Computational strategies for non-convex multistage MINLP models with decision-dependent uncertainty and gradual uncertainty resolution

Bora Tarhan (), Ignacio Grossmann () and Vikas Goel ()

Annals of Operations Research, 2013, vol. 203, issue 1, 166 pages

Abstract: In many planning problems under uncertainty the uncertainties are decision-dependent and resolve gradually depending on the decisions made. In this paper, we address a generic non-convex MINLP model for such planning problems where the uncertain parameters are assumed to follow discrete distributions and the decisions are made on a discrete time horizon. In order to account for the decision-dependent uncertainties and gradual uncertainty resolution, we propose a multistage stochastic programming model in which the non-anticipativity constraints in the model are not prespecified but change as a function of the decisions made. Furthermore, planning problems consist of several scenario subproblems where each subproblem is modeled as a nonconvex mixed-integer nonlinear program. We propose a solution strategy that combines global optimization and outer-approximation in order to optimize the planning decisions. We apply this generic problem structure and the proposed solution algorithm to several planning problems to illustrate the efficiency of the proposed method with respect to the method that uses only global optimization. Copyright Springer Science+Business Media, LLC 2013

Keywords: Decision making under uncertainty; Decision dependent uncertainty; Gradual uncertainty resolution; Multistage stochastic programming; Non-convex mixed integer nonlinear program; Global optimization; Outer-approximation; Oil or gas field exploration; Synthesis of process networks (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10479-011-0855-x

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