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.

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.

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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.

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

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?

The TSP 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).

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.

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.

Finally, we realized 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.

Human considerations in route optimization

Traffic considerations are essential. Dispatchers often have detailed knowledge of local traffic patterns that can save hours during route planning.

Optimizing only for distance can lead to some drivers having longer routes or hours than others. Imbalances like this can be regarded as unfair.

Clean clusters are important as overlapping routes cause frustration among drivers. 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 planned routes by hand without any kind of route optimization and delivery management software. The manual method makes sense for startups and very small businesses, but as the complexity increases, the time spent on solving route puzzles gets expensive.

Routific’s AI solution is smart and fast. We quickly understood this was the best way.

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 miles.

Intelligent route optimization

Regular route optimization minimizes travel time or distance – but there’s more to it. Intelligent route optimization considers the human elements of route planning too.

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

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.