931: Boost Your Profits with Mathematical Optimization, feat. Jerry Yurchisin

30:36But even if it's like this, we could still tell you, oh, you're no more than 5 % off of optimal, no more than 2%, no more than whatever. That in itself is super, super valuable, I think, just to know how far off you can be. And again, that's not something that any other approach can say. You can sort of, even if you use other optimization algorithms, like something like simulated annealing is an optimization thing, is an optimization sort of process. But that does not have this gap, does not have this guarantee. T when it converges, it's just sort of, it's one of those processes that, that, okay, my, my solution hasn't changed by this amount, you know, over my last like thousand iterations.

31:29So I'm, I'm going to call it quits. We're good. That is something that, that we sort of call that like a local optimization. You're sort of getting into, if you're minimizing a function, you sort of, you might get into a little bit of a valley, but there could be a better valley right over there. That's, that's lower for your costs, but you can get stuck in those things. And that's what those types of algorithms, while super useful and beneficial, I'm not like saying that they're bad or anything. They just say, OK, yeah, I got something that looks like it's a good solution, but I can't prove it.

32:01What snap-down optimization can do is also take into consideration those other valleys and go from there and say, OK, this is the best valley.

32:10Jon Krohn:Now, it's interesting that you talk about peaks and valleys there in terms of optimization. Is another word for that topology, Jared? You can call it that. Yeah. Because there are, yeah, there are essentially the, if you sort of think of it as, you know, sort of describing these constraints, it sets up what we call a feasible region. And if you sort of think of it like, like in a, in a linear sense, it's kind of like a box, you know, it could be a square, but then you sort of start slicing that box in a bunch of different ways around the outside by constraints. then you get this like sort of complex shape.

32:48So it is like there is a lot of geometry that is important to mathematical optimization. That's actually precisely why we can do the why we can make the guarantees that we have is because of the geometry of the problem. And the math that goes along with that allows us to say, OK, because of all these proofs and theorems and all of these sort of all this mathematical theory, if this happens then we know that this is the optimal solution very cool and the reason why i bring up topology uh you already know why jerry but we had uh on social media at the time of

33:24Jon Krohn:recording yesterday um so about a month before people hear this a long time listener to the podcast and longtime social media follower a guy named roland phillips based actually i guess not too far away from you in Virginia, a place called Roanoke. Okay. Yeah. That's a little, yeah, not super close, but pretty close. But yeah. So Roland Phillips out of Roanoke, he is a chartered financial consultant and he has a degree in mathematics. He does a lot of work in analytics and automating business processes. And he commented on a recent, So we had an episode, episode 923, which I guess is now about a month ago relative to when people are hearing this episode.

34:09Jon Krohn:It was with Amy Hodler on graphs, on graph networks. And I guess she talked about topologies in something related to graphs. And yeah, Roland wrote that he really liked that episode and that he found it interesting that topology, that kind of word is being mentioned more and more. He says, I think the first mention with Jerry was episode 813. So one of the episodes that I mentioned of yours earlier in the episode, he says that you talked about topology. That was your previous appearance on the show. And yeah, it's interesting, I guess. I don't know. I'm just trying to, I'm tying a bunch of threads here together.

34:49I think it's a good point because people are beginning to, I think there's a little bit more like, hey, we need to take mathematics seriously in this. And that there is room for, we talk about like graph structures and stuff like that. That's something that is also prevalent in mathematical optimization when you're solving these really complex sort of problems where you're trying to understand these yes, no, on, off decisions, or you need to have your decision variables be actually the sort of discrete integer values. There's a lot of that type of stuff that happens as well. Um, and, and, uh, so yeah, it just sort of shows that, um, uh, understanding the mathematics behind things is, is never a bad idea.

