Imagine a company providing last-mile delivery or logistics services that amount to thousands of parcels per day, which are delivered by a fleet of 100 vehicles. The delivery fleet dispatcher needs to create an optimal delivery route for each vehicle, that takes into account multiple objectives and constraints, such as vehicle distance traveled, time to destination, delivery time windows, driver availability, vehicle capacity, and more. The dispatcher also needs to be able to update the delivery plan in real-time, as end-customer and traffic conditions change during the day.
Designed to solve Vehicle Routing Problem (VRP) like the example above, Google Cloud Fleet Routing (CFR) is an AI-powered managed service that uses advanced optimization algorithms and machine learning techniques to help businesses plan and schedule vehicle routes for fleets of various sizes. Commonly known as a “solver” in the world of transport logistics planning, CFR is the first product released under the Google Cloud Optimization AI suite of decision-support systems.
How Cloud Fleet Routing solves Vehicle Routing Problems
A well-known challenge in the field of logistics and transportation, the Vehicle Routing Problem involves finding the most efficient routes and schedules for a fleet of vehicles to transfer goods or services to a set of locations, while considering a wide range of objectives and constraints that may often conflict with one another. Below is a detailed (yet non-exhaustive) list of criteria that can be implemented in CFR:
CFR leverages Google Cloud’s impressive capacity and flexibility in order to resolve Vehicle Routing Problems more efficiently by utilizing both machine learning and compute power scalability. It uses machine learning to analyze historical routing data, combining it with real-time traffic information in order to predict optimal routes. At the same time, it leverages Google Cloud's massive parallel processing capabilities to solve multi-parameter routing problems quickly and accurately.
Optimizing routes in real-time
CFR generates a set of optimized routes which can be visualized on a map, providing fleet managers with a comprehensive operational overview of the activity planned for the fleet, and can be used to guide the drivers of the vehicles as they make their deliveries. Iterating quickly during the delivery planning stage, CFR reoptimizes routes in real-time as new events emerge during the delivery execution stage (such as major traffic incidents or new deliveries to make), and can enable parallel scenario-testing to identify optimal solutions when a set of constraint parameters are tweaked.
Integrate with Google Maps Mobility Solution
Even better, CFR integrates seamlessly with the new Google Maps Mobility Solution, which provides a set of modules for the efficient execution of deliveries that have been planned using CFR, and which take into account real-time changes in delivery priorities and traffic conditions. Drivers, consumers, and fleet managers are able to receive useful and timely information on delivery fulfillment status, which improves user satisfaction and contributes to significant cost savings.
For example, the global logistics company UPS uses Google Cloud Fleet Routing to plan its delivery routes. UPS has seen a 10% reduction in fuel consumption and a 5% reduction in emissions since starting to use CFR.
Conclusion
The team at DoiT is passionate about implementing innovative Mobility solutions, and we are certified resellers of both Cloud Fleet Routing and Maps Mobility Services. We encourage you to take advantage of Cloud Fleet Routing’s AI-infused capabilities to ensure that deliveries are accurate and on-time, save costs by increasing the delivery capacity of the delivery fleet, and reduce CO2 emissions by minimizing total travel distances. Google Cloud Fleet Routing is available now for testing and implementation, and a web application example is available on Github.
