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Episode 5: Improving Operations with Route Optimization

Data Overflow

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Introduction

Route optimization is very useful nowadays. The route suggested by your GPS is made based on different factors and is using data science to improve your trip by saving you time and effort. In this article, we’re going to talk about a specific experience of a route optimization program made for an Asian delivery service called GOGOVAN, where the idea came from, and how it started.

What is GOGOVAN?

Every day, GOGOVAN drivers arrive at warehouses across Asia to pick up thousands of orders that their business partners have asked them to deliver to their clients. These orders can be a range of things — from that long-awaited new phone to an anniversary present that was ordered at the last minute. All of them will be of different sizes, shapes, and weights. For each one of them, there will be a person waiting and hoping this time the courier company gets there on time… This is why at GOGOVAN, they do everything in their power to ensure smooth and timely deliveries with a quality of service that will amaze customers. Every delivery route is carefully planned manually and double-checked by the Operations Team, to make sure it never fails.

The problem they were facing

In the past, GOGOVAN’s Operations Team had to manually sort out the delivery routes, usually on the morning of pickup, and ensure they meet all the delivery time requirements for that day. As you might imagine, that is not a particularly exciting nor easy task. It took one person approximately 1 hour to create a sub-optimal route for 100 waypoints. For requests bigger than that, this time grew exponentially. GOGOVAN instantly realized that this process was simply begging for some automation.We feel sorry for the Operations Team who had to do such mundane work each early morning, but when the order volume grew, that task was slowly becoming mission impossible. The delivery service company saw it as an opportunity to develop a cutting-edge technology that will become a core component of GOGOVAN’s Data Science stack.

How did the solution start?

GOGOVAN is very client and driver-centric. Consequently, they always try to analyze a problem from their perspectives in order to understand how the solution could impact and benefit both. After a lot of brainstorming, these are the goals they came up with:

  • All orders need to be delivered on time.
  • Ensure drivers are not rushed to make it on time by using buffer times and real-time distance.
  • Save fuel by reducing the distance driven.
  • Minimize idle time for drivers — no one likes waiting with a trunk full of packages.
  • Improve vehicle utilization.
  • Fully automate the process.
  • The algorithm needs to be growing with the problem.

They realized that the problem they are facing is widely known as Vehicle Routing Problem (VRP):
VRP can be described as the problem of creating a set of optimal routes from one, or many, depots to multiple customers, subject to a set of constraints. The objective is to deliver goods to all customers, at the same time minimizing the cost of the routes and the number of vehicles.

The Solution

With VRP being a widely recognized problem, there are indeed a lot of companies out there who seem to be tackling the problem. However, GOGOVAN somehow did not feel satisfied with previous solutions… They created their own with the help of the folks at Google who spent months coding all the different algorithms they wanted to test. They built their tool of Route Optimization and it looks like this:

The volume of orders submitted to Route Optimizer quickly increased from 500 items per warehouse to more than 1000. The algorithm’s runtime and memory usage jumped incredibly quickly — from 1 minute and 500 MB to 10 minutes and 5 GB. As they tested it for higher and higher volumes, they finally reached the maximum — for 2000 waypoints the module used up 25GB of RAM. They used a renowned clustering algorithm — DBSCAN. What they have is a state-of-the-art method that groups together geographical points. However, it has its downside: each cluster has to have the same radius. With the combination of the operations know-how, data science and research expertise, large volumes of data, and open-source state-of-the-art contributions, they arrived at a robust in-house solution that:

  • Is more up-to-date, performant, and has customizable algorithms and iteration logic.
  • Is cheaper, more efficient, and more scalable.
  • Allows developing tangible intellectual property assets and building a competitive advantage around it.

Conclusion

In this article, we presented an approach to the Capacitated Vehicle Routing Problem with Time Windows for a large number of waypoints (up to 5000). By using a recursive-DBSCAN method GOGOVAN was able to significantly reduce runtimes and memory usage while maintaining a similar quality of results as in the baseline Google Optimization Tools method. The algorithm was of great help to the Operations team reducing hours of mundane manual work to a few minutes of CPU time. GOGOVAN decided to be a leader in the field by not using a BlackBox solution but developing its own.

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Sciences et technologie

Episode 7: How AI could save the planet’s biodiversity?

Data Overflow

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Innovations in Data Science and machine learning have brought the benefits of Artificial Intelligence to bear on our daily lives. By working alongside machines, people can now accomplish more by doing less. Putting AI into good use can help solve some of the world’s most urgent and difficult problems rather than helping organize our calendars, order our groceries, or play games.

One of the most fundamental global problems today is the exponential loss of biodiversity. In fact, scientists say that our planet is in the middle of its sixth mass extinction, the worst one since the extinction of dinosaurs, 65 million years ago. And that is due to countless direct and indirect human actions, such as poaching (illegal hunting or capturing of animals), overpopulation, habitat destruction, and climate change. Our planet’s rich biodiversity is taking such a huge hit. 

In this fight against human greed, AI models have turned into an unlikely ally, helping save our planet, paradoxically from our own hands.

 

Today, environmentalists are facing unfair odds as hunters call on the latest technology in night-vision goggles, military-grade weaponry, and sophisticated transportation. But thanks to the immense power of deep learning, AI has unlocked the ability to rapidly scan, process, and analyze a variety of signals, identify risks accurately, and provide almost immediate alerts to the authorities.

It is a type of AI system that is particularly effective at pattern recognition and identification. For example, when these models are given thousands of pictures of whale sharks, they can learn to spot a unique whale shark from a future sighting, with remarkable accuracy let alone handle unstructured data such as images, videos, and audio clips. This incredible feature can help solve another urgent matter which is species collection.

To this day, scientists have discovered and described only 1.5 million species of the estimated 10 million on earth. At current rates, we will have to wait almost 500 years to collect all the estimated species and by then, most of them may be extinct. AI and associated technologies have the ability to close this information gap cost effectively and efficiently with hardware becoming increasingly cheap and power-efficient enough to deploy monitoring systems on the ground, on animals, in the sky, and up in space. Early work is proving that algorithms can sift through the massive amounts of data streaming back from these monitoring systems. In turn, humans and machines can begin to identify the plants, birds, fish, and other species captured by these remotely deployed cameras, microphones, and more sometimes down to the unique individual. And we are finding new ways to deploy these technologies every day. For example, Microsoft is working on ways to use organisms such as mosquitoes as small, self-powered data collection devices that can help us better understand an ecosystem through the animals they feed on.

Artificial intelligence can help us understand land-use patterns as well. Microsoft and others are experimenting with ways to turn high-resolution imagery into land cover maps. These maps provide an unprecedented view of what is where, and how it is changing. This in turn helps governments, organizations, and researchers make more informed decisions about when, where, and how to deploy conservation efforts most effectively for the greatest impact. This creates a virtuous cycle of learning, as all this information can then be fed back into AI systems, making them smarter. Thus, AI methods make it possible to build a digital dashboard for the planet, allowing us to monitor, model, and manage environmental systems at a scale like never seen before.

The most obvious use of AI indeed seems to be for further extraction, consumption, and production. However, in the middle of a climate crisis, and with a deteriorating ecosystem, species are dying. Artificial intelligence can be a magical silver bullet that will help us restore the planet. It won’t be an easy journey, but by applying the power of AI to help both humans and our natural systems thrive, we can help provide a better and healthier future for the planet.

Resources:

How Artificial Intelligence Could Save the Planet

A Multilateral AI Strategy for Biodiversity and Restoration

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