This downloadable virtual product contains a case study applying integer programming to oil well pump capacity planning. The case study demonstrates integer capacity optimization in Python using PuLP.
In detail, this product includes:
- PDF file with a presentation of the case study.
- Python model with integer problem implementation using PuLP.
Who will benefit from integer capacity optimization in Python?
This downloadable product suits supply chain analysts, operations researchers, transport planners, and students that want to:
- Get a hands-on example on how integer programming can be applied.
- Obtain an integer programming optimization template in Python.
- Learn PuLP for optimization in Python.
Brief case study description
A new oil field is prepared for production. Oil pumps are to be installed for crude oil production. There are various types of oil pumps available on the market. They differ in price, surface area requirement, and production capacity.
The problem: Minimize oil pump purchase spending but ensure the required production target. Also, oil field surface area available for pump installation is another constraint that must be considered.
Below is an exemplary satellite image of an oil well field in Texas, USA.
Relevant KPIs traced and outputted by this model
The model contained by this product tracks relevant KPIs.
The following KPIs supported by the optimization model:
- Purchase spending on oil pumps.
- Production output in barrels of oil.
- Surface area occupied for oil pump installations.
Learn more about integer programming
You can find several other contributions on SCDA related to integer programming, capacity planning, and linear programming. Examples cover applications in Python, R, Excel, Julia, and Java.
Here are some exemplary articles for you to read: