Ultimate Guide to Route Optimization

Route optimization is the quickest and easiest way for a last-mile delivery business to get more efficient routes and cut costs.

But what IS route optimization exactly? The term is not always well-defined and can mean different things to different people. This guide aims to clear up the confusion, from a short tour of the mathematical background to real-world applications — including the impact on sustainability.

We'll also discuss the problems with conventional route optimization, and introduce the idea of intelligent route optimization as a way to address these.

Route optimization is useful for any organization with drivers on the road who make many stops every day. We’ll focus on last mile delivery businesses and couriers, but the same benefits apply to field service technicians and many others.

What is route optimization?

A short history

In the real world

Algorithms vs humans

Intelligent route optimization

Getting started

Choosing software

Helping the environment

What is route optimization?

Route optimization is the process of finding the shortest, most cost-effective routes between multiple destinations, while meeting real-world needs and business constraints.

If all you have is a dozen addresses and a single driver, a human route planner can do the job. But as the number of stops increases — say as a business grows to a hundred deliveries per day with multiple drivers — the route planning puzzle gets extremely complex.

Route optimization software uses algorithms to automate the process. It can take all the information needed to plan a round of deliveries — addresses, time windows, driver schedules, vehicle capacities, and more — and automatically creates highly efficient routes.

Why does this matter?

In a delivery business, inefficient routes have a direct impact on operational costs. They affect fuel use, vehicle wear and tear, driver wages, and the number of deliveries that can be made in a day.

So if route optimization means less driving, that leads directly to lower fuel costs and increased profitability.There are wider social and environmental benefits to route optimization, too. Route optimization can help relieve traffic congestion and reduce fossil fuel consumption and overall emissions.

A map with delivery addresses plotted on it, ready to optimize.

How do you plan routes to visit all these places in the most efficient way? Route optimization!

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A short history of route optimization

For as long as humans have been moving around the world, they’ve tried to find ways to do it more efficiently. Our ancestors built their first roads following animal tracks, to take advantage of efficient routes that had evolved over many years.

Starting with Leonhard Euler’s solution of the Königsberg bridge problem in 1736 (spoiler: there is no solution), mathematicians have tackled increasingly complex routing problems.

Front-page of the first paper that describes the Traveling Salesman Problem.

This 1949 research note from the Rand Corporation is the first recorded use of the phrase “traveling salesman problem” in an academic publication.

Picture of the Seven Bridges of Königsberg problem.

The Seven Bridges of Königsberg problem: How do you find a route that crosses every bridge, once and only once?

In the 20th century, the Traveling Salesman Problem (TSP) dominated: Given a list of cities, what is the shortest possible route between them that visits each city exactly once and returns to the starting point?
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Mathematicians tried various approaches to tackling the problem throughout the 20th century— in the 1950s and 1960s the RAND corporation even offered prizes for solving it.

To this day, the TSP remains unsolved — at least in the sense of finding the optimal route for problems of any size. Instead, many creative meta-heuristics have been developed to approximate a good-enough solution.

The TSP today

The Traveling Salesman Problem remains one of the most-studied challenges in computer science, along with variations that consider multiple routes like the Vehicle Routing Problem (VRP) and the Pickup and Delivery Problem (PDP).
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And that’s just the start of the variations. When you consider constraints like time windows or vehicle capacities, you now have the Vehicle Routing Problem with Time Windows (VRPTW) and the Capacitated Vehicle Routing Problem (CVRP). You’ll even find academic papers using terms that sound like you’re cursing, like MDHFVRPTW – the Multi-Depot Heterogeneous Fleet Vehicle Routing Problem With Time Windows.

No wonder route optimization isn’t the most popular kid on the block!

Bill Cook lecture on the travelling salesman problem.

A 2021 lecture by Bill Cook explores the mathematics of the Traveling Salesman Problem.

Image of the Travelling Salesman Problem (TSP).

Image of the Vehicle Routing Problem (VRP).

Image of the Pickup and Delivery problem (PDP).

One of the problems of academia is that it incentivizes scientists to publish ever-greater numbers of academic papers. And the best way to get published is to tackle a new variation of the problem, or improve on an existing algorithm by finding more optimal routes. This has led to a huge body of literature covering a plethora of variations with creative acronyms. Chasing the academic goal of finding the best route has led to numerous hyper-optimized, tailored algorithms.

