Optimization with PuLP
PuLP is an linear and mixed integer programming modeler written in Python.
With PuLP, it is simple to create MILP optimisation problems and solve them with the latest open-source (or proprietary) solvers. PuLP can generate MPS or LP files and call solvers such as GLPK, COIN-OR CLP/CBC, CPLEX, GUROBI, MOSEK, XPRESS, CHOCO, MIPCL, HiGHS, SCIP/FSCIP.
Here are some ways to get started using PuLP:
for instructions about installing PuLP see Installing PuLP at Home.
If you’re new to Python and optimisation we recommend that you read Optimisation Concepts, The Optimisation Process, and the Basic Python Coding.
If you want to jump right in then start reading the case studies starting with A Blending Problem.
The full PuLP API documentation is available, and useful functions are also explained in the case studies. The case studies are in order, so the later case studies will assume you have (at least) read the earlier case studies. However, we will provide links to any relevant information you will need.