Risk-averse decision strategies for influence diagrams using rooted junction trees

Abstract

This paper presents how a mixed-integer programming (MIP) formulation for influence diagrams that is based on their gradual rooted junction tree representation can be extended to incorporate more general modelling features, such as risk considerations and problem-specific constraints. We propose two algorithms that enable our reformulations by performing targeted modifications either to the underlying influence diagram or to the associated gradual rooted junction tree representation. We present computational experiments highlighting the superior computational performance of our reformulation against an alternative state-of-the-art MIP formulation for influence diagrams that, by default, can accommodate those modelling features.

Olli Herrala

Doctoral Researcher

Olli Herrala is a Doctoral Candidate in the Systems Analysis Laboratory in Aalto University.

Topias Terho

Doctoral Researcher

Topias Terho is a research assistant working with developments on decision programming framework

Fabricio Oliveira

Associate Professor of Operations Research

Fabricio Oliveira is an Associate Professor of Operations Research at DTU Management. He also holds a position of Adjunct Professor in the Department of Mathematics and Systems Analysis at Aalto University