But who cares if a route solution is 99% optimal versus 99.5% optimal? Especially if the 99.5% solution takes 22.6 years of compute time to calculate! That’s how much computation power was needed to set the world record on a TSP problem with 15,112 cities — with no obvious practical benefit.

Meanwhile, back in the real world, real human beings are still spending hours every day struggling with spreadsheets and plotting pins on Google Maps. And frankly, humans are terrible route planners. Which is why an algorithmically optimized route solution that’s “only” 90% optimal can already translate to 20% to 30% shorter routes.

Did you know?

In computer science terms, the routing problem is known as an NP-hard problem: the number of permutations grows exponentially as the number of stops increases. With 57 stops, the number of possible routes is a ridiculous number — 10^75 or a quattuorvigintillion.
To put that into context, astronomers estimate the total number of stars in the universe at between 10^22 to 10^24.

No wonder humans are bad at route planning!

XKCD comic on the NP-hard routing problem.

Source: xkcd.com

Route optimization in the real world

Large corporations like UPS and FedEx have spent billions employing teams of academics to develop their own in-house route optimization algorithms. At Routific, we set out in 2012 to make the power of route optimization accessible to small and medium businesses as well.

At that stage there were a few companies offering routing software, but the existing solutions were complicated and difficult, so we developed an easy-to-use web application that anyone can learn in minutes.

We also needed to solve a naming problem. In 2012 there were many different terms being used to describe the same thing: “TSP and VRP solvers”, “optimal route planner”, “smart vehicle routing”, and “multi-stop route and schedule planning and optimization”. We coined the term “route optimization” as an umbrella for all these solutions. (It’s sometimes called “dynamic route optimization” — but this term is ill-defined and usually redundant. We prefer to keep things simple.)
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Finally, we realised that our algorithms needed to account for real-world factors — because the real world is much more complex than academic scenarios! For example: Academic papers describe routes as if they could be drawn on a piece of blank paper (called a Cartesian plane) — whereas real drivers need to follow the road network, with varying traffic patterns throughout the day and week.

A Google Trends graph showing rising interest in the term "route optimization' from 2010 to 2025.

Route optimization as a search term on Google since 2010 has increased by 300%

Human considerations in route optimization

Traffic considerations

Who likes to be stuck in traffic? Dispatchers often have detailed knowledge of local traffic patterns. You can easily waste hours if you don't incorporate this in your route planning.

Route balancing

Optimizing only for the shortest distance can lead to situations where some drivers are working much longer routes or hours than others. These imbalanced routes are widely regarded as unfair.

Clean clusters

Overlapping routes cause frustration. When a driver sees a colleague going into the neighbourhood they just left, they see inefficient routing. Routes need to pass the test of real-world practicality.

Algorithms vs humans in route optimization

In a survey of 11,246 businesses, we discovered that 72% of them still planed routes by hand without any kind of route optimization and delivery management software. The manual makes sense for startups and very small businesses — after all, it’s free! But at some point the time spent of solving route puzzles gets expensive.

Why manual route planning is a problem

Richard Seymour is the Managing Director of Mount Zero Olives, an Australian business producing high-quality olive oil. For many years, they relied on tribal knowledge to plan delivery routes, but Richard recognized the problem when that process failed.
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Most small businesses that are just starting out will use Google Maps as a route planner or create delivery routes in Excel. While these are familiar tools and a good way to get started, it still requires a lot of manual work and is very time-consuming.

Stringing together a manual route planning process requires tribal knowledge. In Richard's situation, his main route planner was out sick one day, and everything just fell apart — nobody on his team knew how to plan routes.

We discovered Routific out of desperation. All the knowledge of routes and deliveries was in one particular driver’s head. And he wasn’t around. He was out sick.

Richard Seymour

Mount Zero Olives

The algorithm advantage

Route optimization algorithms create routes that are much more efficient than human route planners, in a fraction of the time.

In a 2017 academic study, researchers invited participants into a lab and presented them with a route planning puzzle on a piece of paper and one hour to complete it.

The easiest variant of the puzzle, which you can see on this page, involved creating four routes that covered 31 stops. The test subjects drew routes that were, on average, 9.8% longer than the optimal solution. Not too bad!

But when the puzzle became a bit more complex — six routes and 39 stops — subjects drew routes that were, on average, 20.5% longer than the best route.

