Data‐driven adaptive nested robust optimization: General modeling framework and efficient computational algorithm for decision making under uncertainty

AIChE Journal ◽  
2017 ◽  
Vol 63 (9) ◽  
pp. 3790-3817 ◽  
Author(s):  
Chao Ning ◽  
Fengqi You
Keyword(s):  
Decision Making ◽  
Robust Optimization ◽  
Computational Algorithm ◽  
Data Driven ◽  
Decision Making Under Uncertainty ◽  
Modeling Framework

scholarly journals Data-driven Modelling for decision making under uncertainty

2018 ◽  
Vol 300 ◽  
pp. 012013 ◽  
Author(s):  
Layla Angria S ◽  
Yunita Dwi Sari ◽  
Muhammad Zarlis ◽  
Tulus
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Decision Making Under Uncertainty

scholarly journals Robust Linear Programming and Its Application to Water and Environmental Decision-Making under Uncertainty

2018 ◽  
Vol 11 (1) ◽  
pp. 33 ◽  
Author(s):  
Yang Zhou ◽  
Bo Yang ◽  
Jingcheng Han ◽  
Yuefei Huang
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Robust Optimization ◽  
Water Quality Management ◽  
Optimization Techniques ◽  
Programming Approach ◽  
Decision Making Under Uncertainty ◽  
Environmental Decision Making ◽  
Robust Linear Programming ◽  
Environmental Decision

In this study, we introduce a robust linear programming approach for water and environmental decision-making under uncertainty. This approach is of significant practical utility to decision makers for obtaining reliable and robust management decisions that are “immune” to the uncertainty attributable to data perturbations. The immunization guarantees that the chosen robust management plan will be implementable with no violation of the mandatory constraints of the problem being studied—i.e., natural resource supply constraint, environmental carrying capacity constraint, environmental pollution control constraint, etc.—and that the actual value of the objective will be no worse than the given estimation if the perturbations of data fall within the specified uncertainty set. A simplified example in regional water quality management is provided to help water and environmental practitioners to better understand how to implement robust linear programming from the perspective of application, as well as to illustrate the significance and necessity of implementing robust optimization techniques in real-world practices. Robust optimization is a growing research field that requires more interdisciplinary research efforts and engagements from water and environmental practitioners. Both may benefit from the advances of management science.

scholarly journals A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Shunichi Ohmori
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Robust Optimization ◽  
Nearest Neighbor ◽  
Predictive Analytics ◽  
Uncertain Parameters ◽  
Minimum Volume ◽  
K Nearest Neighbor ◽  
Modeling Framework ◽  
Prescriptive Analytics

This paper studies the integration of predictive and prescriptive analytics framework for deriving decision from data. Traditionally, in predictive analytics, the purpose is to derive prediction of unknown parameters from data using statistics and machine learning, and in prescriptive analytics, the purpose is to derive a decision from known parameters using optimization technology. These have been studied independently, but the effect of the prediction error in predictive analytics on the decision-making in prescriptive analytics has not been clarified. We propose a modeling framework that integrates machine learning and robust optimization. The proposed algorithm utilizes the k-nearest neighbor model to predict the distribution of uncertain parameters based on the observed auxiliary data. The enclosing minimum volume ellipsoid that contains k-nearest neighbors of is used to form the uncertainty set for the robust optimization formulation. We illustrate the data-driven decision-making framework and our novel robustness notion on a two-stage linear stochastic programming under uncertain parameters. The problem can be reduced to a convex programming, and thus can be solved to optimality very efficiently by the off-the-shelf solvers.

Decision-Making Under Uncertainty for a Digital Thread-Enabled Design Process

2021 ◽  
Vol 143 (9) ◽  
Author(s):  
Victor Singh ◽  
Karen E. Willcox
Keyword(s):  
Decision Making ◽  
Bayesian Statistics ◽  
Structural Design ◽  
Decision Problem ◽  
Data Driven ◽  
Decision Making Under Uncertainty ◽  
Product Lifecycle ◽  
Decision Making Problem ◽  
Different Types ◽  
Multiple Product

Abstract Digital thread is a data-driven architecture that links together information from all stages of the product lifecycle. Despite increasing application in manufacturing, maintenance/operations, and design related tasks, a principled formulation of analyzing the decision-making problem under uncertainty for the digital thread remains absent. The contribution of this article is to present a formulation using Bayesian statistics and decision theory. First, we address how uncertainty propagates in the product lifecycle and how the digital thread evolves based on the decisions we make and the data we collect. By using these mechanics, we explore designing over multiple product generations or iterations and provide an algorithm to solve the underlying multistage decision problem. We illustrate our method on an example structural design problem where our method can quantify and optimize different types and sequences of decisions, ranging from experimentation, manufacturing, and sensor placement/selection, to minimize total accrued costs.

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