Interactive models in operations research—an introduction and some future research directions

1976 ◽  
Vol 3 (4) ◽  
pp. 305-312 ◽  
Author(s):  
Maurice Pollack
Keyword(s):  
Operations Research ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Interactive Models

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 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>

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