You can see that human route planner performance rapidly degrades as the size and complexity of the route planning problem increases. Imagine the difficulty of trying to plan 100 or 1,000 stops by hand — especially where you’re dealing with a complex real-word road network instead of a sheet of paper.

Note also that while a 20.5% shorter route may not sound like a big deal, for a business that delivers to a hundred customers a day, this can easily translate to thousands of dollars saved each month! For larger delivery operations with thousands of deliveries a day, the savings can quickly grown scale into the tens of thousands.

Overall, we think it’s fair to say that an algorithm can outperform human route planners in the real world by 20%. In reality it’s probably much more, but 20% is a good conservative number to work with.

Image of academic study that measured how bad humans are at manual route planning.

The lab-based puzzle had the above picture with the following prompt:
"For this problem you have 4 trucks. There is a total of 370 units to collect averaging 93 per truck. Draw 4 routes that visit each and every one of the sites starting from the depot (the green dot), making sure that each truck returns to the depot with no more than 100 units on board. Remember to change pen colour after drawing each truck route and route the number of the truck (from 1 to 4) next to the route."

We spent about an hour rewriting our routes every time we added a new pick-up point to our network. We used maps, and a lot of manual calculations. Routific’s AI solution is smart and fast. We quickly understood this was the best way.

Thibaut Martelain

Marchè Second Life

Case study: Spring Hope Food

We ran our own humans-vs-algorithms experiment with Spring Hope Food Drive, an annual food bank donation event in our home city of Vancouver. Before finding Routific, organizer Landon Goold spent four hours trying to organize his routes by hand. Then he uploaded the same data to Routific to optimize delivery routes – which took him only three minutes.

The results were staggering: His team got all the deliveries done with eight fewer cars, driving 37% fewer mile

Graphic showing the results of manual planning vs Routific for Spring Hope Food Drive. On the left, manual planning: 30 vehicles, 4 hours planning time, 15.5 hours driving. On the right, Routific: 22 vehicles, 3 mins planning time, 9.7 hours driving – a saving of 5.8 hours.

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Intelligent route optimization

Regular route optimization minimizes travel time or distance – but as we noted above, there’s more to it. Intelligent route optimization considers the human elements of route planning as well.

Why does it matter? After a decade of listening to our customers, it’s clear that mathematically optimized routes are not always practical. Dispatchers and route planners often make manual changes to their optimized routes to get results that are better suited to their business needs.

Routific’s research and development is focused on improving our algorithm to account for the real-world and human factors that complicate the route optimization process.

In the next sections, we’ll take a closer look at the most important developments.

Traffic-aware routing

Experienced route planners know their local traffic patterns, and which roads to avoid at certain times or on certain days. For example, they know to avoid the main route into a downtown area during rush hour, school access roads during drop-off and pickup times, or roads near sports stadiums when there’s a big game on.

Most route optimization algorithms don’t take local time-of-day and day-of-week traffic data into account — so route planners have to manually edit their routes using their personal knowledge and experience.

When routing software providers advertise that they incorporate traffic data, it usually means they pull traffic data from Google Maps and apply it in retrospect to the route solution. Admittedly, this is what we did back in 2015! But we learned that this solution isn’t good enough, as it can break constraints like time windows and result in inefficient route sequences.

Illustration showing two versions of a road with traffic. Above, labelled “With Rush Hour”, the road is jammed with bumper-to-bumper traffic. Below, labelled “Normal Traffic”, there are many fewer vehicles on the road and they’re well spaced out.

Intelligent route optimization uses AI and machine learning to predict future time-of-day and day-of-week traffic patterns, based on historical data for each location. That becomes a direct input to the algorithm during route optimization calculations.

What about real-time traffic information, like an accident that closes a lane? This is not something you can take into account when you’re planning routes in advance — it only becomes a factor once a driver is already on the road. This is where real-time navigation apps like Google Maps, Apple Maps, Waze, etc come in. Routific, like most route optimization solutions, includes a free mobile driver app for iOS and Android. Drivers can use the navigation app of their choice to avoid traffic congestion while they’re driving their route.

If you want to find out more about Routific’s traffic-aware routing, we’re happy to chat. Things are moving fast through 2025, so book a call to get the latest picture.

