Publications de Grani Adiwena Hanasusanto

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approximation error can be substantial. In this paper we show that two-stage robust ...

2018

Data-Driven Inverse Optimization with Incomplete Information

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani, Grani Adiwena Hanasusanto

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical ...

2018

Ambiguous Joint Chance Constraints under Mean and Dispersion Information

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

We study joint chance constraints where the distribution of the uncertain parameters is only known to belong to an ambiguity set characterized by the mean and support of the uncertainties and by an upper bound on their dispersion. This setting gives rise t ...

Informs2017

K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate second-stage policies here-and-now and implement the best of these policies once t ...

Elsevier Science Bv2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

Daniel Kuhn, Grani Adiwena Hanasusanto

We present a risk-averse multi-dimensional newsvendor model for a class of products whose demands are strongly correlated and subject to fashion trends that are not fully understood at the time when orders are placed. The demand distribution is known to be ...

Springer Verlag2015

A Distributionally Robust Perspective on Uncertainty Quantification and Chance Constrained Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

The objective of uncertainty quantification is to certify that a given physical, engineering or economic system satisfies multiple safety conditions with high probability. A more ambitious goal is to actively influence the system so as to guarantee and mai ...

Springer Verlag2015

K-Adaptability in Two-Stage Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Over the last two decades, robust optimization has emerged as a computationally attractive approach to formulate and solve single-stage decision problems affected by uncertainty. More recently, robust optimization has been successfully applied to multi-sta ...

Informs2015

Robust Data-Driven Dynamic Programming

Daniel Kuhn, Grani Adiwena Hanasusanto

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...

2013