In this case study, Tolgahan Tarkan from Dijitalis presents how he optimized AGV fleet investments in large production system and saved more than 1.5 million USD  in capex using Simio simulation software. The presention was broadcasted at the Simio Sync 2025 simulation modeling summit.

The consultant shows details of how he built the model quickly using auto-create feature of Simio. The simulation model is highly flexible since most of the model is created using data-driven methodology therefore enabling any data in the data tables to be imported from Excel such as production schedule, product routings, bill of materials (BOM) so on.

The simulation model also acts as production schedule verification tool in terms of AGV/AMR timeliness. Dijitalis consultant also shows two heat maps, one for aisle congestion and second for aisle traffic flow rate all using Simio simulation software. The Gannt charts in Simio also provides information about delay causes in the initial system. Therefore it is possible to do root cause analysis with the Gannt charts of Simio software.

The consultant shows details of how he built the model quickly using auto-create feature of Simio. The simulation model is highly flexible since most of the model is created using data-driven methodology therefore enabling any data in the data tables to be imported from Excel such as production schedule, product routings, bill of materials (BOM) so on.

The simulation model also acts as production schedule verification tool in terms of AGV/AMR timeliness. Dijitalis consultant also shows two heat maps, one for aisle congestion and second for aisle traffic flow rate all using Simio simulation software. The Gannt charts in Simio also provides information about delay causes in the initial system. Therefore it is possible to do root cause analysis with the Gannt charts of Simio software.

Using PuLP this Python template solves a capacity planning problem using linear programming. 

Linear optimization in Python for oil pump capacity planning

In the exemplary case study solved by this Python model a new oil field is prepared. For this, oil pumps must be purchased. Different pump types have different production capacities and purchasing prices.

The problem is to minimize purchasing expenses while at least securing required production volumes.

Another constraint is the available surface area of the oil field. Above figure displays an exemplary oil field in Texas.

KPIs traced by the linear capacity planning Python model

The optimization model considers the following KPIs:

• Purchasing expenses
• Production output
• Surface area occupied on the oil field

Who will benefit from this linear programming template?

Linear programming is a fundamental mathematical programming technique, applied not only in capacity planning. For example, logistics networks can be optimized with linear programming as well. It is also used for e.g. marketing campaign planning, pricing, and much more.

Hence, getting familiar with linear programming and owning a Python template for implementing a linear optimization program in Python, can be beneficial to a wide group of users. This includes students, analysts, and managers – in manufacturing, logistics, purchasing, accounting, controlling, and marketing.

More about linear optimization in Python and other programming languages

If you are interested in linear programming, capacity planning, and mathematical optimization, here are some related articles that might help in getting you started:

• Optimization via master production scheduling
• Price and inventory optimization
• Integer programming
• Linear programming in Julia
• Linear program with Google ortools in Python