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.
@article{arxiv.2106.12708,
title = {A Scalable Stochastic Programming Approach for the Design of Flexible Systems},
author = {Joshua L. Pulsipher and Victor M. Zavala},
journal= {arXiv preprint arXiv:2106.12708},
year = {2021}
}