When it comes to OR/DS/QM, you can say I am a wanna-be. The stuff I had learnt (Graph Theory, Optimization, Transportation problems etc.,) are all very pertinent to the field but its all very rusty in my mind given it was a while ago I studied them. And today AI-ML is also included here which I have no clue about. So I plan to brush up on the old stuff I had learnt and learn atleast the simple stuff in AI-ML. Partly why I took up a Big Data Analytics specialization.

1. Books

  1. Introduction to Operations Research, by Hillier and Lieberman: The go-to book management students refer to when they want to get a taste of Operations Research. One tip if you plan to read it: I tried to read it the traditional way - where I start from page 1 and wanted to go till the end. It wasn’t really a good idea. In this book, each chapter is a huge topic in itself. For example, just reading the chapter of Queuing Theory was not pleasurous. I generally used to explore various industries which will incorporate one or more of these concepts in it, and then I would read just the relevant chapter - I started using it as a reference book than a course textbook. For example, one big part of aviation is route scheduling and to get a good understanding of it, I had to get hold of the transportation problems, network optimization - so I read these chapters, to understand Passenger Flows in Airport Operations, knowing queuing theory was necessary, so I ended up reading it. You might be able to read the book in one go, but in case you are not able to, try out this method.
  2. Introduction to Management Science: A modeling and case studies approach with spreadsheets, by Hillier and Hillier: A book written by the Hillier Father-Son duo. Same with the previous book, I did not “finish” this book in one go. Just read the concepts and did the excel problems when I encountered the relevant concept. A great book if you want to focus mainly on modeling problems and solving them on Excel (than get into the crazy mathematics behind it).
  3. Introduction to Queuing Theory, by Robert Cooper
  4. Basic Queuing Theory
  5. Machine Learning - A Probabilistic Perspective, by Kevin P. Murphy: A book my professor suggested for better understanding ML
  6. Grokking Deep Reinforcement Learning, by Miguel Morales: Honestly the only book in this section I have braved to open and understand first few chapters well. Even though I don’t have a mathematical understanding of (D)RL as of now, I have a hunch that it is well-suited for many problems in Supply Chain Management and overall the field of Operations in general. Which is why I want to get my understanding right when it comes to (D)RL. Book available on libgen.
  7. Reinforcement Learning: An Introduction, by Robert S. Sutton and Andrew G. Barto
  8. Regression Analysis: A Practical Introduction