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January 22, 2026AIChE Journal1 citationsOpen Access

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

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JJJnana Sai JaganaSRSreekanth RajagopalanSASatyajith Amaran

Key Points

  • The aim is to develop a design optimization approach that effectively incorporates flexibility into industrial system design under uncertainty.
  • Introduced a design optimization approach named design for flexibility.
  • Employed adjustable robust optimization to model uncertainty with recourse options.
  • Evaluated the approach through three case studies to demonstrate its effectiveness.
  • The approach effectively evaluates trade-offs between cost and flexibility.
  • It accommodates complex uncertainty sets beyond traditional analyses.
  • Demonstrates versatility in adjusting to various operational decisions.

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.

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Cite This Study

Jagana et al. (2026) studied this question.

synapsesocial.com/papers/6971bd4c642b1836717e2030https://doi.org/10.1002/aic.70222
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