scholarly journals Distributionally Robust Optimization: A review on theory and applications

2022 ◽  
Vol 12 (1) ◽  
pp. 159
Author(s):  
Fengming Lin ◽  
Xiaolei Fang ◽  
Zheming Gao
Keyword(s):  
Robust Optimization ◽  
Operations Research ◽  
Future Research ◽  
Performance Guarantee ◽  
Distributionally Robust Optimization ◽  
Research Directions ◽  
Out Of Sample ◽  
Solution Methods ◽  
Future Research Directions ◽  
Distributionally Robust

<p style='text-indent:20px;'>In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart reformulations. Next, we summarize the efficient solution methods, out-of-sample performance guarantee, and convergence analysis. Then, we illustrate some applications of DRO in machine learning and operations research, and finally, we discuss the future research directions.</p>

scholarly journals Emergency Logistics in a Large-Scale Disaster Context: Achievements and Challenges

2019 ◽  
Vol 16 (5) ◽  
pp. 779 ◽  
Author(s):  
Yiping Jiang ◽  
Yufei Yuan
Keyword(s):  
Emergency Response ◽  
Operations Research ◽  
Large Scale ◽  
Future Research ◽  
Research Directions ◽  
Business Operations ◽  
Emergency Logistics ◽  
Key Characteristics ◽  
Future Research Directions ◽  
New Research

There is growing research interest in emergency logistics within the operations research (OR) community. Different from normal business operations, emergency response for large scale disasters is very complex and there are many challenges to deal with. Research on emergency logistics is still in its infancy stage. Understanding the challenges and new research directions is very important. In this paper, we present a literature review of emergency logistics in the context of large-scale disasters. The main contributions of our study include three aspects: First, we identify key characteristics of large-scale disasters and assess their challenges to emergency logistics. Second, we analyze and summarize the current literature on how to deal with these challenges. Finally, we discuss existing gaps in the relevant research and suggest future research directions.

scholarly journals International Real Estate Review

2011 ◽  
Vol 14 (1) ◽  
pp. 85-117
Author(s):  
Charles Ka Yui Leung ◽  
◽  
Kenneth K. Chow ◽  
Matthew S. Yiu ◽  
Dickson C. Tam ◽  
...  
Keyword(s):  
Real Estate ◽  
House Price ◽  
Policy Implications ◽  
Future Research ◽  
Price Dynamics ◽  
Significant Heterogeneity ◽  
Research Directions ◽  
Out Of Sample ◽  
Future Research Directions ◽  
House Price Dynamics

This paper attempts to contribute in several ways. Theoretically, it proposes simple models of house price dynamics and construction dynamics, all based on the maximization problems of forward-looking agents, which may carry independent interests. Simplified versions of the model implications are estimated with the data from four major cities in China. Both price and construction dynamics exhibit strong persistence in all cities. Significant heterogeneity across cities is found. Our models out-perform widely used alternatives in in-sample-fitting for all cities, although similar success is only limited to highly developed cities in out-of-sample forecasting. Policy implications and future research directions are also discussed.

scholarly journals From Data to Decisions: Distributionally Robust Optimization Is Optimal

2020 ◽  
Author(s):  
Bart P. G. Van Parys ◽  
Peyman Mohajerin Esfahani ◽  
Daniel Kuhn
Keyword(s):  
Robust Optimization ◽  
Cost Function ◽  
Optimization Problem ◽  
Empirical Distribution ◽  
Distributionally Robust Optimization ◽  
Expected Cost ◽  
Large Deviations Theory ◽  
Out Of Sample ◽  
Distributionally Robust ◽  
Meta Optimization

We study stochastic programs where the decision maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transforms the data to an estimate of the expected cost function under the unknown data-generating distribution, that is, a predictor, and an optimizer of the estimated cost function that serves as a near-optimal candidate decision, that is, a prescriptor. As functions of the data, predictors and prescriptors constitute statistical estimators. We propose a meta-optimization problem to find the least conservative predictors and prescriptors subject to constraints on their out-of-sample disappointment. The out-of-sample disappointment quantifies the probability that the actual expected cost of the candidate decision under the unknown true distribution exceeds its predicted cost. Leveraging tools from large deviations theory, we prove that this meta-optimization problem admits a unique solution: The best predictor-prescriptor-pair is obtained by solving a distributionally robust optimization problem over all distributions within a given relative entropy distance from the empirical distribution of the data. This paper was accepted by Chung Piaw Teo, optimization.

scholarly journals Performance evaluation for distributionally robust optimization with binary entries

2020 ◽  
Vol 11 (1) ◽  
pp. 1-9
Author(s):  
Shunichi Ohmori ◽  
Kazuho Yoshimoto
Keyword(s):  
Performance Evaluation ◽  
Robust Optimization ◽  
Optimization Technique ◽  
Data Driven ◽  
Optimal Decision ◽  
Distributionally Robust Optimization ◽  
Worst Case ◽  
Leibler Divergence ◽  
Out Of Sample ◽  
Distributionally Robust

We consider the data-driven stochastic programming problem with binary entries where the probability of existence of each entry is not known, instead realization of data is provided. We applied the distributionally robust optimization technique to minimize the worst-case expected cost taken over the ambiguity set based on the Kullback-Leibler divergence. We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.

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