Route optimization might sound like a dry topic, but in our experience the more you learn about it, the more fascinating it becomes. Read on to find out everything you need to know about route optimization and deep dive into how it applies for last mile delivery.
What is route optimization and why is it a problem?
Route optimization is simply trying to find the “best” route to get from point A to point B. When you only have two points, it’s reasonably straightforward. In this scenario, “best” usually means the fastest, or most direct route. However, it becomes more complicated the more stops you have.
In a last mile scenario, a delivery truck might have 50 or 100 deliveries to make over an entire city. Which order should they deliver them in? Can they map the route to start and end at the depot? This is what’s known as the Travelling Salesman Problem, or TSP. In a delivery context, a company often has thousands of deliveries that can be delivered by dozens of vehicles that it wants to plan at the same time. This is often referred to as the Vehicle Routing Problem, or VRP.
TSP and VRP are mathematical problems, where the more stops you have, the more possible combinations there are and the more complex the solution becomes. This is further complicated by the definition of “optimized” – what does it really mean? Different people will have different preferences.
Last mile delivery specific requirements for route optimization
The last mile delivery scenario introduces some unique requirements that place more parameters on the definition of “best” optimization. In addition to starting and finishing in the same space, requirements for route optimization can include:
- A pick up and drop off model, where the vehicle gains additional deliveries while on the route
- Some businesses will allow their drivers to take their vehicle home at the end of the shift, so the final destination may not be the depot
- The cargo being delivered may have additional requirements like shorter delivery times, refrigeration, or requiring the most recently picked up item to be delivered first
- Different modes of transport, including internal combustion engine (ICE) vehicles, electric vehicles (EVs), walking, and bikes also have different factors for optimization
- Territory requirements including geofencing such as keeping a route within a certain area and using boundaries like rivers, bridges, or highways
There are other vehicle routing scenarios that are similar to deliveries, such as school buses, garbage pickups, street cleaning, maintenance routing, and even emergency vehicles, all need routing optimization.
Read our deep dive on the delivery needs of the bottling industry.
“Optimization” is subjective
The “best” route depends on the goal of the organization or person taking the trip. For a family road trip, the best route is the one that includes detours to sites and landmarks of interest. For a logistics business, the best route can be tied to both commercial and environmental goals:
- Lowest fuel consumption or carbon emissions
- Least amount of time on road or fastest route
- Shortest distance
- Optimized for EV battery consumption
- Deliver during customer’s preferred time windows
To add further complexity, the “most optimal” route is not always the “preferred” route – drivers and businesses may have preferences they’d still rather stick to even if it adds more time or distance to the overall route. It could be a difficult intersection, a road that’s often congested with traffic or roadworks, or even simply just a personal preference.
Complexity comes from multiple factors
If you were asked to plan a ten stop route, factoring in the vehicle type, customer delivery windows, and territory boundaries, then you had to aim to get your route down to the lowest fuel consumption, and finally layer on personal preferences, do you think you could do it?
You can actually test how good your personal ability to optimize a route is – we have a game that’ll match you up against our routing algorithm. Best played on desktop, see how good you are!
How are routes optimized?
There’s several ways to optimize a route, and it’ll depend on the available data and information. Territories, geographic boundaries, and preferences will all play a role.
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For example, in the diagram above we see a clustering of deliveries on a simplified map, showing a highway and a river. The highway acts as a facilitator, something that improves access and efficiency for delivery. Meanwhile, the river is an obstacle that may impede efficiency.
From that, we then see three options for ways to optimize the deliveries. The first (b) disregards both the facilitator and the obstacle, and clusters the deliveries by proximity. The second (c) factors in the obstacles to cluster deliveries based on their proximity without crossing the obstacles. Finally (d) shows clustering that takes both the obstacles and facilitators into account, resulting in a different configuration.
Clustering can be customized to an organization’s route optimization preferences.
