Projects per year
Influence diagrams are widely employed to represent multi-stage decision problems in which each decision is a choice from a discrete set of alternative courses of action, uncertain chance events have discrete outcomes, and prior decisions may influence the probability distributions of uncertain chance events endogenously. In this paper, we develop the Decision Programming framework which extends the applicability of influence diagrams by developing mixed-integer linear programming formulations for such problems. In particular, Decision Programming makes it possible to (i) solve problems in which earlier decisions cannot necessarily be recalled later, for instance, when decisions are taken by agents who cannot communicate with each other; (ii) accommodate a broad range of deterministic and chance constraints, including those based on resource consumption, logical dependencies or risk measures such as Conditional Value-at-Risk; and (iii) determine all non-dominated decision strategies in problems which multiple value objectives. In project portfolio selection problems, Decision Programming allows scenario probabilities to depend endogenously on project decisions and can thus be viewed as a generalization of Contingent Portfolio Programming (Gustafsson & Salo, 2005). We present several illustrative examples, evidence on the computational performance of Decision Programming formulations, and directions for further development.
- Contingent portfolio programming
- Decision analysis
- Decision trees
- Influence diagrams
- Stochastic programming
FingerprintDive into the research topics of 'Decision programming for mixed-Integer multi-stage optimization under uncertainty'. Together they form a unique fingerprint.
01/09/2020 → 31/08/2024
Project: Academy of Finland: Other research funding