96 research outputs found
K-Adaptability in Two-Stage Distributionally Robust Binary Programming
We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate secondstage policies here-and-now and implement the best of these policies once the uncertain parameters have been observed. We analyze the approximation quality and the computational complexity of the K-adaptability problem, and we derive explicit mixed-integer linear programming reformulations. We also provide efficient procedures for bounding the probabilities with which each of the K second-stage policies is selected
A study of distributionally robust mixed-integer programming with Wasserstein metric: on the value of incomplete data
This study addresses a class of linear mixed-integer programming (MILP)
problems that involve uncertainty in the objective function parameters. The
parameters are assumed to form a random vector, whose probability distribution
can only be observed through a finite training data set. Unlike most of the
related studies in the literature, we also consider uncertainty in the
underlying data set. The data uncertainty is described by a set of linear
constraints for each random sample, and the uncertainty in the distribution
(for a fixed realization of data) is defined using a type-1 Wasserstein ball
centered at the empirical distribution of the data. The overall problem is
formulated as a three-level distributionally robust optimization (DRO) problem.
First, we prove that the three-level problem admits a single-level MILP
reformulation, if the class of loss functions is restricted to biaffine
functions. Secondly, it turns out that for several particular forms of data
uncertainty, the outlined problem can be solved reasonably fast by leveraging
the nominal MILP problem. Finally, we conduct a computational study, where the
out-of-sample performance of our model and computational complexity of the
proposed MILP reformulation are explored numerically for several application
domains
Theory and Applications of Robust Optimization
In this paper we survey the primary research, both theoretical and applied,
in the area of Robust Optimization (RO). Our focus is on the computational
attractiveness of RO approaches, as well as the modeling power and broad
applicability of the methodology. In addition to surveying prominent
theoretical results of RO, we also present some recent results linking RO to
adaptable models for multi-stage decision-making problems. Finally, we
highlight applications of RO across a wide spectrum of domains, including
finance, statistics, learning, and various areas of engineering.Comment: 50 page
Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier
The system operators usually need to solve large-scale unit commitment
problems within limited time frame for computation. This paper provides a
pragmatic solution, showing how by learning and predicting the on/off
commitment decisions of conventional units, there is a potential for system
operators to warm start their solver and speed up their computation
significantly. For the prediction, we train linear and kernelized support
vector machine classifiers, providing an out-of-sample performance guarantee if
properly regularized, converting to distributionally robust classifiers. For
the unit commitment problem, we solve a mixed-integer second-order cone
problem. Our results based on the IEEE 6-bus and 118-bus test systems show that
the kernelized SVM with proper regularization outperforms other classifiers,
reducing the computational time by a factor of 1.7. In addition, if there is a
tight computational limit, while the unit commitment problem without warm start
is far away from the optimal solution, its warmly started version can be solved
to optimality within the time limit
Hydrogen Supply Infrastructure Network Planning Approach towards Chicken-egg Conundrum
In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs),
reasonable hydrogen supply infrastructure (HSI) planning decisions is a premise
for promoting the popularization of HFCVs. However, there is a strong causality
between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of
HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of
demand would in turn stimulate the further investment in HRSs, which is also
known as the ``chicken and egg'' conundrum. Meanwhile, the hydrogen demand is
uncertain with insufficient prior knowledge, and thus there is a
decision-dependent uncertainty (DDU) in the planning issue. This poses great
challenges to solving the optimization problem. To this end, this work
establishes a multi-network HSI planning model coordinating hydrogen, power,
and transportation networks. Then, to reflect the causal relationship between
HFCVs and HRSs effectively without sufficient historical data, a
distributionally robust optimization framework with decision-dependent
uncertainty is developed. The uncertainty of hydrogen demand is modeled as a
Wasserstein ambiguity set with a decision-dependent empirical probability
distribution. Subsequently, to reduce the computational complexity caused by
the introduction of a large number of scenarios and high-dimensional nonlinear
constraints, we developed an improved distribution shaping method and
techniques of scenario and variable reduction to derive the solvable form with
less computing burden. Finally, the simulation results demonstrate that this
method can reduce costs by at least 10.4% compared with traditional methods and
will be more effective in large-scale HSI planning issues. Further, we put
forward effective suggestions for the policymakers and investors to formulate
relevant policies and decisions
Distributionally Robust Optimization: A Review
The concepts of risk-aversion, chance-constrained optimization, and robust
optimization have developed significantly over the last decade. Statistical
learning community has also witnessed a rapid theoretical and applied growth by
relying on these concepts. A modeling framework, called distributionally robust
optimization (DRO), has recently received significant attention in both the
operations research and statistical learning communities. This paper surveys
main concepts and contributions to DRO, and its relationships with robust
optimization, risk-aversion, chance-constrained optimization, and function
regularization
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