35:44You know, it's, it's, it's, uh, sometimes it's hard. Sometimes it's maybe not quite as useful, but, um, but it doesn't hurt. And, and, and a lot of all, uh, pretty much all of these like cool sort of breakthrough things that we have um you know with with deep learning you know like 15 years ago or something like that and now with with um lms and everything there's there's there's a mathematical process that's behind it you don't really need to know everything about it but um but but it's there and and uh if you think about like the history of all this sort of stuff like the history of like a neural network that was in the 40s was when that kind of stuff was being the 50s i think yeah Yeah.

36:28Oh, maybe the forties. Yeah. Around that time.

36:31Jon Krohn:Exactly. I think about it. Yeah. Yeah. Yeah. But that's when most a century. Yeah. That's when the math behind that was being sort of invented. And, and the same thing is true with, with the optimization. That was actually around the same time where the sort of like the breakthrough algorithms for mathematical optimization, um, one being the, uh, called the, the, the simplex algorithm, um, which again, if you sort of look into that a little bit, you'll sort of see the, why geometry is important in that algorithm. But yeah, that stuff was developed a long, long time ago. It just, it took, particularly for something like deep learning, it took the massive amounts of data to make it useful and the massive amounts of computing power to actually sort of have that be a breakthrough.

37:16So yeah, it's interesting what may happen next with stuff that has been sort of long cast aside is like, eh, whatever, that's, I can't do that. But then, you know, with new technological breakthroughs, who knows?

37:28Jon Krohn:So, and speaking of neural networks, deep learning, new technological breakthroughs, something that is completely new at Girobi, as far as I'm aware, certainly we haven't talked about in any way in your preceding episodes, really, is use of LLMs of large language models. And so, for example, I believe that you may now have or you or Girobi will soon have a custom GPT in the chat GPT store. So right now we have actually technically three custom GPTs in the chat GPT store. One of them is called Growbot, which is now called Legacy Growbot. And I'll talk about what the new Growbot is in a little bit.

38:11Jon Krohn:That name will never go out of fashion. Yeah. The new Growbot, that's set. Yeah. We actually had a company vote about what we should call the new... Because we had old robot, we had a company vote about what we should call the new one. We had other names and stuff like that. And just new robot one by like, hands down. But the other things that you can find on the chat GPT store, what we call the Groby AI modeling prompt engineer and modeling assistant. And I highly recommend if you want to just sort of, you know, not take my word for all this and everything. And so it's like, OK, how can mathematical optimization work for my specific problem?

38:55Just go to that, fire up that custom GPT. And it's meant to be a very conversational sort of way of understanding if mathematical optimization is the right approach for your problem. And then if it is, let's have a little conversation back and forth to really develop the problem statement. Because that is the core. That's like the most important thing with a mathematical optimization problem in practice is really understanding your problem. And really understanding, again, what are all the levers that I should be able to have access to pulling and pushing buttons? What are all my possible things that I actually have control of?

39:39and one of the things that has, that leads to failure in optimization problems is, oh, I didn't consider this. So, you know, and the solution gave me something. The optimal solution is something that doesn't look like what I was expecting. And it's because I didn't give it all the information. I didn't model it properly. And that's what this prompt engineer is really there to help you do is really understand your problem and come up with like a very sort of concrete and thorough problem statement. So you should clearly, by the end of that, you should clearly know what your objective is. I want to minimize my costs or I want to minimize customer churn or something like that.

40:18And here are my sets of constraints and here are the decisions, decision variables, decisions that I can make to do that. That's what that's for. And then we have the modeling assistant, which is made to take that problem statement and then actually give you a sort of first stab at the mathematical formulation. So again, the process for all of these for mathematical optimization is take your business problem, understand it, transform it into math, which is essentially sets of linear or now nonlinear, but sets of equations and inequalities and things like that. Transform it mathematically and then transform that into code.

