Wasserstein distributionally robust shortest path problem

  title={Wasserstein distributionally robust shortest path problem},
  author={Zhuolin Wang and Keyou You and Shiji Song and Yuli Zhang},
  journal={Eur. J. Oper. Res.},
Second-Order Conic Programming Approach for Wasserstein Distributionally Robust Two-Stage Linear Programs
A second-order conic programming (SOCP) approach to solve distributionally robust two-stage stochastic linear programs over 1-Wasserstein balls is proposed and it is shown that such a robust program is generally NP-hard as it involves a norm maximization problem over a polyhedron.
Distributionally robust mean-absolute deviation portfolio optimization using wasserstein metric
  • Dali Chen, Yuwei Wu, Jingquan Li, Xiaohui Ding, Caihua Chen
  • Computer Science
    Journal of global optimization : an international journal dealing with theoretical and computational aspects of seeking global optima and their applications in science, management and engineering
  • 2022
The experimental results show that the portfolios constructed by the proposed DR-MAD model are superior to the benchmarks in terms of profitability and stability in most fluctuating markets, suggesting that Wasserstein distributionally robust optimization framework is an effective approach to address data uncertainty in portfolio optimization.
Distributionally robust front distribution center inventory optimization with uncertain multi-item orders
As a new retail model, the front distribution center (FDC) has been recognized as an effective instrument for timely order delivery. However, the high customer demand uncertainty, multi-item order
Target-Oriented User Equilibrium Considering Travel Time, Late Arrival Penalty, and Travel Cost on the Stochastic Tolled Traffic Network
A target-oriented multi-attribute travel utility model is introduced, where each attribute is assigned a target by travelers, and travelers’ objective is to maximize their travel utility that is determined by the achieved targets.
Distributionally Robust Optimization: A review on theory and applications
This paper starts with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart reformulations, and summarizes the efficient solution methods, out-of-sample performance guarantee, and convergence analysis.
Finding Reliable Paths Considering the Earliest Arrival Time and the Latest Departure Time With 3-Parameter Lognormal Travel Times
Two models and algorithms to find reliable paths considering the earliest arrival time and the latest departure time are presented, using the 3-parameter lognormal distribution to describe travel time.
Distributional Robust Portfolio Construction based on Investor Aversion
: In behavioral finance, aversion affects investors' judgment of future uncertainty when profit and loss occur. Considering investors' aversion to loss and risk, and the ambiguous uncertainty


Data-Driven Distributionally Robust Shortest Path Problem Using the Wasserstein Ambiguity Set
The model can be reformulated to a mixed 0-1 second order cone program (SOCP) that can be solved efficiently by the optimization techniques and the experimental results show that the proposed model can achieve a considerable out-of-sample performance.
On the robust shortest path problem
Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach
This paper reformulates the original shortest path problem as a cardinality minimization problem directly based on samples of travel time on each road link, which can be obtained from the GPS trajectory of vehicles, and applies an ℓ1-norm minimization technique and its variants to solve the cardinality problem.
Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems
This paper proposes a model that describes uncertainty in both the distribution form (discrete, Gaussian, exponential, etc.) and moments (mean and covariance matrix) and demonstrates that for a wide range of cost functions the associated distributionally robust stochastic program can be solved efficiently.
Kullback-Leibler divergence constrained distributionally robust optimization
The main contribution of the paper is to show that the KL divergence constrained DRO problems are often of the same complexity as their original stochastic programming problems and, thus, KL divergence appears a good candidate in modeling distribution ambiguities in mathematical programming.
A Mean-Risk Model for the Traffic Assignment Problem with Stochastic Travel Times
A new traffic assignment model that takes into account the stochastic nature of travel times is proposed and it is proved that both can be encoded by a representation with just polynomially many paths.