Getting Better Answers Faster: Providence Software Startup Dynadec Goes Way Beyond the Traveling Salesman Problem

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the important parts of the search space quickly. We also have a platform which hybridizes several different techniques, which you typically need in these cases.

The second part is dealing with uncertainty. If you want to solve a problem once and for all—if you model all of the uncertainties and try to find the best policy forever—that is very difficult. The problem is too hard. We want to dynamically make decisions that solve the problem we have today, and use predictive information to guide decisions.

The combination of those two things gives us a high-quality solution. We are trying to solve the problems we have today using predictive models and optimization using the information we’ve gathered.

X: That may be a bit too high-level. Can you give some examples?

PVH: Imagine you have a repair technician who repairs machines for a company, and these machines are taken out of operation with a certain frequency. You want to service them as quickly as possible so they get back into operation and maximize your revenue. We would collect information about which machines are broken and when, and with what frequency do they break, and where are they located. To schedule the technician in an optimal fashion, we’d want to give you some idea of which machines will break today, and you can use those scenarios to decide which technician should go where.

We are working with Los Alamos [National Laboratory] on a different problem related to disaster recovery. Los Alamos has a very powerful tool to predict the effects of earthquakes or hurricanes. They give us scenarios, and we can help them predict which power lines are going to break, and if they fix these lines, what the network will look like. We help you decide how you should position your resources to restore power most quickly after a disaster. These things are changing over time, of course—so five minutes later we may have another set of scenarios. A lot of this is done manually right now, and the companies we are talking to see that there is an opportunity to get much better, higher-quality solutions most of the time. There will always be a human in the loop who will see the different sets of possible solutions, but we are basically automating the decision process.

X: Do you ever run into resistance from people who feel that there’s an important gut-instinct aspect to the kinds of decisions you’re talking about? People who are reluctant to turn over these critical decisions to software?

PVH: We run into that all the time. Some people have prior experience with optimization technology that couldn’t solve their problems, so they’re very skeptical. Other people say “Yes, but how do we know this really is a good solution?” The way we address that issue is by saying, “Okay, let us show you what we can do.” We show them pilot solutions that actually do better than manual. We show them the data. And we try to explain how the decisions are done; we typically build a visualization that removes some of the “magic,” that tells them this is why we are doing this right now.

But none of this will remove the need for a human in the loop, because the model is never a complete characterization of real life. People want to be involved in the loop because they see things the system hasn’t seen and which is not modeled completely or accurately.

X: Would you go even farther and say that there may be certain application areas—say, high-reliability, highly dangerous environments like nuclear plants or aircraft carriers—where it’s inappropriate to rely too much on optimization software?

PVH: That’s a tough one. Let me tell you my take on this. Some of the applications of this technology will be in areas like controlling the electrical grid. And typically, what they do now is, there is always a human making the final decisions, but they use a lot of different software to help the human choose. Software can help the human understand what’s going on and predict what the future can be.

We would enter the picture by telling you what the future can be. When you’re trying to manage risk, you give recommendations to people and eventually they have to make the decisions. I think if you see a lot of different recommendations produced using different tools and they all agree, that would give you some extra confidence. This is a decision support system—that’s how I see it.

When we ask customers about this, what is actually a big surprise to me is that most of them seem to believe that humans, not software, are the main source of errors.

X: What sorts of milestones do you hope to achieve this year?

PVH: I think getting engaged with some major customers, building the right technical team, and laying out the first steps of the applications that we will be developing in the next two years are the critical milestones for us. So building a set of very convincing customer solutions is one of the main priorities.

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Wade Roush is the producer and host of the podcast Soonish and a contributing editor at Xconomy. Follow @soonishpodcast

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