41:00That's what the modeling assistant is supposed to do is the last two of those. It gives you a first sort of stab at the formulation, the math part, and then also the code in Garobi Pi. And if the problem is small enough, it'll actually run it and solve it. We've attached essentially a Python wheel that has that limited solver capability attached to it. So you can actually sort of see it in action. So that's a great place to start. really understanding how optimization can be used, particularly in the very, in the context you're most interested in, because you can hear me talk about supply chain or logistics or, or whatever it may be, these sort of traditional fields where optimization is used sort of day in and day out by these massive companies.

41:48But also other things like, okay, well, I want to know about like, marketing mix optimization, I'm in a marketing department, how can I best spend my budget to maximize sort of clicks or something like that, boom, we can talk about that. Or whatever you're trying to think about your problem is, it's a great way to go about it. And all that's in the chat GPT store. And now we're sort of doing our own stuff as well with the new Garobot.

42:15Jon Krohn:Excellent. So the idea with Garobot, whether it's in the chat GPT store or outside of it, is that we're using large language models to make it easier to do mathematical optimization. So we're taking, you know, our business problem constraints that we have, maybe expressing that in natural language and getting a headstart on all the mathematical definitions that are essential to getting our optimization solver running. Yeah. Yeah. And, and particularly with, with, um, with the chat GPT store stuff and, and with, with the, the new robot, which is something that we host um uh it's you know um um essentially built with the aws with a clod running in the background um more or less and um so we we have that in between you know either of those yeah you sort of start with your sort of vague business problem and it helps you develop it and then yeah it gives you i mean it's obviously i don't think i need to preach to the choir here about like the how how you need to take the output of LLMs with, with the grain of salt and the caution and all that sort of stuff.

43:21You know, we all, we all know that, that, that not everything is perfect. That comes out, but particularly for, for this audience who may be learning optimization and, and stuff like that, it's really, really dang good. And it will give you really good responses, really correct modeling, model representations of your business problem, good code right out of the gate. And that's where we're moving towards as a company is let's build these tools. And GrowBot is being the first of that to really help sort of lower the barrier of entry, to speed up the process of defining your model, of doing the modeling, doing the code writing, and stuff like that, really speeding that up.

44:15And where Girobot sits now is it's best if you're a little bit of a, I don't want to say an expert, but if you have some experience with our API and our stuff like that, because it's really calling on our material, Our internal material, external material, all that sort of stuff, that's what's being used on the back end. And it really provides a great way to do things in a, to do all the modeling, to do all the coding and to do things like that in a way that is sort of just going to speed things up significantly. And we're really excited about where we're going to be going in the future. And there's going to be a lot more that we're going to hopefully put out there.

45:05Jon Krohn:Nice, really cool application, integrating LLMs essentially into the workflow of doing a mathematical optimization problem, making things easier for people in the flow of work, which is one of those great AI use cases. It's nice to see it there for you folks at Garobi. Something else that you have for us, I think that's completely new since your previous appearances on the show, are some interesting new real-life use cases of mathematical optimization. So you have, of course, alluded to some of them. We've talked about the burrito optimization game as a toy example or the new Garobine coffee example.

45:46Jon Krohn:you've mentioned that application areas like supply chain, logistics, those tend to be areas that use mathematical optimization a fair bit. But I'd love to dig into a few more cool real life use cases that have cropped up in recent months. Yeah. So we recently had what we call the Groby Decision Intelligence Summit. It's our fancy sort of event that we put on ourselves. We invite customers we invite prospects we invite anyone who's interested in in learning more about optimization you know we invite you to to come last year it just finished up a couple weeks ago um we were in vegas um and so we're bringing in like you know um uh super cool customers that are doing really cool things and we had a couple talks that that i thought were i like how you had to pause there because you're like, it could like company names go into your head and then you can't say them.

46:47So we get a, some pause, really cool companies. Um, but I will mention a couple, there's a couple that I can mention. There's some that I can't, um, sadly, um, again, we're, we're the best kept secret in decision-making. I guess that's what, if you're going to come away with anything, optimization and Garobi is the best kept secret because people don't like to talk about us because yeah. Why would you spill the beans? But there are two, two presentations that I really liked. One was from Toyota and they're, they're talking about how they used optimization for, for planning of vehicle manufacturing.

