Why should you use Python for operations research?

Excited to echo the sentiments on Python's incredible versatility and user-friendliness! As someone deeply immersed in the tech world, I've seen firsthand how Python stands out as a powerful, general-purpose language that beautifully supports multiple programming paradigms, including object-oriented, functional, and procedural programming. One of the things that continually amazes me about Python is its simplicity in syntax, which significantly lowers the barrier to entry for beginners. This simplicity, combined with a rich library of modules, allows for quick learning, easy reading, and efficient writing of code.

In my experience, Python is great for Operations Research because it's easy to use and has a lot of helpful tools. It includes both paid tools like Gurobi and CPLEX, which are fast and powerful, and free ones like PuLP and Pyomo, which are versatile and cost nothing. This variety means you can pick the right tool for any job. Python's simple language helps make complex tasks more manageable, which is perfect for beginners and experts alike. It comes with many libraries for doing math, handling data, and even making graphs, making it easier to mix operations research with data science for better results.

Strang's lectures teaches you linear algebra and makes you think about column modeling. column modeling is key for complex convoluted polynomial expressions that you ll feed in all your blenders, heat exchangers, crackers, and gazillions of plant models.... python makes that easy, pleasurable, and you start working with vectors of functions/polynomials..next you re doing tensors in no time. I love c++ for other reasons i still use it lots. i love higher modelling languages : other usage.

Python has lot of OR libraries like SCIP, PULP, PYOMO, PYMOO, ORTOOLS, GUROBIPY etc. Object oriented programming is also very easy with python and hence OR applications can be built easily using python.

There are two parts of OR you can do in python. For mathematical model building, Pyomo, Pymoo, Pulp, SCIP, Google OR tools, MIP are some of the libraries that are widely used. For simulation purposes, Simpy, ModSimpy is available. But note that, installing OR libraries does not give you the guarantee of a solver. In some cases, you have to call a solver of your choice, in some cases, some solvers are built automatically. That part needs special care.

Please add InsideOpt Seeker to the list! While implemented in C++ (for speed), the solver has a very expressive Python library that make it extremely easy to model optimization problems. #insideopt.com

A variety of commercial mathematical programming solvers have their own Python packages as well. Examples are Gurobi, CPLEX, and Xpress.

I've been reflecting on the transformative power of Python in the realm of operations research (OR) and how it's become an indispensable part of our toolkit. Python's wide array of libraries and packages empowers us to tackle the complexity of modeling, simulating, and solving various OR problems with remarkable efficiency and clarity. For instance, when it comes to defining and solving optimization problems, libraries like PuLP, Pyomo, and CVXPY offer incredible versatility. They allow us to work with linear, integer, and nonlinear optimization problems and interface seamlessly with powerful solvers like CPLEX, Gurobi, and GLPK.

As our journey with Python in the realm of Operations Research (OR) continues to evolve, I'm thrilled to share a curated list of resources for those keen on deepening their understanding or even starting their adventure in this fascinating intersection of technology and decision science. Books to Ground Your Knowledge: Python for Operations Research by Winston and Venkataramanan: An essential read that bridges Python programming with OR techniques. Python for Data Analysis by Wes McKinney: Offers insights into handling, analyzing, and visualizing data in Python - a must for data-driven OR.

Thank you LinkedIn AI generated articles! Finally, a topic we can get behind! Yes, Python is a great language to use for operations research. The syntax of Python is relatively easy, which gives you more time to focus on your models. Additionally, there are lots of packages to make your OR tasks easier. There are both open source and commercial packages for optimization. There is networkx for network analysis. There is SimPy for simulation work. Whatever OR work you need to do, you can probably do it in Python.