Adaptive Two-stage Stochastic Programming with an Application to Capacity Expansion Planning

Abstract

Multi-stage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that are fully adapted to the uncertainty. Often, e.g. due to contractual constraints, such flexible and adaptive policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of planning periods. Static or two-stage stochastic programming frameworks might be better suited to such settings, where the decisions for all periods are made here-andnow and do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, where the stages are not predetermined but part of the optimization problem. In particular, each component of the decision policy has an associated revision point, a period prior to which the decision is predetermined and after which it is revised to adjust to the uncertainty realized thus far. We motivate this setting using the multi-period newsvendor problem by deriving an optimal adaptive policy. We label the proposed approach as adaptive two-stage stochastic programming and provide a generic mixed-integer programming formulation for finite stochastic processes. We show that adaptive two-stage stochastic programming is NP-hard in general.Next, we derive bounds on the value of adaptive two-stage programming in comparison to the two-stage and multi-stage approaches for a specific problem structure inspired by the capacity expansion planning problem.Since directly solving the mixed-integer linear program associated with the adaptive two-stage approach might be very costly for large instances, we propose several heuristic solution algorithms based on the bound analysis. We provide approximation guarantees for these heuristics. Finally, we present an extensive computational study on an electricity generation capacity expansion planning problem and demonstrate the computational and practical impacts of the proposed approach from various perspectives.

A Unified Framework for Adjustable Robust Optimization with Endogenous Uncertainty

“…Instead, one has to restrict oneself to a few revision points at which the planning decisions are revised and updated. In their recent work, Basciftci et al 58 have formally introduced an adaptive two-stage stochastic programming approach in which for each decision policy, one revision point is considered and chosen as part of the optimization problem. This problem can be viewed as a stochastic program with type-2b endogenous uncertainty where planning decisions for time periods before the revision point have to be made here and now while decisions after the revision point can be adjusted based on the uncertainty revealed up to the revision point.…”

Stochastic dual dynamic programming for multistage stochastic mixed-integer nonlinear optimization

“…This provides an extremely powerful modeling framework. Special classes of MS-MINLP, such as multistage stochastic linear programming (MS-LP) and mixed-integer linear programming (MS-MILP), have already found a wide range of applications in diverse fields such as electric power system scheduling and expansion planning [3,36,37,39], portfolio optimization under risk [8,21,26], and production and capacity planning problems [2,4,9,12], just to name a few.…”