47:27So they're getting, you know, sort of you know, demand forecasts of like, okay, this, this is the, the number of this type of vehicle that I expect to, you know, to that customers would want in this region at this time. So you can sort of see if you're thinking about by region over a certain amount of time, the whole sort of fleet of Toyota vehicles that they offer, you know, that's a pretty big problem. And now you're thinking about like, okay, manufacturing that, how can I best manufacture these things, these cars at minimal costs and everything, you sort of see all of the small things that trickle into making a car.

48:14It's a very complex process. But so they ran through how they're sort of building tools. And there is an aspect of LLMs and natural language in this as well. But they allowed their planners to sort of interact with an optimization model that, you know, an optimization team built this optimization model, but they allowed sort of their planners to interact with that and do scenario tests. And and what if analysis on all of these sort of things? I'm like, well, what if what if the tariffs on this particular thing, you know, what if tariffs go up by from from zero percent to 10 percent? And then next week, they're 80 percent.

49:00And then the week after that, they're back down to 10. And sometimes they're 30 percent. You know, this is a it's an insane time to try and plan long term manufacturing right now. It's like insane with all this sort of fluctuation of particularly tariffs. But they had a tool that was at optimization in the back, had sort of an LLM sort of interface where the planners can really interact with this and say, OK, well, what if tariffs are this or what if, you know, my supply of this this thing was cut in half or something? It's like interacting with the optimization model in a very natural way and getting all these sort of cool scenarios and really being able to understand, okay, what if this happens?

49:45What should I be doing? How should I be manufacturing things at sort of like at some macro level and really making decisions that will impact the company? and it's just providing like a whole new way to access optimization to people who don't, they're not going to be writing the models, they're not going to be doing any of the Python coding, but these are the people who are making the decisions who have all that, have all this sort of SME expertise, all this business expertise, all this foundational knowledge of like, I actually know how to plan, you know, manufacturing for cars and stuff like that.

50:25I know all of this. I don't know optimization, but now we're now like this, this, this, you know, this group at Toyota, they did an exceptional job of sort of blending the two and letting people interact with that. So that was one super cool case. And the other one is with Total Wine, the other ones that I can mention is Total Wine. And what I it's again, a similar problem of like, how can I, it's a similar is problem because it's kind of supply chainy, but it's, you know, essentially, if you think about what a total wine store is, it's a massive store that has all the beer and wine that you could ever want.

51:03Like anything you're interested in finding and depending on state laws, there's maybe like, uh, like liquors and stuff like that too.

51:10Jon Krohn:Um, and here I thought it was a platform for getting complete complaints. I love it. But, uh, they, uh, what I really liked about their story is um sort of the think about like the the complexity of decision making um that can happen within within something that it's like okay well i buy a bunch of beer i buy a bunch of wine okay but you're sort of thinking about about again about complexities um and in the presentation the the presenter is talking about like okay i want to buy just like one brand of beer or something the the the choices that you have in just that single sort of brand is pretty massive like am i buying massive cases am i buying individual six packs how many am i buying you know um you know 20 you know uh cases of 24 cases of 18 you know all that sort of stuff when when am i getting them how often are they coming how often are they arriving um and everything like that and And now you think about that for pretty much every beer that exists in particularly in like North America or are you importing them?

52:16Every wine is sort of you sort of it's a massive, massive problem and not easy to solve. But what I really liked about about this problem is, you know, the Toyota folks that I just mentioned and a lot of our sort of customers, they are they have what we call operations research sort of expertise sort of in-house. Um, even the Toyota example, the person who presented it was not, you know, did not have the traditional background of our customer of our, of our common customer. Um, he is, uh, uh, an AI person, um, but, um, had some mathematical chops to him. And so it was like, not super, uh, he took to it a little bit faster than I think some would, but, um, but total wine folks, they were a team of data scientists.

53:03They were people who did not have a traditional sort of operations research background, industrial engineering. Those are some of the common degree types that people have who have been exposed to linear programming, mixed integer programming, the mathematical optimization things. That's where you typically learn that. These were people who were, you know, I'm a data scientist, been doing that for a decade now. Oh, we have this new problem type that we're trying to solve. machine learning is not cutting it. What else can we do? Oh, okay. I've learned of mathematical optimization. Now we need to actually do it.

53:39And so it was a total success story of, of taking a team of people who did not really know how to do this right away. Understanding their learning, their pain points and stuff like that, understanding what worked for them and what didn't. It was just a great story to hear that, that this stuff, you know, that if you're, if you're listening to this now and you're like, oh, well, you know, I'm, you know, I don't have time to listen. I don't have time to learn all of this or, or I don't know the benefits of should I just hire or something like that? You know, that's also complicated. It could be time consuming and blah, blah, blah.

54:14You can be done in house. You can build a team that can take care of this, that can do this at the scale. And, and I think this is where a company like Garobi, this is why I love working for the company I work for, is we don't just like hand you the software and say, good luck, have fun. You know, as long as your check clears, you know, blah, blah, blah, you know, we're not going to talk with you. We have an exceptional sort of support team that helps you with this. So if you get stuck, you know, not stuck with like, hey, I don't know how to build my model stuff but like hey this is taking a lot longer than i thought to run or or we're getting like these these um sort of error messages or or we have issues with this or that you have people you said you know when you submit like a a ticket with us you have someone with a phd in optimization or decades of experience that looks at that and thinks here's how i can help you Um, so, um, so they leveraged that and they used sort of our, you know, they used our, our, our support system to really sort of help them.

55:31And, and now they're saving, uh, I mean, I, I don't want to mischaracterize the number, but it's a lot of money. Um, and they're being able to reinvest it then. And that's, that's, what's really great about these projects. These optimization projects is, is, you know, yeah, you're saving money typically, but it It gives you an opportunity to reinvest and make things better elsewhere. So those are a couple of really cool customer stories that I was able to hear. And there's tons more, though. Tons, tons more.

56:00Jon Krohn:Thanks, Jerry. It's great to be able to get all the detail on these kinds of mathematical optimization projects. It gives us color that we can use to imagine in our own worlds, in our own businesses, and the problems that we're tackling, how we could be taking advantage of mathematical optimization to, as you say, get some savings and reinvest that into some more growth somewhere else. Now, looking to the future a little bit, in episode 887 of this podcast, we had the global CTO of Dell, John Rose, on the show. And it's an incredible episode. He's an amazing individual and a really compelling speaker.

56:40Yeah.

56:42Jon Krohn:Incredible episode, 887. And in it, near the end, just like near the end of this episode, he talked a fair bit about quantum computing and how quantum computing is an inevitability. Just like you could see deep learning capabilities over time, and eventually you hit this inflection point where all of a sudden there's tons of real world applications that are commercially viable. All of that is, you know, you could have modeled it for deep learning for years, decades in some cases, like Ray Kurzweil was. And John Rose says that same thing is now happening with quantum computing. Yes, today it is expensive.

57:23Jon Krohn:Yes, today there are not a lot of real world applications, but just look at the trajectory of where this is going. It is an inevitability that quantum computing will change the world in the coming decades. It's how does quantum computing interact with mathematical optimization? It's something that kind of gets in the way of people doing optimization because for the main reason is there's, you know, if you've taken any sort of course or understanding of like sort of computational complexity and things like that, a problem complexity, which is I've been talking about that a lot. You run across terms of like NP, P versus NP of problem types, NP hard, NP complete, all these types of sort of categorizations of decision problems that sort of show how hard it is to solve.

