July 2021

Optimization in Excel vs Python

9 July 2021 (1,530 words)


There are many tools for implementing and solving optimization models. At Solver Max we specialize in using Solver/OpenSolver in Excel. Another popular tool is the Python programming language, using a library such as CVXPY or PuLP.

Each tool has advantages and disadvantages, including:

  • Excel is, by far, the most widely-used analysis tool. It provides a visual environment for building models, with many built-in tools for analyzing and visualizing data. Excel is relatively easy to get started with, so many people are familiar with using Excel for modelling, analysis, and presentation of results. Excel also has great depth – much more than most people realize. While Excel's grid structure makes data easy to see, its rigidity can complicate the handling of data that varies in size. Consequently, Excel models may not be as flexible as those created using a programming language.
  • Python is a popular programming language. Libraries like CVXPY and PuLP handle much of the complexity of the optimization process (like the Solver and OpenSolver add-ins do in Excel). But learning how to program in Python can involve a steep learning curve, which is quite a barrier to entry. Consequently, far fewer people are familiar with programming in Python. Even if the optimization model developer is comfortable with Python, many model users may prefer a more familiar environment for interacting with a model.

To compare Excel and Python, we replicate a Python optimization model described in the blog How to schedule flights in Python.

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