Route Optimisation

AI for route optimisation

Route optimisation is the process of planning the most efficient and effective route for a fleet of vehicles to travel. It involves taking into account a variety of factors, such as the location of stops, vehicle capacity, traffic conditions and time constraints.

It's a classic problem in optimisation—and is the generalised version of a problem referred to as the Travelling Salesperson Problem.

Route optimisation is a complex task that only gets more complex as you deal with larger fleets, more constraints or increasingly complicated delivery schedules. Hence, traditional methods of route optimisation can be time-consuming and—as complexity increases—inaccurate, as they may not be able to take into account all of the relevant factors.

AI route optimisation solutions can help businesses to overcome the challenges of traditional route optimisation methods. These solutions use AI to analyse large datasets of data, including historical delivery data, traffic data and vehicle data. This information is then used to generate optimal routes for large, complex delivery networks.

Benefits

AI route optimisation solutions provide a number of benefits, including:

• Reduced costs: By reducing the number of vehicles required, the number of driver shifts and the amount of fuel used.
• Reduced CO2 emissions: By reducing the number of miles driven and potentially type of vehicles used (e.g. moving to electric vehicles).
• Improved asset utilisation: By enabling a smaller number of vehicles and drivers to deliver higher loads and do more miles, reducing overall asset requirements.
• More product delivered: By enabling higher fill rate of vehicles and reducing the risk of unbalanced routes where there is more product assigned than capacity on the day.
• Increased customer happiness: Fewer delays, through more realistic schedules that better take into account customer and route time requirements.
• Meet regulatory compliance: Through robust and feasible schedules that comply with constraints which include regulatory requirements (e.g. driver working time directive which states they cannot work for more than a certain amount of time without breaks).‍
• Better managed operational risks: Through scenario modelling of risks and disruptions to put in place contingency planning.

Who it’s for

AI route optimisation solutions can benefit businesses in a wide range of industries, including retail, logistics, transportation and field services. They benefit businesses of all sizes, from small businesses to large enterprises. Though, the more complex the delivery network, the higher the ROI an AI solution is likely to produce.

Some of the specific roles that can benefit from AI-powered route optimisation solutions include:

• Fleet managers
• Dispatchers
• Transportation managers
• Logistics managers
• Delivery managers

How it works

A typical Datasparq route optimisation solution works by analysing the following types of data:

• Historical delivery data (such as delivery times, distances, and traffic conditions)
• Real-time traffic data
• Vehicle data (such as vehicle capacity and mileage)
• Delivery data (such as the location of stops, delivery times, and delivery requirements)

This data is then used to train a machine learning model to generate optimal routes for businesses. The model can then be used to create routes for individual vehicles, or for entire fleets of vehicles.

Getting started

As outlined above, by optimising delivery routes, AI solutions can help businesses to reduce costs, improve customer service, increase productivity and reduce their environmental impact.

Contact us today if you're interested in learning more.