No more spaghetti routes

“Drivers hate seeing each other on the road. They have a fit! They all talk to each other and show each other their routes. They get really upset,” one customer told me.

Delivery drivers also dislike getting off the highway for just a couple of stops. They often prefer to drive a long leg at the beginning and end of their route, so they can focus their work in a dense area.

The general problem is what we call “spaghetti routes” — tangled routes that overlap and criss-cross each other. They may be shorter mathematically, but they are not always better in the practical sense.

Here’s an image that illustrates the difference:

Drivers have to get off the road, do two stops, then keep driving – they hate it!

Joe Wemert

Becool Couriers

Side-by-side screenshots showing a close-up view of routes planned around Los Angeles. On the left, two areas are circled. In each, three or more drivers are serving the same area.

The image on the left is from a competitor, the one on the right is from Routific. These routes were created using the same set of stops and drivers.

While the picture on the left had slightly shorter routes, you can see them overlap with one another like a messy spaghetti bowl. If you look at the circled areas, you can see three or four different drivers serving the same small area.

Take a close look at the blue route starting at the bottom left and finishing at the top right, with a lot of single deliveries along the way. You can imagine the look on the driver’s face when they keep crossing paths with the driver of the purple route!

Routific’s solution to the problem is what we call “clean clusters”: Our algorithm builds routes by clustering nearby stops together as much as possible, without violating constraints like time windows.

Driver familiarity

Driving an area you know well is more pleasant and more efficient.

In fact, research led by Paolo Intini shows that drivers can drive 10% faster on roads they have driven on more than five times.

That’s because there’s a learning curve — the more often a delivery driver covers the same area, the better they know its roads, parking options, building entrances, and clients. This can shave a few minutes off every delivery, adding up to a lot of time over multiple routes and days.

This is another reason why planners in many delivery businesses manually change the routes they get from route optimization software: to try and keep drivers in the territories they know best. Some create fixed driver territories — but hard boundaries can harm overall route efficiencies.

Drivers can drive 10% faster on roads they have driven on more than five times.

Paolo Intini

Polytechnic University of Bari

A heat map of an area in Manhattan, with red at the core (most familiar) shading through yellow and green to blue (least familiar). The key is is headed “Driver Familiarity Map” and “Joe Santo”. The map thus clearly shows the area that is most familiar to the driver named Joe Santo.

Routific’s intelligent route optimization includes a driver familiarity feature, being rolled out during 2025. The system keeps track of where drivers have been and builds up a profile of the areas they’ve visited most often. That enables dispatchers to assign routes to drivers with this knowledge in mind.

Balanced routes

Route optimization algorithms are designed to achieve particular objectives (called a “fitness function” in the math jargon). Typically, the objective is to minimize either total drive time or distance.

But, as we’ve already noted, this doesn’t always give great real-world results. The practical problems include not just spaghetti routes, but also unfair allocation of work between drivers — assigning 50 deliveries to one driver where another only gets 20, for example.

Route balancing needs differ depending on the business and the use case:

A business that pays drivers by the number of packages they deliver will want to balance the routes by number of deliveries.
A business that pays based on hours worked will want to balance the routes by shift time.
When drivers get a fixed salary, they will prefer to be treated fairly, with roughly equal workloads.

Routific’s smart auto-balancing allows dispatchers to choose between balancing shift times or balancing orders.

Accurate geocoding

One of the most fundamental tasks in online mapping services is geocoding. Geocoding is the process of converting an address or place name to an actual location on Earth, with a corresponding pin on the map.

In last mile delivery, accurate geocoding is important because it can help to avoid delays and missed deliveries. For example, let’s say a delivery needs to be made to an office building at 220 Cambie Street in Vancouver.

In the image below, you can see the difference when the address is geocoded to the middle of the building vs the street entrance. In the first case, the driver ends up being routed to an alleyway with no parking and no entrance to the building. In the second case, they get sent straight to the entrance.
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Big buildings often have multiple entrances: For example, a loading bay might be on a completely different street from the main entrance. Correctly locating the right spot during route planning can save a lot of time. Even a few minutes spent circling around the block adds up over a day!

In other words: More accurate geocoding can translate into more deliveries per day, which lowers cost per delivery.For the most accurate geocoding:

Make sure you have clean addresses.
Choose routing software that has smart geocoding and highlights unclear addresses for manual verification.