58:17And there's like this idea that if something has a certain label now in today's sort of computational world, that it is unsolvable and don't even try it. And mixed integer programming, which is, again, like the main sort of way that people, the main sort of model type that people use mathematical optimization for and Garobi for, that falls into one of those super hard buckets. So people think it falls in a super hard bucket. The super hard bucket can't be solved today. So I need to wait for something like quantum. and so there is this sort of misconception of that sort of thought path that I just laid out what we're here to say is okay quantum computing it may be an inevitability it may not be I'm not going to sort of plant a flag on one side or the other on that but what we do note is today is yeah it's not and I think everyone would agree that It's not a viable way to really solve problems today.

59:31So if you're a CTO today or something like that or CEO today or something like that, wouldn't you like to save a ton of money or be super efficient now instead of waiting for the possibility of this happening in the future? so um so so that's sort of like one of the hurdles that we're trying to get over is um you could be doing you could be doing all the stuff that that um that some of these optimization quantum optimization problems are saying that will happen in the future like oh you can revolutionize your supply chain in the future with quantum you could revolutionize your supply chain now with mathematical optimization and then and here's what's here's what's really cool.

1:00:15Okay, let's say that in 10 years, quantum optimization becomes the thing. You've already laid the groundwork. You're not starting from scratch. You're not missing the boat. All of the things that you would need to do today to use mathematical optimization, understanding your business case, understanding all the problems that you can approach with this, all the ones you can't, understanding all the stakeholders, their involvement, getting by in, getting all sort of like the data connected to make sure that you're making the right decision with the right data going into your optimization model. All that stuff needs to happen regardless of if you're using mathematical optimization or quantum.

1:00:57It still has to happen. So why not do it now? Why not get the benefits now? And then if quantum becomes the thing that some people think it's the inevitability, then you're just that much better prepared. You don't need to then, and if there becomes like this sort of rising tide of quantum, you'll know exactly where you are and what you can be doing. And then when you actually do sort of prototype or proof of concept quantum, you have a reliable benchmark. You have the right number that you should be comparing it to, you have an optimal state now versus what an optimal state could be with quantum because sort of what the difference between that and what we can provide now and in the future and what quantum would probably provide is a lot more granularity, a lot more complex of the way that you model your problem down to like the nitty grittiest detail.

1:01:59Sometimes that can't happen with mathematical optimization now. If you're talking about a supply chain, you're not modeling individual workers and what they're moving, you know, doing stuff like that. Maybe with quantum, that is something that you can do. You can get that extra granularity to help you really do things, or it solves like problems that maybe take like an 10 minutes or an hour to do with, with optimization. Now, maybe you are solving that in a second or a millisecond. Um, so, so the whole, the whole thing is like, it could be, but why not get started now?

1:02:29Jon Krohn:I love that perspective. I had no idea where you're going to go with that answer. And this was a, yeah, that was definitely not where I expected to be, but I love that framing of if there's kinds of problems that you could be solving in your organization today that you think could only be solved in the future with quantum computing, that might not be true. Mathematical optimization could be the answer. Really interesting perspective. And I love that you've come on the show again for the third year in a row for our annual reminder of the importance of mathematical optimization, how it can be such a useful tool to have in our belts alongside the other analytical or predictive tools that we have in our tool belt.

1:03:13Jon Krohn:And Jerry, you probably remember from previous years that I always have the same final two questions for you. And I didn't really prepare you for the penultimate one today. So hopefully you you already have in mind a book recommendation for us? Yes, I do. I do. So, and this is pivoting very, very far from everything else that we've talked about today. But for me personally, I'm, I have two kids. So I have a four-year-old son and a one-and-a-half-year-old daughter. so that takes up a lot of my spare time but um i i read a book a little while ago um called a better man um it's a mostly serious letter to my son by uh michael ian black he's a comedian that i that i like probably dating me and showing letting people know how old i am um but it was a great book that i read and it really put into perspective like raising kids particularly a son and stuff like that, raising kids in today's day and age.

