Optimization Under Uncertainty

Optimization under uncertainty plays a crucial role in process systems engineering. these approaches incorporate uncertainties related to market demand, raw material availability, equipment reliability, and process dynamics into optimization models. By considering these uncertainties, we can develop decision strategies that are robust, resilient, and capable of adapting to changing conditions.

Challenges in Optimization Under Uncertainty:

Optimization under uncertainty comes with its own set of challenges that need to be addressed for successful implementation. Some of the key challenges include: 

• Uncertainty Characterization: Accurately characterizing uncertainties and quantifying their impacts on process systems can be challenging. Uncertainty sources may be diverse, non-linear, and dependent on various factors, requiring advanced modeling techniques and data analysis methods.

•  Computational Complexity: Incorporating uncertainty into optimization models increases computational complexity, as it involves solving optimization problems multiple times for different scenarios. Advanced algorithms, optimization techniques, and efficient computational tools are needed to handle the increased computational burden.

Strategies for Optimization Under Uncertainty:

To address these challenges and achieve effective optimization under uncertainty in process systems engineering, several strategies and techniques can be employed:

• Stochastic Programming: Stochastic programming techniques explicitly model uncertainties as random variables and optimize decisions based on probabilistic outcomes. These models allow for the incorporation of probability distributions and risk measures, providing robust decision-making frameworks.

• Scenario-Based Optimization: Scenario-based optimization involves generating a set of representative scenarios and optimizing decisions based on the outcomes of these scenarios. This approach captures a range of possible conditions and enables robust decision-making under uncertainty.

• Robust Optimization: Robust optimization techniques aim to optimize decisions that are resilient to uncertainties by considering the worst-case scenarios. These models seek solutions that perform well under a range of possible conditions, providing a level of protection against adverse events.

Relevant Research Articles from Group:

• Maheshwari A, Misra S, Gudi RD, Subbiah S, Laspidou C. An Operational Scheduling Framework for Tanker-Based Water Distribution Systems under Uncertainty. Industrial & Engineering Chemistry Research (Just Accepted), 2023. https://doi.org/10.1021/acs.iecr.3c00496.