How to get started with route optimization

All that’s needed to get started with route optimization software is a list of addresses — usually kept in a spreadsheet or exported from an order management or e-commerce system.

We’ll use Routific as an example, but all routing software follows the same three basic steps:

Import a list of addresses
Define route constraints
Optimize routes and inspect

Our philosophy is that AI should augment human dispatchers, not replace them. We think of Routific as an Iron Man suit — it gives the dispatcher superpowers! A single dispatcher can now manage a fleet of twenty drivers easily.

Upload a spreadsheet, or import directly from another system using an API.

The data can include order details, delivery time windows, stop durations, load sizes, and more.

The addresses are then geocoded and plotted on a map.

Good routing software will warn you about potentially faulty addresses so you can review and fix them.

A screenshot of the order import screen in Routific.

The next step is to set up delivery parameters like start locations and shift times.

With Routific, you can either specify the number of routes up front, or let the algorithm figure out how many routes are needed.

You can also specify vehicle capacities to make sure routes are not overloaded.

The Route Templates screen in Routific where users can set route parameters.

With all the addresses geocoded and route parameters set, optimizing routes is a matter of clicking one button. Depending on how many stops need to be optimized, the process could take anything from a few seconds to an hour or so.

Algorithms and AIs aren’t perfect, and often an experienced dispatcher will see an opportunity to tweak and improve the final routes. Routific makes it easy to inspect routes and make changes. Over time, most route planners learn to trust the algorithm and let it do most of the heavy lifting.

Screenshot showing Routific with three optimized routes in Vancouver, Canada.

Choosing route optimization software

There are hundreds of different route optimization solutions on the market — Capterra’s Route Planning Software category lists 338 different routing solutions! The choice can seem overwhelming, but in fact every solution is designed for slightly different use cases. Capterra doesn’t split these sub-categories out, but in their route planning category you will find solutions for:

Delivery management (this is the subcategory Routific falls into)
Sales territory management
Fleet management and telematics
Field service management
Waste management
School bus routing
Truck and commercial vehicle routing

All of these industry verticals need very different things from their routing software! For example:

Delivery management solutions will include proof of delivery options and features to improve customer experience, like automated notifications and live tracking.
Sales territory management solutions will include links to CRM systems.
Field service management solutions often include options to allow customers to book a service online.
Fleet management solutions may focus on GPS tracking, ELD compliance, fuel consumption and fleet performance metrics.

The first step, then, is to identify which subcategory will best serve your business needs. Then make a list of the features you are looking for, and make a shortlist of software to test. Then sign up for a free trial of each one, and test it using your own data.
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You can sign up for a free trial of Routific here. And to get a quick overview of the main contenders in the delivery management category, check our article on the Best Route Planning Software for Businesses.

Getting the most out of Capterra

Capterra is a public review site where real users can rate software and leave feedback and testimonials. But you should know: By default, Capterra sorts its reviews so that “Sponsored” items are at the top. In other words, companies can pay for the top spot!

Change the sort order to “Highest rated” to get a more useful view of the list.

Screenshot of Capterra's Route Planning Software category, highlighting that you should sort by "Highest Rated" instead of "Sponsored".

Best Route Optimization Software for Businesses

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5 ways optimizing delivery routes could help the environment

Written by: Andrea Vale

As e-commerce booms, more products are shipped around the world. Global e-commerce sales ballooned from $1.34 trillion in 2014 to more than $3.35 trillion in 2019, according to the International Trade Administration. And that was before the COVID-19 pandemic online shopping surge hit, which drove unprecedented e-commerce growth: In a span of just three months, online shopping grew at the same pace as the previous 10 years. Pandemic-related shifts in consumerism have since driven global e-commerce above $5.4 trillion as of 2022, with projections expecting the worldwide market to reach $6.39 trillion by 2024.

Unfortunately, this new landscape of delivery has contributed to a near-unprecedented level of environmental pollutants. The transportation industry uses tonne-kilometers to measure the movement of 1,000 kilograms (about 2,205 pounds) of cargo over a distance of one kilometer (about .62 miles). In 2020, almost 140 billion tonne-kilometers of cargo were transported worldwide—26.8 billion via roadways—resulting in more than 3 billion cumulative tons of carbon dioxide emissions.