1:04:19So yeah, highly recommend that as a book for any sort of newer parents out there or perspective, you know, hey, this is something that I'm going to get my, you know, dive into. Highly recommend that book. It was great for me to read, put some good perspectives on that. So that's my, I'm going to pivot from all the math stuff that I usually talk about with what I recommended the number zero. And then the last one after that was, um, showed some Nintendo geekdom of mine. I think it was, uh, ask a water was my other book that I, that I recommended this time. It's all about parenting. Cause that's outside of like talking to people like you day in and day out during my, my office hours after that, it's just kids.

1:05:05So, um, so that's, that's what's on my brain. 90 % of my life.

1:05:09Jon Krohn:That must be wonderful, jerry i appreciate the recommendation and i'm blown away you remember your recommendations from previous year as well um all right final question this one's an easy one a layup how should people follow you after this episode obviously we'll have garobi.com slash learn in the show notes for people to get your free tutorials and code and open source resources and get going with mathematical optimization and garobi in their own lives but uh where else Sure. The best way is probably LinkedIn. That's where, you know, I kind of post a lot of my thoughts of the events that we have, the events that are upcoming.

1:05:47And a lot of a lot of stuff, you know, I talk about like great partners when I do a presentation with with, you know, a cool company that we work a lot with, like Nextmove, who does operationalizes optimization modeling. you'll get a lot of that information there. And so in addition to LinkedIn, I also have a Blue Sky account that I post some thoughts here and there about random things. And our company is also sort of kind of on there and we promote a lot of our events there. So that's another great way to sort of stay in touch with what we're doing and what I'm doing. So LinkedIn and Blue Sky are are kind of the two, the two go-tos for, for mathematical optimization and anything that you want to hear about from me, if that's what you're still into after listening to this episode.

1:06:36Jon Krohn:We really appreciate you coming on the show yet again. I always love these episodes and I'm sure a lot of our listeners do as well. Thanks for all the new use cases and applications, ideas related to mathematical optimization this year. And hopefully we'll be checking in with you again soon. That sounds great. Happy to be on again. Happy to sort of get some information out to your loyal listeners who are a bunch of cool people. And yeah, feel free to anyone to interact and hopefully I'll see you guys around. Always a treat to have Jerry Yurchison on the show. In today's episode, he covered how mathematical optimization provides a framework for complex decision making by defining decision variables, constraints, and objectives to find provably optimal solutions.

1:07:24Jon Krohn:He talked about how unlike machine learning, optimization tells you exactly what actions to take given your specific business constraints and goals. How Garobi has developed custom GPTs and AI tools that streamline the application of mathematical optimization. And he talked about real world applications of the approach, such as Toyota's vehicle manufacturing planning system and Total Wine's complex inventory optimization. As always, you can get all the show notes, including the transcript for this episode, the video recording any materials mentioned on the show the urls for jerry's social media profiles as well as my own at superdatascience.com slash 931 thanks to everyone on the super data science podcast team our podcast manager sonja brevich media editor mario pombo partnerships manager natalie zyaiski researcher serge massis writer dr zara karshay and our founder kirill aromenko thanks to all of them for producing another awesome episode for us today for enabling that super team to create this free podcast for you.

1:08:23Jon Krohn:We're so grateful to our sponsors. You can support the show by checking out our sponsors links, which you can find in the show notes. And if you'd ever like to sponsor the show yourself, you can find out how to do that at johnkrone.com slash podcast. Otherwise, help me out by sharing this episode with folks that would love to learn about mathematical optimization, review the episode on your favorite podcasting app or on YouTube. subscribe obviously if you're not a subscriber but most importantly i just hope you'll keep on tuning in i'm so grateful to have you listening and hope i can continue to make episodes you love for years and years to come until next time keep on rocking it out there and i'm looking forward to enjoying another round of the super data science podcast with you very soon