Design for flexibility: An adjustable robust optimization approach with decision‐dependent uncertainty

ABSTRACT Flexibility is a crucial characteristic of industrial systems that face increasing volatilities and is therefore essential to ensure feasible operation under uncertainty. Flexibility is often closely tied to the design of a system, and careful consideration must be taken to understand the trade‐off between design cost and operational flexibility. In this work, we introduce a design optimization approach that we call design for flexibility , which incorporates a rigorous measure of flexibility directly into the objective function. We employ adjustable robust optimization to model uncertainty and allow for recourse in operational decisions. Compared to traditional flexibility analysis, the proposed approach can accommodate complex uncertainty sets beyond hyperrectangles as well as multiple flexibility indicators, allowing for a more comprehensive representation of uncertainty. We apply the proposed approach to three case studies, where the results demonstrate its versatility and effectiveness in rigorously evaluating the trade‐offs between cost and flexibility when designing industrial systems.

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Publication Details

Journal
AIChE Journal
Published
2026-01-19
DOI
https://doi.org/10.1002/aic.70222
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

Design for flexibility: An adjustable robust optimization approach with decision‐dependent uncertainty

Q. Zhang, Sreekanth Rajagopalan, Satyajith Amaran, Jnana Sai Jagana
AIChE Journal
Advanced Multi-Objective Optimization Algorithms
article

Design for flexibility: An adjustable robust optimization approach with decision‐dependent uncertainty

Q. Zhang, Sreekanth Rajagopalan, Satyajith Amaran, Jnana Sai Jagana
article en

Abstract

ABSTRACT Flexibility is a crucial characteristic of industrial systems that face increasing volatilities and is therefore essential to ensure feasible operation under uncertainty. Flexibility is often closely tied to the design of a system, and careful consideration must be taken to understand the trade‐off between design cost and operational flexibility. In this work, we introduce a design optimization approach that we call design for flexibility , which incorporates a rigorous measure of flexibility directly into the objective function. We employ adjustable robust optimization to model uncertainty and allow for recourse in operational decisions. Compared to traditional flexibility analysis, the proposed approach can accommodate complex uncertainty sets beyond hyperrectangles as well as multiple flexibility indicators, allowing for a more comprehensive representation of uncertainty. We apply the proposed approach to three case studies, where the results demonstrate its versatility and effectiveness in rigorously evaluating the trade‐offs between cost and flexibility when designing industrial systems.

AIChE JournalVol. 72(4)
University of Minnesota (US), Dow Chemical (United States) (US)
Openalex Percentile: Top 81%
Advanced Multi-Objective Optimization Algorithms
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