Machine Learning-Based Vehicle Routing Software

How to increase the number of deliveries in a day? if you are a person who delivers products and goods then consider using Vehicle Routing Software.

This is the question that fleet managers across the world think about day in and day out. The task of assigning deliveries to drivers is complex.

Imagine this in terms of mathematical and statistical notations.

‘N’ number of drivers are traveling through ‘ m’ nodal points via ‘X’ routes to meet ‘y’ customers. Sounds complex? Let’s explain this is in a better way:

What is a Vehicle Routing Problem?

Vehicle Routing Problem is a problem that arises when a business attempts to find optimal routes for a set of destinations. Traveling salesman problem (TSP) is a problem that arises when a vehicle routing problem is analyzed with just one vehicle.

What is an optimal route for a vehicle?

The fastest and least total distance to reach a destination is known as the optimal route. When there are no constraints, one vehicle can be assigned to all destinations and provide the shortest route. This problem now becomes the same as TSP. The definition of optimal routes in daily life is different.

What is an optimal route for multiple vehicles?

The optimal route is minimizing the longest single route for all vehicles and completing all deliveries quickly.

Constraints for a fleet in the last-mile delivery

Capacity constraints: Overloading a vehicle more than its maximum carrying capacity is a constraint for a fleet in last-mile delivery.

Time constraints: The specified timeframe or time windows within which fleet drivers must reach their destination is another constraint in last-mile delivery.

Resource constraints: The assets and resources such as the number of vehicles or drivers available to manage daily deliveries are called resource constraints.

Why do you need a Vehicle Routing Software?

The right routing software helps optimize the routes for your deliveries. It takes all constraints into consideration while formulating routes.

When does a machine learning-based Vehicle Routing Software come into play?

Once a shipment at the warehouse is ready for delivery, it needs to be assigned to a rider who covers a pin code area. By following the optimal route recommendations, the rider should deliver goods to various specified destinations.

There is an increasing demand for massive shipment volumes and a quicker turnaround time. A vehicle routing software helps businesses fulfill these requirements with dynamic vehicle tracking and route planning.

A machine learning-based Vehicle Routing Software considers the availability of riders, vehicle capacity, traffic congestion, time windows, etc. while simultaneously coming up with optimal route plans. It also considers preferred time slots and enables the personalization of deliveries when routing the driver to the customer location.

Benefits of machine learning based vehicle routing software

Route planning with accurate ETAs

Earlier, it was possible to generate routes through brute force mathematics and determine the shortest one. Today, AI and machine learning algorithms can uncover correlations and trends in massive datasets that traditional methods cannot deduce.

A route built to solve the vehicle routing problem can easily identify factors that correlate with the efficient routes over time. The route planning software is built with machine learning (ML) algorithms that become accurate as fleet managers plan more routes predicting traffic patterns, on-time delivery, etc.

The application of ML ensures that calculation of Expected Time of Arrival (ETA) moves from mere guesswork with large windows to accurate estimates based on real-time data.

Artificial Intelligence has the potential to turn customer locations, current traffic patterns, historical traffic data, truck and driver data into more optimal routes. A machine learning-based vehicle routing software ensures that logistics businesses are a few steps ahead in generating efficient routes.

A typical route planner without the usage of ML algorithms generates efficient routes but lacks accurate ETAs and can be a headache for businesses. It requires human intervention in setting accurate ETAs. On the other hand, ML algorithms can calculate complex parameters and constraints and provide optimal routes with accurate ETAs too.

Minimizes lost time

The skyrocketing demand for online deliveries has pushed businesses to offer same-day and next-day deliveries and satisfy their customers. With worsening traffic and shorter delivery window timeframes, it has become crucial to minimize the lost time of deliveries.

A machine learning-based vehicle routing software based on historical and present data can effectively anticipate potential traffic situations. Based on the analysis of congestion data over a period, it helps fleet managers and drivers generate the fastest routes with less idling time in traffic. This helps fleet drivers cut down the lost time due to traffic congestions.

Faster delivery times

Businesses today have been pushed to compete with e-commerce giants like Amazon and Walmart. The key to improving competitiveness in the last-mile delivery is to ensure orders are dropped cost-effectively on time.

A machine learning-based vehicle Routing software speeds up the calculation time for generating optimal route recommendations. With routes being consistently efficient, drivers can complete more deliveries on a given day.

Quicker the deliveries higher the space for businesses to accommodate last-minute changes and additions. Decades ago, it was difficult to make adjustments to already set routes. But today an ML-backed vehicle routing software allows businesses to be more agile to changing delivery needs and enables dynamic route planning.

Improves cost savings

With an ML-backed vehicle routing software, logistics businesses can ensure that their vehicles spend less time on the road. The faster the deliveries the higher the cost savings a company enjoys. Faster deliveries reduce fuel expenses significantly and minimize the overall fleet transportation costs.

Less time spent on the road helps businesses reduce the wear and tear of vehicles. It helps them create regular predictive maintenance checks for vehicles on a timely basis thereby maintaining the vehicle’s health. It even saves additional labor costs and insurance costs if there are any untoward incidents occurring for the vehicle.

Conclusion

As the COVID-19 pandemic brings continuous threats and major changes to e-commerce, businesses should ensure that their fleet takes the most optimal routes. ML-powered vehicle routing software helps businesses ensure cost-effective and fast deliveries even if there are congested roads, influxes of orders, or any fundamental complexities in the last-mile delivery.

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