Robust Actionable Prescriptive Analytics

52 Pages Posted: 11 May 2022 Last revised: 12 Sep 2024

See all articles by Li Chen

Li Chen

University of Sydney Business School

Melvyn Sim

National University of Singapore (NUS) - NUS Business School

Xun Zhang

University of Science and Technology of China (USTC)

Long Zhao

NUS Business School - Department of Analytics and Operations

Minglong Zhou

Fudan University - School of Management

Date Written: May 11, 2022

Abstract

We propose a new robust actionable prescriptive analytics framework that leverages past data and side information to minimize a risk-based objective function under distributional ambiguity. Our framework aims to find a policy that directly transforms the side information into implementable decisions. Specifically, we focus on developing actionable response policies that offer the benefits of interpretability and implementability. To address the potential issue of overfitting to empirical data, we adopt a data-driven robust satisficing approach that effectively handles uncertainty. We tackle the computational challenge for linear optimization models with recourse by developing a new tractable safe approximation for robust constraints, accommodating bilinear uncertainty and general norm-based uncertainty sets. Additionally, we introduce a biaffine recourse adaptation to enhance the quality of the approximation. Furthermore, we present a localized robust satisficing model that efficiently solves combinatorial optimization problems with tree-based static policies. Finally, we demonstrate the practical application of our framework through a simulation case study on risk-minimizing portfolio optimization using past returns as side information. We also provide a simulation case study on how the framework can be applied to obtain an interpretable policy for allocating taxis to different demand regions in response to weather information.

Keywords: robust optimization, robust satisficing, robust analytics, prescriptive analytics, side information

Suggested Citation

Chen, Li and Sim, Melvyn and Zhang, Xun and Zhao, Long and Zhou, Minglong, Robust Actionable Prescriptive Analytics (May 11, 2022). Available at SSRN: https://ssrn.com/abstract=4106222 or http://dx.doi.org/10.2139/ssrn.4106222

Li Chen

University of Sydney Business School ( email )

Cnr. of Codrington and Rose Streets
Sydney, NSW 2006
Australia

Melvyn Sim

National University of Singapore (NUS) - NUS Business School ( email )

1 Business Link
Singapore, 117592
Singapore

Xun Zhang

University of Science and Technology of China (USTC) ( email )

96, Jinzhai Road
Hefei, Anhui 230026
China

Long Zhao

NUS Business School - Department of Analytics and Operations ( email )

15 Kent Ridge Dr
Singapore, Singapore 119245
Singapore

Minglong Zhou (Contact Author)

Fudan University - School of Management ( email )

No. 670, Guoshun Road
No.670 Guoshun Road
Shanghai, 200433
China

HOME PAGE: http://https://sites.google.com/view/minglongzhou

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