Still, it _is_ a good story, and plausible based on what I saw to be the state of the industry back then. Your run of the mill last-mile courier services were really badly organized, from the mathematics and optimization side as simple as they get, and ability to build a robust optimization transportation management system would've given serious competitive edge.

(edit: removed repeated words)

Similarly to you, I spent a short while trying to sell VRP optimization with an API business model, and what dawned on me was that most companies do not have the necessary in-house expertise to integrate optimization into their existing tools even if the API is well-designed. There also really seems not to be any urgency to do that and most logistics companies just offload their inefficiencies onto their customers. Your routes are not effective? No problem, just bill more.

Some years ago I heard about a Swedish team of optimization experts who got so fed up with selling optimization to unwilling transportation companies that they founded their own—just to mop the floor with their ineffective competition. :D

I agree that ease of use is key here. In my PhD dissertation, I tried to address the issue by adding self-adaptivity within transportation management systems, mostly through automatic parameter tuning and algorithm selection. Such approaches remove some amount of fiddling when the optimization tool is adapted to a new optimization problem. Worth a look, perhaps, if you're interested.

Many thanks again for the interesting article and all the best with Timefold.

1) E.g., already by the '90s, we had quite capable algorithms for the VRP. I have open-sourced a library of classical VRP algorithms called VeRyPy, containing simple and not-so-simple heuristic algorithms. It has enjoyed modest success among VRP researchers and practitioners. Nowhere near the success of OptaPlanner, but also, the purpose is different—OptaPlanner is production-ready, whereas VeRyPy is more geared towards education and research purposes.

Edit: IIRC the shader a Short Hike used was discussed in the GDC talk by the game's author: https://www.youtube.com/watch?v=ZW8gWgpptI8

In my recent PhD dissertation I tried to address the challenges from a different angle: I proposed using machine learning to predict the most suitable heuristic algorithm and its parameter values for a specific logistics planning problem. This way the developer or the user does not need to worry about the details of the optimization solver. The book is freely available for download from: https://jyx.jyu.fi/handle/123456789/65790

In a dystopia version megacorporations own and control the digital assistant and can decide what information it feeds to you. It will become the marketer you trust, which has a significant influence on the decisions you do.

Nobody likes the scruffy operations researcher who awkwardly tells decision makers what to do in the form of an optimal solution, because this takes the power away from them. However, if one presents several good, preferably nearly optimal but different to each other, solutions between which the decision can be made freely, you have a better chance to make a change to better.

There is an Excel sheet for solving such problems, and if one wishes to go further, the basic heuristic and metaheuristic algorithms are not that hard to implement either. http://people.bath.ac.uk/ge277/index.php/vrp-spreadsheet-sol...

With these keywords you are welcome to fall into this rabbit hole as deep as you wish. I promise to greet you on my way up (currently finishing my PhD on the topic).