The Pooling Problem is a challenging problem in the petrochemical refining, wastewater treatment and mining industries. This problem can be regarded as a generalization of the minimum-cost flow problem and the blending problem. It is indeed important because of the significant savings it can generate, so it comes as no surprise that it has been studied extensively since Haverly pointed out the non-linear structure of this problem in 1978.
This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.
Two alternative formulations based on Bilinear Programming, a subclass of non-convex Quadratic Programming problems, are presented, namely:
- P-formulation (concentration).
- Q-formulation (proportion).
- Deployment of Bilinear Programs.
- Optimization based on Spatial Branch and Bound (sB&B).
- Benchmark run on an instance of the Standard Pooling Problem to compare the performance of the formulations mentioned above.
It is shown that solving Bilinear Programs with Gurobi is as easy as configuring a single global parameter. The dramatic difference in performance of alternative formulations is also highlighted.
In order to run this Jupyter Notebook properly, you must have a Gurobi license. If you do not have one, you can request an evaluation license as a commercial user, or download a free license as an academic user.
https://gurobi.github.io/modeling-examples/pooling/std_pooling.html
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