scholarly journals Kullback–Leibler divergence-based distributionally robust optimisation model for heat pump day-ahead operational schedule to improve PV integration

2018 ◽  
Vol 12 (13) ◽  
pp. 3136-3144 ◽  
Author(s):  
Zihao Li ◽  
Wenchuan Wu ◽  
Boming Zhang ◽  
Xue Tai
Keyword(s):  
Heat Pump ◽  
Robust Optimisation ◽  
Leibler Divergence ◽  
Optimisation Model ◽  
Distributionally Robust ◽  
Operational Schedule

A robust optimisation model for production planning and pricing under demand uncertainty

2016 ◽  
Vol 54 (13) ◽  
pp. 3885-3905 ◽  
Author(s):  
Ehsan Ardjmand ◽  
Gary R. Weckman ◽  
William A. Young ◽  
Omid Sanei Bajgiran ◽  
Bizhan Aminipour
Keyword(s):  
Production Planning ◽  
Demand Uncertainty ◽  
Robust Optimisation ◽  
Optimisation Model

scholarly journals Distributionally Robust Counterfactual Risk Minimization

2020 ◽  
Vol 34 (04) ◽  
pp. 3850-3857
Author(s):  
Louis Faury ◽  
Ugo Tanielian ◽  
Elvis Dohmatob ◽  
Elena Smirnova ◽  
Flavian Vasile
Keyword(s):  
Decision Making ◽  
Robust Optimization ◽  
Model Uncertainty ◽  
State Of The Art ◽  
Risk Minimization ◽  
Practical Applications ◽  
Uncertainty Measures ◽  
Leibler Divergence ◽  
Benchmark Datasets ◽  
Distributionally Robust

This manuscript introduces the idea of using Distributionally Robust Optimization (DRO) for the Counterfactual Risk Minimization (CRM) problem. Tapping into a rich existing literature, we show that DRO is a principled tool for counterfactual decision making. We also show that well-established solutions to the CRM problem like sample variance penalization schemes are special instances of a more general DRO problem. In this unifying framework, a variety of distributionally robust counterfactual risk estimators can be constructed using various probability distances and divergences as uncertainty measures. We propose the use of Kullback-Leibler divergence as an alternative way to model uncertainty in CRM and derive a new robust counterfactual objective. In our experiments, we show that this approach outperforms the state-of-the-art on four benchmark datasets, validating the relevance of using other uncertainty measures in practical applications.

A robust optimisation model for remanufacturing network design problem with one-way substitution

2016 ◽  
Vol 24 (4) ◽  
pp. 484 ◽  
Author(s):  
Alireza Ghafarimoghadam ◽  
Atefeh Karimi ◽  
Mohammad Mousazadeh ◽  
Mir Saman Pishvaee
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Network Design Problem ◽  
Robust Optimisation ◽  
Optimisation Model
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