Transporting shipments by road may offer significant opportunities for sustainable transformation. Options range from the simple and intuitive—such as coordinated delivery timing or sharing warehouse and vehicle spaces—to the technologically innovative, like autonomous vehicles, deploying drones, or sending out delivery robots.

Using government sources, sustainability reports, and industry publications, Routific compiled a list of five ways optimized delivery routes can help the environment. Read on to learn how changes can be made along the supply chain to reduce the global footprint of the expanding e-commerce industry.

Source: Andrey_Popov // Shutterstock

Avoiding unbalanced route lengths—and empty vehicles

The transportation sector is responsible for 16.2% of all greenhouse gas emissions across all industries. Within that sector, road freight vehicles account for 40% of all transportation-related GHG emissions, which translates to 4-5% of all global CO2 emissions. Many freight vehicles take routes that are not optimized for efficiency—traveling long distances with comparatively little cargo or even none at all—thus squandering fuel.

There are a variety of ways to balance out routes to make them more carbon-efficient. Some companies are exploring opportunities to establish collection points to allow for a single route to result in a high-yield delivery. Others are seeking collaboration between companies to co-load and share vehicle space, thereby maximizing a vehicle's capacity and avoiding low or empty journeys.

Additionally, narrow delivery windows of only a few hours can also result in a route with a light load. They often force vehicles to depart at a certain time, even if there are only a few packages in tow. Widening delivery windows can allow freight vehicles to carry out more deliveries while expending less fuel.

Source: Milos Muller // Shutterstock

Prevent idling in traffic

Every year, the average freight truck spends around 1,000 hours idling. All told, this results in burning millions of gallons of diesel needlessly. Though much of this latent time occurs when drivers take necessary breaks to sleep or eat, a great deal happens when vehicles are stuck motionless in traffic.

To avoid this and reduce the amount of wasted fuel, drivers could rely on real-time GPS systems, which can alert them to heavily congested areas and reroute them along speedier roads. Employing night-time deliveries could also reduce freight traffic by 15%.

There are also a variety of idle reduction technologies designed to balance the internal engine heating and cooling that otherwise result in engines burning more fuel than necessary. These include energy-efficient HVAC systems and solar panel systems. In addition, automatic engine start-stop systems are already available in most new passenger vehicles and have demonstrated emissions reduction.

Two couriers handing packages to load the van.

Source: Gorodenkoff // Shutterstock

More deliveries per route

Optimizing the number of deliveries that can be made on a single route can save fuel and maximize energy efficiency. By encouraging companies to combine warehouses and loading facilities, they can better fill up a space's capacity, ensuring more goods and resources are ready for loading and delivery. Businesses can also use work-sharing delivery vehicles to ensure a single route meets its full potential capacity of goods delivered per route.

Another strategy could be consolidating deliveries to a few key areas, thereby reducing delivery emissions by as much as 30%. Package pickup lockers, like those used by Amazon, and shops that accept packages for multiple recipients could reduce congestion by up to 18%.

Source: BigBlueStudio // Shutterstock

Optimizing last-mile delivery

"Last-mile" or "final mile" delivery refers to the final leg of a product's journey when transported from the delivery vehicle to the consumer's front door. This delivery aspect often results in the most environmentally damaging practices, as consumers increasingly demand near-instant deliveries. Speedy deliveries put pressure on companies to prioritize speediness over fuel efficiency and can mean sending out a driver with a light load simply to meet a deadline.

Last-mile delivery is expected to account for a 36% increase in delivery vehicles in the world's 100 largest cities by 2030, resulting in delivery emissions rising by almost one-third. One researcher from the Hellenic Institute of Transport in Greece found that 20-30% of a city's overall CO2 emissions are already the direct result of last-mile deliveries.

Unlike a large cargo truck transporting many tons of goods to one location like a warehouse, last-mile deliveries require fleets of smaller trucks to hand-deliver each product to someone’s doorstep, adding up to many cumulative miles over individual trips. However, while final-mile deliveries present the most significant environmental damage during the product shipping process, they also pose the largest opportunity for sustainable improvement.

Stockpiling products to have them ready for last-minute orders or investing in carbon offsets (i.e., planting trees or installing solar panels to 'gain back' the CO2 emitted) can balance out negative impacts while still keeping consumers satisfied with delivery times.

