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arXiv · 2106.12708

A Scalable Stochastic Programming Approach for the Design of Flexible Systems

Joshua L. Pulsipher·Victor M. Zavala
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Abstract

We study the problem of designing systems in order to minimize cost while meeting a given flexibility target. Flexibility is attained by enforcing a joint chance constraint, which ensures that the system will exhibit feasible operation with a given target probability level. Unfortunately, joint chance constraints are complicated mathematical objects that often need to be reformulated using mixed-integer programming (MIP) techniques. In this work, we cast the design problem as a conflict resolution problem that seeks to minimize cost while maximizing flexibility. We propose a purely continuous relaxation of this problem that provides a significantly more scalable approach relative to MIP methods and show that the formulation delivers solutions that closely approximate the Pareto set of the original joint chance-constrained problem.

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Joshua L. Pulsipher, Victor M. Zavala. 2021-06-24. A Scalable Stochastic Programming Approach for the Design of Flexible Systems. https://doi.org/10.1016/j.compchemeng.2019.05.033

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