Bivariate Clustering and Regression using MILP

This is a project for Combinatorial Optimization and Network Analysis course (1254083) at Amirkabir University of Technology.

This project implements the following paper:

A unified framework for bivariate clustering and regression problems via mixed-integer linear programming
(John Alasdair Warwicker, Steffen Rebennack)
March 2023
https://doi.org/10.1016/j.dam.2023.03.010

Data

DebrisFlow.txt[1][2] is one of the datasets provided in the paper which is used in this implementation to test model outputs.

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Dependencies

This project requires pyomo which you can install using one of the following commands:

$ conda install -c conda-forge pyomo
$ pip install pyomo

References

[1] V. Krasko, S. Rebennack, Two-stage stochastic mixed-integer nonlinear programming model for post-wildfire debris flow hazard management: Mitigation and emergency evacuation, European J. Oper. Res. 263 (1) (2017) 265–282.

[2] K. McCoy, V. Krasko, P. Santi, D. Kaffine, S. Rebennack, Minimizing economic impacts from post-fire debris flows in the western United States, Nat. Hazards 83 (1) (2016) 149–176.