yorak

Sorry to dissapoint you and the few other curious ones, this was some 10 years ago and such details such as name of the company have fell of from my overfilled brain long ago. While the person who told me the story is a reputable fellow I must admit they still are a secondhand source. Being a finn I tend to trust people and take their word on it and, hence, do not recall doing my research to factcheck.

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)

Geoffrey, thanks for sharing your story with us. OR sure is a weird niche. Good enough algorithms for solving these problems have existed for decades1, but we still see low adoption and your 95% estimate of the companies not optimizing their operations rings true.

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.

At least to me the argument does not hold water. My fear is that humans being human, simulation of a coral reef imaginary or future holodeck-like experiences will not save the actual coral reefs. They might even hasten their demise (as less people are interested of the "ugly" reality of the reefs). The tech is cool, but the environment angle, at least here, feels as an afterthought.

I agree and have been saying for a while that an AI you control and run (be it on your own hardware or on a rented one) will be the Linux of this generation. There is no other way to retain the freedom of information processing.

I blame being an operations researcher for always (pessimistically) first and foremost seeing how the system can be gamed. You have to think very very carefully what the objective function is and which kind of undesired solutions need to be forbidden using the constraints.

Yes, rather than exchange rate, I'd be more worried about corvids carrying trash from the landfill to exchange it to food. Or, in case of cigarette stubs, emptying well contained ashtrays to the tables and floors only to get to those valuable stubs.

Aka. the Chinese room argument. However, I'm not so sure us people are little more than just pattern matching machines. When I start to talk (or write, as I'm doing now), the words kind of just flow out. I can make the argument, that I understand the "real" world, but do I really?

What is most impressive here, which I think other commentators of the thread have not pointed out, is its ability to have an inner dialogue (monologue?) with itself in this sample. For me, that property of the generated text (or should I write, thought process) gave the chills. Now, given this, AGI seems to be quite a few steps closer indeed.

I've personally not seen this happen. Instead, I've seen many oversold ML/AI tools that offer no advantages over using open and freely available tooling that the data scientist (with or without PhD) is already familiar with. I know this must be frustrating to ML solution vendors, but as with any product, the value proposition has to be there and easy to see for the domain expert. And the value has to be great because the downside is the vendor lock-in of the proprietary solution. Hence, ask yourself: given a green field real world ML project, would you use your tool (with the the same learning algorithms, data manipulation methods etc. under the surface as everybody else), or resort to using some battle proven free and open stack.

From how I see it the conflict is not technical but ideological. Or, in technical terms, what is the objective?: for Forrester and Collins this seemed to be increasing the economical growth of the city and managing KPI (crime, poverty etc.). Baker on the other hand considers also human dignity and our responsibility of keeping a fellow human fed, under a roof, and safe and does not ignore the externalities of the decisions.

In an utopia scenario you own the digital assistant. It is based on open source (even if deployed to cloud ) and you carefully curate the data sources is has access to.

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.

There is this trick among us operations researchers I'd like to share: even if one is able to perfectly model the peculiarities of a given application and get the optimal solution, it should not be the only output. Instead, one should put forward several good alternatives which the stakeholders can then discuss and become proponents for.

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.