You probably never think about the science that goes on behind the scenes in your complex vehicle routing program – at least not when all is running smoothly.

However, no program is without flaws. What happens if the program you’re using no longer meets your requirements? Do you even know what your complex vehicle routing issues are, let alone how to describe them?

If not, you’ve come to the right place. We’ll go over all of the various routing software strategies that can help you schedule and route your deliveries. We’ll go into how your route planning software generates its results and how to figure out which vehicle routing features are lacking if your current solution isn’t cutting it.

Table of Contents

Vehicle Routing Problems Aren’t a New Phenomenon

vehicle routing problem

Any major scientific breakthrough stems from the need to solve a particular problem. Today’s software solutions for issues of complex vehicle routing are no exception. A more mundane problem took more than 150 years to solve properly before GPS and electronic spreadsheets.

The issue was as follows:

“How can you compute the shortest route possible that visits each city identified only once and returns a traveling salesman to his city of origin if given a list of cities/downs and their distances?”

Is this a dilemma you’ve seen before? It ought to. It’s the basic routing issue that has plagued multi-stop deliveries throughout history, from the Pony Express to today’s gig economy food deliveries. You’ll run into this issue if you need to find the shortest path between more than a few locations. Humans, like you, lack the cognitive ability to determine the most effective route quickly.

The Traveling Salesman Problem (TSP)

Traveling Salesman Problem

The Traveling Salesman Problem (TSP) was first published in a German-language traveling salesman handbook published in the 1830s. The TSP dilemma was discovered years ago when the most simple solution – moving from the closest point to the next closest point – failed miserably.

It doesn’t take long for the issue of the traveling salesman to force someone in charge of modern deliveries to seek out a software solution. The ease with which software today solves TSP belies the difficulty in finding a solution. It took over 150 years of research and development.

With the release of the open-source Concorde software in 1993, TSP was eventually put to rest. The app is still available for download and is free. Concorde was recently used to solve an 85,900-city routing issue with 100% reliability, despite the fact that it is still being refined.

Concorde has been modified and adapted in almost every routing framework available today. It is easy to determine the most effective routes on a network of roads with today’s computing technology. The keyword here is “easy.”

What if your company needs anything more dynamic? How do you, for example, determine the most effective routes to and from specific locations in real time? After all, traveling salesmen in the 1800s didn’t have to deal with much traffic. Yes, you do.

The Dynamic Vehicle Routing Problem

Vehicle Routing Problem

For many vehicle routing activities, static TSP solutions are no longer applicable. Vehicle routing issues have become more complicated as a result of third-party location data. Only a new model could solve this problem’s complexity. Anything capable of answering questions such as:

  • How do you update vehicle routes to make them more efficient in light of current traffic conditions?
  • How do the drivers reroute their deliveries to avoid delays caused by unexpected circumstances?
  • After a truck has left the factory, how do you update distribution routes?

Only third-party info, geo-intelligence, online databases, and millions of ants can solve today’s complex vehicle routing problems.

What is Ant Colony Optimization (ACO)?

Ant Colony Optimization

Ants are arguably the best route finders on the planet. Individual marching ants are used by ant colonies to search, capture, and return food to the colony, according to scientists. They are adept at determining the easiest, most direct route as well as rerouting around obstacles. But how do they manage it?

When an ant leaves its colony, it will wander around aimlessly, looking for resources (such as food) or pheromones left by other ants. Each ant searches for food in its own unique way.

Humans, on the other hand, have a tendency to do the exact opposite. We tend to perform our searches in a systematic manner. A community of humans searching for a specific resource is much more likely to send jobs in various directions. Sending people in particular directions is always inefficient – at least for ants – just as going from point to point in the Traveling Salesman Problem seems rational at first.

Using Digital Pheromones to Solve the Problem of Dynamic Vehicle Routing

Ants have a leg up on humans when it comes to navigation. When an individual ant discovers a resource that it desires, it will take a small amount and return to its colony. The ant emits a pheromone when it returns. This fragrance leaves a trail of crumbs for other ants to discover and track.

From there, the entire ant colony will dash back and forth to the resource, each ant contributing to the pheromone scent and reinforcing the most direct path. The potency of ant pheromone, on the other hand, diminishes over time. Longer routes emit weaker pheromones than shorter routes, causing ants to take the most effective path – biological route optimization.

Humans do not emit pheromones that can be detected by others. We’ve been leaving something better – digital footprints – since the turn of the century.

