A scalable stochastic programming approach for the design of flexible systems

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 chan...

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Bibliographic Details
Published in:Computers & chemical engineering 2019-09, Vol.128 (C), p.69-76
Main Authors: Pulsipher, Joshua L., Zavala, Victor M.
Format: Article
Language:English
Subjects:
Complex systems
Design
Flexibility
Joint chance constraints
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Summary: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.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2019.05.033