Comment by andrew771 | Hacker News Reader

Comment by andrew771 | Hacker News Reader

Hi all, author here. I'm very humbled that this made it to the front page! A few things I want to say:

1) Most of this work is a simplified copy of the papers I linked to. Special thanks to James Bookbinder and his team at University of Waterloo.

2) I'm an OR novice, and this is my first optimization project. I feel now that I can roughly model the flight assignment domain, but what the solvers do is a mystery to me, and when I read about Lagrangian relaxation and column generation, I'm lost. Fortunately I haven't needed those techniques in this project.

3) The mathematical model, when written down, looks mystifying even to me, the author, and that's unfortunate. My goal in writing this post was to reduce the mystery and explain the model in plain English (plus math notation), but in the end I'm afraid it still looks eye-glazingly complicated. Data science can be all too happy to cloak itself in mystery by writing down what are actually pretty basic equations. I don't have a good solution to this.

Most of all, I got such joy out of building the model one constraint at a time and seeing the solver follow my directions and spit out optimal solutions. I couldn't believe it worked. It was like I discovered electricity. Lastly, much credit goes to Flexport leadership for allowing me, an OR novice, to embark on this optimization project.

Heya! Very nice and congratulations on the front page :)

On (2) that's the great part about solvers, is that they're essentially quite incredible black boxes. Most people who actually do optimization theory also don't really know how they work. (My money is on black magic for a number of cases.) Kidding aside, writing one is always informative and interesting (and, with languages like Julia, surprisingly not complicated).

3) My suspicion is that matrix notation would actually improve the end result quite a bit. A lot of the constraints you've written down have very common structure (e.g., the total weight constraint can be written as y = X'g, where m is the vector of weights, or the required assignment constraint, X1 = 1, where 1 is the all-ones vector).

Writing this out and the corresponding descriptions next to it would make it much easier to parse. E.g., for the above cases

  min tr(CX) + c'y
  s.t. X1 = 1    (all items need to be shipped)
       X'g = y   (y is the total weight on each ULD)
       etc.

Congrats again on the front page and the article!

> Most of this work is a simplified copy of the papers I linked to

To me, those are probably the most interesting category of blog posts. Some interesting technical content that isn't accessible to a layperson in the literature.

> My goal in writing this post was to reduce the mystery and explain the model in plain English (plus math notation), but in the end I'm afraid it still looks eye-glazingly complicated.

In places maybe, but not overall. You'll find that as you learn, the more deeply you understand the subject, the more simply you'll be able to explain it. That's not true the other way round - not everyone with deep understanding of a subject can explain things well!

Congrats on the post. Enjoyed it.

I've watched with interest as INFORMS has tried to rebrand OR as data science, but this is the first time I've seen anyone refer to it as such in the wild.

I hope you're able and encouraged to develop your interest in this area. It's actually all over the place (I'm currently working in rail), usually cloaked by a less sexy job title :)

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