11

Excel remains extensively used in industry for non-OR applications. That means that if you are doing an OR application that does not require access to a database, there's a good chance the data for the application will come to you in either an XLSX or CSV file. On the flip side, when it comes time to convey the solution provided by your application, it is ...


9

For an idea of industrial applications of OR write large (minus the gory technical details), you might want to look at a few INFORMS publications. Analytics (aimed at the general public) and OR/MS Today (for INFORMS membership) contain articles about implementations. The INFORMS Journal of Applied Analytics (formerly known as Interfaces) contains primarily ...


9

You can consider going through below blogs: i) Erwin Kalvelagen's blog ii) Prof Paul Rubin's blog (https://orinanobworld.blogspot.com/) Interesting things about above blogs is that you can see different applications, their implementations and practical issues while handling them. Another good course is by Prof. Pascal Van on Coursera (https://www.coursera....


6

Many many people know Excel and use Excel. So many OR projects start with some Excel spreadsheet. And that is why being able to read from and write to Excel is key. You may even start the project with the Excel solver. Moreover Excel is a common tool when companies choose plugin optimization instead of packages, custom or tailored optimization.


6

I found it quite useful to start (social scientist with background in stats). I had mostly read more complicated papers using linear programming in geographical allocation contexts, and was totally baffled by it. (Relative to stats, in retrospect people using X/Y as decision variables and Greek letters as fixed values is where quite a bit of my confusion ...


2

Having previously worked in software engineering, then trained in mathematics (stats & OR), and since worked in OR for ~3 years, I have a strong opinion on this. If you want to work in OR then it is expected that you are familiar with the topics in the book including some theory behind LP, MIP, and so on. In practice, the real difficulty in tackling a ...


2

Hillier and Lieberman is a good read and solid reference to get the "big picture" of many techniques. You may also want to look into Operations Research: Applications and Algorithms by Winston.


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