A-Metaheuristic-Approach-to-the-Multi-period-Reliable-Location-Problem-in-Time-Varying-Risk

Description

This directory comprises the folders containing the source code, data and figures for the research article: A Metaheuristic Approach to the Multi period Reliable Location Problem in Time Varying-Risk

Source

This folder contains the python source code for optimizing multi-period reliable location problem:

Convex_Hull_Reduction.ipynb

• This python script contains Convex hull method for searching potential location.

Heuristics.ipynb

• This python script contains the supporting functions for neigborhood search of the SMC-SA algorithm.

SMCSA_approach.ipynb

• This python script contains the main SMC-SA algorithm for our metaheuristic optimization.

Reliable_Location_infected-Linear-Multi period.ipynb

• his python script contains the main MIP formulation using Gurobi.

Sensitivity_analysis.ipynb

• This python script contains the main driver code for our Uncertainty analysis

Data

This folder contains all data sets.

• Each data file is provided in readable text formats (.csv)
• Data file contains different 3 data sets (Data_1,Data_2,Data_3), different number of nodes (49, 88, 150) and different risk levels (low, medium, high).
• Text header contains relevant data on each sample ( Index, demand, emerg_cost, prob_fail, fixed cost, lat, lon, State, State_cd)
• For sensitivity analysis, data are in q_sensitivity that contain simulated information from beta distribution.

Figure

This folder contains all the figures from our research.

How to Run the Code:

• Download the Heuristics.ipynb, SMCSA_approach.ipynb, Reliable_Location_infected-Linear-Multi period.ipynb, Sensitivity_analysis.ipynb python scripts from source folder and place them in a directory.
• Download the data folder and place them in the same directory as the python scripts.

MIP

• Run the scripts Reliable_Location_infected-Linear-Multi period.ipynb, then check the output in the python notebook.

Convex hull

• Run the scripts Convex_Hull_Reduction.ipynb by adjusting N value and considering percent area coverage, then use location index number to input in SMC-SA algorithm.

SMC-SA

• Run the scripts SMCSA_approach.ipynb, then check the output in the python notebook.

Uncertainty analysis

• Run the scripts Sensitivity_analysis.ipynb, then check the figure in the python notebook.