An Optimization-based Scheduling Methodology for Appointment Systems with Heterogeneous Customers and Non-stationary Arrival Processes

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

The data in this repository exemplifies the data that was used in the research reported on in the paper An Optimization-based Scheduling Methodology for Appointment Systems with Heterogeneous Customers and Non-stationary Arrival Processes by S. Chatterjee, Y. Hebaish, H. Aprahamian and L. Ntaimo.

Cite

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

https://doi.org/10.1287/ijoc.2023.0039

https://doi.org/10.1287/ijoc.2023.0039.cd

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

@article{chatterjee2024,
  author =        {Chatterjee, S. and Hebaish, Y. and Aprahamian, H. and Ntaimo, L.},
  publisher =     {INFORMS Journal on Computing},
  title =         {{An Optimization-based Scheduling Methodology for Appointment Systems with Heterogeneous Customers and Non-stationary Arrival Processes}},
  year =          {2024},
  doi =           {10.1287/ijoc.2023.0039.cd},
  url =           {https://github.com/INFORMSJoC/2023.0039},
  note =          {Available for download at https://github.com/INFORMSJoC/2023.0039},
}  

Description

The data folder includes the arrival rate data for first-time and crisis customers and the optimal schedule proportions for both service types for all the cases in Table 1 and Figure 9 of the paper. It also contains the schedule proportions obtained using the DPS policy, as shown in Figure 10 of the paper. Refer to the README.md file in that folder for more details on the data.

The scripts folder includes all the Python codes required to generate the various scheduling polcies used in this paper, as well as the results in the Online Supplement. It also includes a 'requirements.txt' file that can be used to load the relevant packages needed to run the scripts into a virtual Python environment. Note that to run some of the scripts, a license of the Gurobi solver will be required. Refer to the README.md file in that folder for more details on the parameters needed for the scripts.

Contact

If you have any question please contact me at:

• Sohom Chatterjee ([email protected])