Finally, customers shouldn't be underestimated. MIT researchers found that showing trees to buyers helped them visualize the carbon footprint of their purchases, motivating them to opt for less-urgent delivery times. Using a similar technique at checkout could possibly help companies reduce their last-mile emissions by 25%.

Source: Siwakorn1933 // Shutterstock

Launching supply chain technologies

There are a variety of developing technologies that can make packaging, shipping, and delivery more sustainable overall. Within warehouses and loading docks, digital freight matching services can optimize delivery by matching vehicles with the most efficient shipment load and route for their fuel capacity.

Once parcels hit the road, carbon-saving transport systems can be invaluable in reducing emissions. Electric vehicles with intelligent transport systems can alert manual drivers or self-driving vehicles to adapt their routes in order to save fuel. For more automated modes of transportation, some companies are looking into delivery drones and even robots. And some companies are taking a two-wheeled approach, as a single e-cargo courier bike alone could save 101,000 kilograms of CO2 per year compared to a delivery vehicle.

Summary

Route optimization can reduce mileage by 20%–40%, in turn reducing costs and emissions. It is the single most impactful lever a delivery business can pull overnight to increase its gross margins.

Author

Marc Kuo

Marc Kuo is the Founder & CEO of Routific, a route optimization platform for growing delivery businesses. With over a decade of experience in the last-mile industry, he has advised hundreds of delivery businesses on their route planning and delivery operations.

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Frequently Asked Questions

Can Google Maps optimize my route?

No, Google Maps cannot optimize your route. Google Maps is created for consumers who travel from A to B. But when presented a list of addresses to visit, Google cannot give you the optimized sequence of stops.

Here's an article that describes the relationship between route optimization and Google Maps.

How does route optimization software work?

Given a list of addresses, drivers, and constraints, route optimization software will calculate the best routes possible and present them on a map, and in the case of Routific, on a timeline view as well. You can then inspect the routes and make manual adjustments as you see fit.

What are the benefits of route optimization?

1. Automate route planning: the amount of time you spend manually planning routes every day can be better spent on growing your business.
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2. Optimize routes: with route optimization you can find 20% to 40% shorter routes, which directly reduces your cost per delivery (which is made up of fuel costs and driver wages).
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3. Reduce risk of tribal knowledge: most business owners that are just starting their delivery business often take on the burden of manual route planning. Over time, they acquire tribal knowledge on manual route planning. With route planning software, anyone can do it. And the business owner can finally take a vacation.
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4. Tracking KPIs: with route planning software you get the benefit of visibility, how much you’re driving each day, your cost per deliveries, how much mileage each car has accumulated, which makes fleet management easier too.
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5. Real-time driver tracking: most route optimization solutions come with a mobile app with GPS tracking, so you know where your drivers are in real-time.

Does route optimization take traffic into account?

Most route optimization software does not take traffic into account when creating the routes. They typically rely on average road speeds using mapping data from OpenStreetMaps. We discovered that the lack of traffic consideration leads to overoptimistic ETAs from OpenStreetMaps, which then leads to unrealistic routes your drivers cannot follow.

With Routific’s intelligent route optimization, we do take traffic into account. We have trained 179 machine learning models across the world to predict traffic patterns, which we then incorporate into the route optimization algorithm. We have seen ETA accuracies improve significantly – sometimes as much as an hour on an 8-hour route.

Are there any free route planning apps?

Yes, there are many free route planning apps available, but they each have limitations.

We have written an in-depth review that lists the 8 best free route planners.

Does route optimization software also include dispatch and customer notifications?

Routific’s route optimization software also includes delivery management functionality such as dispatching, live GPS tracking, and customer notifications.

Many other route optimization softwares also have this, though not all.

Is there a standalone route optimization API?

Routific has a standalone route optimization API, accessible via a stateless REST API, with wrappers available for Node, Ruby, and Python.

We also have APIs available that allows you to connect to our full end-to-end delivery management platform.

What's the difference between route planning and route optimization?

Route planning is the entire process that starts from having a list of addresses, creating a multi-stop routes (either manually or use route optimization algorithms), inspecting and editing those routes, all the way to dispatching those routes to your drivers, ideally on their mobile app.

Route optimization is a part of the route planning process, which seeks to automate the process – so you spend less time route planning – and to find the most efficient route possible, thereby cutting mileage and fuel costs.

For more details, see Route Planning vs Route Optimization.