Decision programming for mixed-Integer multi-stage optimization under uncertainty

Ahti Salo*, Juho Andelmin, Fabricio Oliveira

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
118 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)550-565
Number of pages16
JournalEuropean Journal of Operational Research
Issue number2
Early online date2022
Publication statusPublished - 1 Jun 2022
MoE publication typeA1 Journal article-refereed


  • Contingent portfolio programming
  • Decision analysis
  • Decision trees
  • Influence diagrams
  • Stochastic programming


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