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 set of alternatives, 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 mixedinteger linear programming formulations for solving such problems in the presence of many kinds of constraints. 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 involve 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. We provide several illustrative examples as well as evidence on the computational performance of Decision Programming formulations.

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