Ant Colony Optimization Algorithms Development

Ant Colony Optimization was developed by mathematicians and computer scientists as an extension of the static Traveling Salesman Problem (ACO). Since then, they’ve created route optimization algorithms to solve a variety of routing issues, including dynamic vehicle routing. Artificial intelligence and real-time mobile device geolocation are used for the most reliable solutions.

The general pattern of all ACO software algorithms is the same. Digital ants – data packets – are sent out by the program in a weighted grid. The grid is then built using a pre-existing road network. Each virtual ant moves around the grid at random. If it comes across the desired location (for example, a route destination), it returns to its starting point, resulting in an optimized route. Around the grid vertices it moves, the computerized ant leaves virtual pheromones. Based on the edges have the most pheromones, the path-finding program determines the best route.

Businesses and their fleets can make real-time changes when making deliveries with ACO. If, for example, a big traffic jam prevents your truck from making its next, most efficient delivery, ACO software will assist you in planning a new route – on the fly. To build a new, more optimized route, it will most likely use third-party traffic data, your distribution database, and possibly your truck inventory.

The availability of location-based data and third-party APIs paved the way for the development of newer, more efficient dynamic vehicle routing software. Businesses, on the other hand, introduce new challenges just as quickly as experts find solutions.

The Time-Dependent Vehicle Routing Problem

The main distinction between ant colony optimization and dynamic vehicle path optimization is unrelated to pheromones. It’s all about the amount of time and the amount of data.

An ant colony, unlike your delivery service, has thousands of delivery vehicles, each carrying a single item. Delivery services necessitate route optimization using a small number of vehicles with specific package capacities.

Punctual deliveries depend on well-managed routes with predetermined delivery capacities. Another example of scientists inventing a new problem by solving an old one is ACO, which is based on the time-dependent vehicle routing problem. How do you ensure that your planned deliveries are completed as quickly as possible?

ACO Solutions for the Time-Dependent Vehicle Routing Problem

ACO algorithms were rapidly adapted by scientists to improve dynamic vehicle route optimization. The new algorithmic problem was given the name time-dependent vehicle routing problem with time windows, which only a scientist could love (TDVRPTW). The answer to TDVRPTW can be found in the name. The ACO vehicle routing algorithms now include time windows and vehicle power data as variables.

Companies can optimize their deliveries into sequential routes by using delivery time windows, such as morning, midday, afternoon, and evening. As a result, ACO can be reconfigured to find the most reliable distribution routes over a period of time. It can also take into account vehicle capability to optimize the amount of deliveries that can be made in a given time frame.

Businesses that supply perishable goods or provide time-sensitive services can now use ACO-based applications with time windows. This program simplifies the process of routing various objects during the day by allowing routes to be easily changed and updated dynamically for particular time periods.

The Multi Ant Colony Routing Problem

ant colony optimization

When the Traveling Salesman Problem was finally solved in 1993, it took more than 150 years to solve. Given the complexities of the vehicle routing issues that have arisen over the last 30 years, it seems downright quaint. This issue was the new TSP until recently: What if you need to build routes for a large fleet of trucks that all service the same city but leave from different warehouses?

Researchers recently extended ACO algorithms to allow for multi-ant colony optimization, as reported in the European Journal of Operations Research. Companies may use multi ant colony optimization to solve the complex vehicle routing problem from multiple sources (for example, warehouses) at the same time. Vendors of routing applications will be able to build apps that will make company logistics easier as a result of this breakthrough. A salesman’s path does not need to be mapped separately any longer. A business isn’t limited to coordinating all of its salespeople’s routes from a single location.

With today’s tech, companies can schedule their entire fleets’ complex vehicle delivery routes in real time.

Your Dynamic Vehicle Routing Solution has Been in the Works for Over 150 years

Problems with vehicle routing are nothing new. For well over a century, mathematicians and computer scientists have been tackling them. Every time they make progress, they encounter new problems to solve. Researchers are developing software solutions to new vehicle routing problems just as quickly as they emerge, thanks to the power of post-Cold War technology.

EasyRoutes Route Planner

You now know how to recognize and clarify the various forms of complex vehicle routing issues that your distribution company can encounter in order to remain competitive. You’re aware of the issues and how complex routing tools will help the organization address them.

Please remember to think of the ants that made your dynamic vehicle routing app possible the next time you use it. And be thankful that scientists are still solving these routing problems at such a rapid rate. After all, the era of three-dimensional, drone-based dynamic distribution routing has arrived.