Hierarchical compromise optimization of ambulance locations under stochastic travel times: software

This Zenodo repository provides the two-stage stochastic 0-1 integer linear programming model and the BFC-CSC algorithm used in the research of the paper entitled "Hierarchical compromise optimization of ambulance locations under stochastic travel times" by Gago-Carro, I., Aldasoro, U., Lee, D.-J. and Merino, M.. This paper has been published in Computers and Operations Research (https://doi.org/10.1016/j.cor.2025.107208). The confidentiality agreement with the Basque Public EMS system reserves the right not to disclose their data. Therefore, the data provided in this repository is not the one used in the research, but is an example of a small size. 
The repository is composed of three folders of files:

0_preparing_files:
    calculate_ideal_antiideal.py
1_f1_f2_model:
    ambulance_BFC-CSC.py
    ambulance_model.py
2_dinf_model:
    ambulances_Dinf_BFC-CSC.py
    ambulances_Dinf_model.py
3_Output:
    1_f1_f2:
        BFC_log_f2a_minMean.txt
        BFC_log_f2b_minMean.txt
        BFC_log_f2c_minMean.txt
        BFC_output_kpi_f2a_minMean.txt
        BFC_output_kpi_f2b_minMean.txt
        BFC_output_kpi_f2c_minMean.txt
        CPLEX_output_kpi_f2a.txt
        CPLEX_output_kpi_f2b.txt
        CPLEX_output_kpi_f2c.txt
    2_dinf:
        BFC-CSC_output_log_DinfminMean.txt
        BFC-CSC_output_Y_Dinf_minMean.csv
        CPLEX_Dinf_output_X.csv
        CPLEX_Dinf_output_Y.csv
4_data:
    ambulanceDataFile.py
    ideals.py