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This semester I will start teaching Programming in Python to Master students in Supply Chain Management.
I would like to start the first lesson with "Why learning programming languages will be useful to them".
But when I search about this term in the internet using search engines like Google, Bing, Yahoo, DuckDuckGo, I do not get good answers.

I tried the following terms

• "industrial engineering" "programming languages"


• "operations research" "programming languages"

Interestingly, I do not see useful scientific articles or news. Only one related result is Chapter 2 Programming languages in Handbooks in Operations Research and Management Science.
Apart from this old chapter from 1992, other results, for me at least, are not useful.
Then come my questions.

1. Are programming languages is necessary for operations research practitioner?


2. If programming languages are not necessary, then are they at least useful for operations research practitioner?

If you want to go behind prototyping a model, you will need a programming language.
Here is a non-exhaustive list :

1. Sometimes simply modeling and calling the solver won't suffice to handle complex problems. To develop custom algorithms, heuristics, etc you will need to learn a programming language. Python is a good choice for fast prototyping and C/C++ for developing fast algorithms.




2. If you are dealing with end-users, and you want to build useful end-to-end products you will need to build GUI, have some knowledge of databases, etc. Here I would say that even some web programming/scripting languages (e.g. Javascript) are useful since a lot of the applications are web-based and host on the cloud.




3. If you want to mix OR methods with data analytics/machine learning etc. Python is a good way to go.




4. If you are working with a team of software engineers/data scientists etc who are developing end-to-end apps or integrating OR methods with data analytics etc. Those people don't know about specific modeling languages.

Finally, learn how to program and write elegant code is very crucial for a lot of OR projects. I think one needs also to learn the best practices such as version control, documentation, unit testing, etc.

In my opinion, programming languages are necessary as an OR practitioner. There are a number of reasons for this:

• Data Input/Output often requires some programming language. Whether it’s reading in data from an excel spreadsheet, or an SQL database or what have you, you will want to do this programmatically using Python etc.


• Many companies do not have GAMS, CPLEX Optimization Studio or similar, so a practitioner is basically forced to use a general programming language.


• When working interdisciplinary (eg with machine learning), a general language is much easier to interface than a special modeling language


• In my opinion, general programming languages offer a lot of advantages (test ability, continuous integration etc) that make deployment much, much easier.

By the way, I’d be curious to hear about how your course goes. Any way you could make the material public?

Some optimization software/frameworks (commercial or open-source) such as AMPL, GAMS, Cplex, ... have a specific Algebraic modelling language. Some of them have another type of programming that uses APIs to connect with general programming languages ​​like C ++ / Java or others.

AFAIK, Algebraic modelling language is a bit easier than general programming to write optimization models. Indeed, you would also see this or this posts.

Agree with all the mentioned great answers, I also think general programming languages are necessary for the OR practitioners and would like to add the following points:

• 
  

  Applications: Beside the theoretical advancement of OR techniques and concepts in academia, there is another parallel and strong branch of OR, namely application of the OR in real-world problems. Those problems are usually complicated and managing all the necessary data and details needs lots of variables and constraints which are interconnected by different logics among them. Although some of the algebraic languages are able to deal with these type of constraints, most of the time it is easier to use general programming languages to tackle big problems. Moreover, sometimes you need to compare and validate your results with the outputs of specific software in the field of application. Those software usually need predefined format for the inputs and even you need the output of software as your model's input. These connections can be easily made by using a general programming language. As an example:

  
  
  
  • AMPL $longleftrightarrow$ Matlab $longleftrightarrow$ Abaqus(as software for structural analysis).
  
  



• Theory: Looking at the huge literature of the proposed metaheuristic approaches, one can easily see the necessity of those approaches. AFAIK, using algebraic languages to code those algorithms are nearly impossible or tediously sophisticated.