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---
title: "Operations Research and Optimisation: where to start?"
date: 2020-05-26
title: "Operations Research and Optimization: where to start?"
date: 2020-05-27
---
[[https://en.wikipedia.org/wiki/Operations_research][Operations research]] (OR) is a vast area comprising a lot of theory,
different branches of mathematics, and too many applications to
count. In this post, I will try to explain why I find it so
fascinating, but also why it can be a little disconcerting to explore
at first. Then I will try to ease the newcomer's path in this rich
area, by suggesting a very rough "map" of the field and a few
references to get started.
count. In this post, I will try to explain why it can be a little
disconcerting to explore at first, and how to start investigating the
topic with a few references to get started.
Keep in mind that although I studied it during my graduate studies,
this is not my primary area of expertise (I'm a data scientist by
trade), and I definitely don't pretend to know everything in OR. This
is a field too vast for any single person to understand in its
entirety, and I talk mostly from a "amateur mathematician and computer
scientist" standpoint.
entirety, and I talk mostly from an "amateur mathematician and
computer scientist" standpoint.
* Why is it hard to approach?
@ -78,7 +76,7 @@ learn is modelling, i.e. transforming your problem (described in
natural language, often from a particular industrial application) into
a mathematical programme. The mathematical programme is the structure
on which you will be able to apply an algorithm to find an optimal
solution. Even if (like me) you are initially more interested by the
solution. Even if (like me) you are initially more interested in the
algorithmic side of things, learning to create models will shed a lot
of light on the overall process, and will give you more insight in
general on the reasoning behind algorithms.
@ -91,10 +89,10 @@ of problem, so it is very useful as a reference. When you encounter a
concrete problem in real life afterwards, you will know how to
construct an appropriate model, and in the process you will often
identify a common type of problem. The book then gives plenty of
advice on how to best approach each type of problem. Finally, it is
also a great resource to build a "mental map" of the field, avoiding
to get lost in the jungle of linear, stochastic, mixed integer,
quadratic, and other network problems.
advice on how to approach each type of problem. Finally, it is also a
great resource to build a "mental map" of the field, avoiding getting
lost in the jungle of linear, stochastic, mixed integer, quadratic,
and other network problems.
Another interesting resource is the freely available [[https://docs.mosek.com/modeling-cookbook/index.html][MOSEK Modeling
Cookbook]], covering many types of problems, with more mathematical
@ -214,4 +212,12 @@ extraordinary. They also have an accompanying book, the [[https://neos-guide.org
containing many case studies and description of problem types. The
[[https://neos-guide.org/content/optimization-taxonomy][taxonomy]] may be particularly useful.
* Conclusion
Operations research is a fascinating topic, and it has an abundant
literature that makes it very easy to dive into the subject. If you
are interested in algorithms, modelling for practical applications, or
just wish to understand more, I hope to have given you the first steps
to follow, start reading and experimenting.
* References