Indeed! It is never simple. Sexy simplistic CP-SAT constraint programming solver versus containers with deep frozen goods or tons of fireworks. Both need to go into a very special place on the ship. Then soft business requirements, keep a weekly schedule. Plus the real business issue: 5-10% of invoices in logistics are incorrect! Mistakes like wrong rate. (disclaimer: the stories I heard while trying to digitise end-to-end value chains. We failed.)

Yes, and that's leaving aside the explainability problem. Let say for arguments sake that you have an optimal planning algorithm, if you cannot explain to the people using it why it generates a certain solution, it will not get adopted.

As an employee at a terminal, you have a) no reason to trust some software people who may have never seen one of them steel boxes up close, b) if this works, you or your colleague gets laid off, and c) if it doesn't work, you're now stuck cleaning up the mess the computer will inevitably make for you, which is way less fun than planning things yourself.

The 5%-10% invoices being incorrect is probably on the low end, I'd probably say it's 5-10% in total invoice VALUE. Because of the sheer amount of containers, you need automated invoice calculation. But if you miss an edge case ("We are allowed to invoice a 5 buck surcharge if the container is filled with at least 100kg of explosive goods, AND was loaded during the night shift because dangerous goods handling requirements now require us to have a safety officer on call, which we normally don't have during the night shift") you are leaving serious amounts of money uninvoiced, and you will never know. One of the reasons not that much effort goes into optimization algorithms is that there is so much low hanging fruit left on getting your invoices out correctly...

>As an employee at a terminal, you have a) no reason to trust some software people who may have never seen one of them steel boxes up close, b) if this works, you or your colleague gets laid off, and c) if it doesn't work, you're now stuck cleaning up the mess the computer will inevitably make for you, which is way less fun than planning things yourself.

Also d.) It works sometimes so you get forced to use it and then it gets ransomwared and you're completely unable to work because they laid off so many people you can't fall back to manual operations.

Nurse scheduling is weird. I worked on a project in this space. Basically the nurses had found their own optimization technique over the hospital’s scheduling methods.

This is so true! Worked in the past on flight optimization problems and the most obvious solution (fly in a direct trajectory, or almost direct but considering wind) would easily achieve a 10% reduction in fuel usage. Now implementing that would be a nightmare, with so many layers involved and the need for realtime comms between aircraft and ground to keep the solutions in sync and conflict free. Unfortunately reality is always more complicated

I work on these sort of industrial optimization problems, and you're absolutely right about the amount of money left on the table. I had one problem at a previous megacorp that I worked on for about 8 months, and then passed off as a two part solution. the first part was a new optimization engine design, but it also required a second part, a new physical process for crossdocking. To this day, 10 years later, my former colleagues still report actual measured savings of $300M/year, which I still find to be mind-boggling.

You're also right about the complications and the need to be adaptable to various situations that arise. Some of that just comes from people who are afraid of getting their hands dirty and really understanding the domain they're working on. Operations Researchers and Industrial Engineers are not much different from Computer Scientists: sometimes the desire for a beautiful and simple solution prevents you from building something useful. Sometimes in order to build something useful, your idealized fast-converging LP turns into a QP/MIP bastardized rat king that only converges on an optimal solution half the time, but still gets a better-than-before solution the rest of the time. And some of us really hate that it has to be that way. The rest of us laugh their way to the bank.