systematic response to an unexpected, disruptive occurrence such as a natural disaster. The response …
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Streamlining emergency response: A K-adaptable model and a column-and-constraint-generation algorithm
Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study
This study introduces a novel approach for optimizing residential energy systems by combining linear policy graphs with stochastic dual …
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Integrated Operational Planning of Battery Storage Systems for Improved Efficiency in Residential Community Energy Management Using Multistage Stochastic Dual Dynamic Programming: A Finnish Case Study
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