Using real time conditions for route optimization
Weather, traffic, construction, and even unexpected events like livestock on a road can all interrupt your standard routes, so incorporating this data is essential for true route optimization. Machine learning, trends, and predictive analytics can all be used to optimize delivery routes for speed of delivery.
How can route optimization result in business outcomes?
Optimization goes hand in hand with improvement – it’s in the name after all! It’s not just routes that benefit from being optimized though, the business as a whole will reap the rewards too.
Enhancing customer satisfaction through route optimization
Improving your time to fulfill deliveries, meet difficult delivery windows, or even service an out of hours delivery are all ways to improve customer satisfaction.
Having a high customer satisfaction rating leads to having those relationships for longer, referrals, and positive reputation in the market leading to new customers. With optimized routes, you may even be able to achieve adding new customers in without needing additional vehicles or drivers, improving your fleet utilization and overall return from your assets.
One such company that has achieved this is StarTrack Courier. Using Adiona FlexOps, they were able to model what onboarding large new customers would entail before bringing them on. This gave their new customers more confidence in selecting StarTrack Courier as their supplier, and reduced interruption to their existing deliveries and routes.
Implementing route optimization
Of course we’re biased, but Adiona’s implementation is flexible to your organization’s needs. We offer both API integration and data upload, avoiding lengthy IT integrations. Get started straight away by uploading historical data and existing routes and optimize your routes in minutes.
It only takes a quick 30 minute training session to get up and running with Adiona, and it’s designed for route planning teams, so you don’t need to be a data scientist or technically inclined to use it.
Tips for selecting route optimization software
There’s so much to consider when selecting software. Here are the top things to factor into your decision:
- Does the software support multiple delivery models, such as pick up drop off (PUDO) or hub and spoke?
- Who from your team will be using it, and how easy will it be for them to navigate through the software?
- Does the pricing model make you pay for re-running an optimization if you’re not happy with it?
- Will it scale with your routes? How many stops can it handle per route?
- Does it factor in real time data such as traffic?
- Does it include a simulation engine? Good software will cover not only route optimization but a number of use cases such as modeling changes to your fleet, depot, and testing alternative delivery models.
- Are your routes updated dynamically or periodically? Adiona offers pricing for both use cases, so you’re not paying year round if you’re only using it once a quarter or biannually.
The role of route optimization in future trends
As the logistics industry faces pressure to reduce emissions and reach net zero targets (both on behalf of customers and for their own businesses), route optimization is one method to greatly reduce scope 2 emissions.
Routes can be optimized for fuel consumption, and our customers have seen up to 49% reduction in that metric alone. Add on improved vehicle utilization, simulating fleet electrification plans, and reduced time on the road, these all add up.
In 2024, Adiona customers are on track to prevent 12 million kilograms of CO2e from being emitted just by optimizing their last mile delivery routes. That’s the equivalent of 1.3 million gallons of fuel, 16.2 million miles on the road, and $4 million in cost savings.
Route optimization contributes to ESG outcomes
The logistics industry has a huge role to play in reducing carbon emissions and contributing to environmental goals. Just switching to EVs and sourcing electricity from renewable sources will make a huge dent in emissions.
In summary
Route optimization:
- Is a complex mathematical problem, that organizes the stops on a route according to a desired “best” outcome
- It is a flexible problem that can be solved according to a business’s needs
- When selecting a software solution, look for diverse route optimization models
- Route optimization contributes to business success across customer satisfaction, new business growth, and reducing emissions
Start your route optimization journey, and check out our use case demos to see how the platform works without needing to schedule a call.
FAQ
Q: What is route optimization?
A: Route optimization is determining the most efficient route that completes all stops according to time, fuel consumption, distance, or other factors.
Q: How does route optimization software work?
A: Route optimization software uses algorithms to determine the most efficient routes for deliveries, factoring in variables like traffic, weather, delivery windows, and vehicle capacity.
Q: Can route optimization reduce delivery costs?
A: Yes, it reduces fuel consumption, vehicle maintenance costs, and the number of hours spent driving, all of which lead to lower overall delivery and operational costs.
