Asymmetry and Ambiguity in Newsvendor Models

@article{Natarajan2018AsymmetryAA,
  title={Asymmetry and Ambiguity in Newsvendor Models},
  author={Karthik Natarajan and Melvyn Sim and Joline Uichanco},
  journal={Manag. Sci.},
  year={2018},
  volume={64},
  pages={3146-3167}
}
A basic assumption of the classical newsvendor model is that the probability distribution of the random demand is known. But in most realistic settings, only partial distribution information is available or reliably estimated. The distributionally robust newsvendor model is often used in this case where the worst-case expected profit is maximized over the set of distributions satisfying the known information, which is usually the mean and covariance of demands. However, covariance does not… 
View via Publisher
optimization-online.org
49 Citations
Highly Influential Citations
5
Background Citations
16
Methods Citations
5
Results Citations
3

49 Citations

Citation Type

Citation Type

Regret in the Newsvendor Model with Demand and Yield Randomness
TLDR
This work combat the issue of distributional ambiguity by taking a data-driven distributionally robust optimization approach to hedge against all distributions that are sufficiently close to a uniform and discrete distribution of empirical data, where closeness is measured by the type-infinity Wasserstein distance.
Regret in the Newsvendor Model with Demand and Yield Randomness
TLDR
This work combat the issue of distributional ambiguity by taking a data-driven distributionally robust optimization approach to hedge against all distributions that are sufficiently close to a uniform and discrete distribution of empirical data, where closeness is measured by the type-1Wasserstein distance.
Statistical Bias in the Newsvendor Model
We consider the Newsvendor model where uncertain demand is assumed to follow a probabilistic distribution with known functional form, but unknown parameters. These parameters are estimated,…
Distributionally Robust Newsvendor Problems with Variation Distance
We use distributionally robust stochastic programs (DRSP) to model a general class of newsvendor problems where the underlying demand distribution is unknown, and so the goal is to find an order…
Robust newsvendor problems with compound Poisson demands
TLDR
To make the best use of all the information contained in the first few moments of demand sizes as well as the structural properties of a compound demand distribution to compute a better ordering quantity, a new decision model employing Panjer's recursion as constraints is proposed.
On the Heavy-Tail Behavior of the Distributionally Robust Newsvendor
Distributionally robust optimization is increasingly becoming a popular methodology to deal with uncertainty in optimization problems. Although the methodology optimizes for the worst-case…
Controlling risk and demand ambiguity in newsvendor models
A Robust Data-Driven Approach for the Newsvendor Problem with Nonparametric Information
TLDR
This work develops an approach that uses data input to construct a distribution ambiguity set with the nonparame... to solve the standard newsvendor problem with an unknown demand distribution.
Distribution-Free Inventory Risk Pooling in a Multilocation Newsvendor
TLDR
An algorithm is proposed that can approximate general fulfillment cost structures by nested structures, yielding a computationally tractable heuristic for distributionally robust inventory optimization on general newsvendor networks.
The overconfident and ambiguity-averse newsvendor problem in perishable product decision
...
...

References

SHOWING 1-10 OF 73 REFERENCES
Regret in the Newsvendor Model with Partial Information
TLDR
This paper derives the order quantities that minimize the newsvendor's maximum regret of not acting optimally, which can be extended to a variety of problems that require a robust but not conservative solution.
Newsvendor optimization with limited distribution information
TLDR
This model tries to minimize the regret that is defined as the ratio of the expected cost based on limited information to that based on complete information, called Relative Expected Value of Distribution (REVD), and shows how to derive an optimal solution to the REVD model.
Expected Value of Distribution Information for the Newsvendor Problem
TLDR
This paper aims at computing the maximum EVDI over all f ∈ F for any order quantity, and an optimization procedure is provided to calculate the order quantity that minimizes themaximum EVDI.
The Newsvendor under Demand Ambiguity: Combining Data with Moment and Tail Information
TLDR
This work proves the proposed approach is (weakly) consistent under some technical regularity conditions and analytically characterize its rate of convergence, and develops theoretical and numerical results for the ambiguity-averse, repeated newsvendor setting.
A maximum entropy approach to the newsvendor problem with partial information
Technical Note - A Risk-Averse Newsvendor Model Under the CVaR Criterion
TLDR
A risk-averse newsvendor with stochastic price-dependent demand with Conditional Value-at-Risk (CVaR) as the decision criterion is considered to investigate the optimal pricing and ordering decisions in such a setting.
The Data-Driven Newsvendor Problem: New Bounds and Insights
TLDR
This paper analyzes the sample average approximation SAA approach for the data-driven newsvendor problem and obtains a new analytical bound on the probability that the relative regret of the SAA solution exceeds a threshold.
Distributionally robust multi-item newsvendor problems with multimodal demand distributions
TLDR
This work presents 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 and demonstrates that disregarding ambiguity or multimodality can lead to unstable solutions that perform poorly in stress test experiments.
How Larger Demand Variability May Lead to Lower Costs in the Newsvendor Problem
TLDR
It is shown that there exist demand distributions for which the intuition is misleading, i.e., for which larger variances occur in combination with lower costs.
TRACTABLE ROBUST EXPECTED UTILITY AND RISK MODELS FOR PORTFOLIO OPTIMIZATION
TLDR
This paper derives exact and approximate optimal trading strategies for a robust (maximin) expected utility model, where the investor maximizes his worst‐case expected utility over a set of ambiguous distributions.
...
...