Globalized Distributionally Robust Counterpart

This archive is distributed in association with the INFORMS Journal on Computing under the MIT License.

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper "Globalized Distributionally Robust Counterpart" by F. Liu, Z. Chen and S. Wang.

Cite

To cite the contents of this repository, please cite both the paper and this repo, using their respective DOIs.

Below is the BibTex for citing this snapshot of the repository.

@article{GDRC,
  author =        {Feng Liu and Zhi Chen and Shuming Wang},
  publisher =     {INFORMS Journal on Computing},
  title =         {Globalized Distributionally Robust Counterpart},
  year =          {2023},
  url =           {https://github.com/INFORMSJoC/2022.0274},
  doi =           {10.1287/ijoc.2022.0274.cd},
}  

Requirements

For these experiments, we use

• MATLAB 2017a
• Cplex 12.10.0

Content

This repository includes the source code and computational results for all the experiments presented in the paper.

Data files

The folder data includes all the samples used in our experiments.

1. The file d_insample.xlsx contains 20 randomly generated demand samples from a truncated normal distribution.
2. The file d_outsample.xlsx includes 2000 randomly generated demand samples from a truncated normal distribution.
3. The file distance.xlsx represents the distance between different stores.

Code files

The folder scripts includes all the codes used in our experiments.

1. The code files in the folder GADRO-Violation are for evaluating the constraint violation of G-ADRO models under different values of the Wasserstein distance between the out-of-sample distribution and the reference distribution, where the file Violation_main.m is the main program. The codes have been used in Section 5.1 of our paper.

2. The code files in the folder GADRO-Trade_off are for testing the out-of-sample performance of the G-ADRO models including the average total cost and the probability of exceeding the target, where the file GADRO_main.m is the main program. The codes have been used in Section 5.1 of our paper.

3. The code files in the folder GARS-Violation are for evaluating the constraint violation of G-ARS models under different values of the Wasserstein distance between the out-of-sample distribution and the reference distribution, where the file Violation_main is the main program. The codes have been used in Section 5.2 of our paper.

4. The code files in the folder GARS-Trade_off are for computing the average total cost and the average target deviation of G-ARS models under different values of target, where the file GARS_main.m is the main program. The codes have been used in Section 5.2 of our paper.

5. The code files in the folder Cross-Validation are for selecting a proper value of gamma using a 4-fold cross-validation technique.

6. This main folder includes those .m files (functions) that could be used multiple times in the numerical experiments.

Results

1. In the folder GADRO, the file Constraint_Violation.xlsx includes the constraint violation of G-ADRO models, which is exactly Table 1 in our paper. GADRO-1.pdf shows the out-of-sample performance of the G-ADRO models on the trade-off between the average total cost and the probability of exceeding the target while GADRO-2.pdf zooms in the region [2650, 3000] of the average total cost in Figure GADRO-1.pdf and shows that there exists an efficient portion of the frontier. Moreover, the file Data.mat includes the data in the figure (pdf).

2. In the folder GARS, the file Constraint_Violation.xlsx includes the constraint violation of G-ARS models, which is exactly Table 2 in our paper. GARS-1.pdf compares the average total cost of G-ARS models under different values of target while GARS-2.pdf compares the average total cost of G-ARS models under different values of target. Moreover, the file data.mat includes the data in the figure (pdf).

3. In the folder Cross-Validation, the file CV_train.pdf shows the average total cost and probability of exceeding target of the G-ADRO model with different values of gamma while the file CV_test.pdf shows the out-of-sample performance of the G-ADRO model with different values of gamma, including the average total cost and probability of exceeding target. Moreover, the files data_train.mat and data_test.mat include the data in the figures (pdf).

Replicating

To replicate any of the results presented above, put all the data files and the relevant code under the same folder and run the main program.