Dynamic Vehicle Routing Problem: How Do You Solve It?

The efficient distribution of goods involves the determination of routes so that the overall distribution cost is minimized. The goal of routing is to create a cost-effective route by minimizing the distance and travel time.

It involves various constraints of operation like vehicle capacity, time window (delivery or pick up), route length, vehicle speed, and availability of the vehicle. The Vehicle Routing Problem (VRP) helps fleet managers to plan routes that minimize last-mile delivery costs and improve vehicle efficiency.

If you want to learn what dynamic routing problems are and how to identify them, you have found the right article. Here, you will come to know about different technical approaches used in routing software while planning routes.

What are Vehicle Routing Problems?

Every tremendous scientific advancement arises from a specific problem that needs to be solved. Today’s software solutions to dynamic vehicle routing problems are no exception. Before GPS and computer spreadsheets, a more mundane dilemma took more than 150 years to solve adequately.

Vehicle Routing Problem is a common problem faced by the delivery services carrying out last-mile deliveries. There are two types of routing issues namely Dynamic and Static Vehicle Routing Problems. The DVRP is one of the variants of VRP. Dynamic is much harder to solve than static issues.

The main objective of the VRP is to minimize the total cost of the routes which can be resolved by advanced logistics tools. These tools not only resolve routing issues but also reduce overall transportation costs and improve delivery experiences for customers.

Vehicle routing requires technological support for real-time communication between the vehicles and the decision maker. Any time you want to figure out the shortest route among more than a handful of places, you will run into this problem. Humans, including you, are cognitively incapable of determining the most efficient route quickly.

A routing platform with advances in communication and technologies like machine learning, IoT, and predictive analytics is the best way to address VRP. It takes into account business constraints like vehicle availability, resource limitations, and time constraints.

What is Travelling Salesman Problem (TSP)?

The Travelling Salesman Problem (TSP) is a German-language salesman handbook that originated in the 1830s. The TSP problem was identified because the solution of traveling from the closest point to the next closest point didn’t work.

It took over 150 years of scientific endeavor for the traveling salesman problem to drive anyone managing modern deliveries to look for a software solution. The simplicity with which software solves TSP today belies how difficult it was to find a solution. 

TSP was finally kicked to the curb in 1993, with the release of the open-source Concorde program. The software remains free and downloadable. Concorde was recently used to solve city routing issues with 100% efficiency.

In almost every application for vehicle routing available today, modifications of Concorde can be seen. Determining the most efficient routes from a network of roads has become simple with technological advances. 

What about when your business needs something more dynamic? For example, how can you find the most efficient routes to and from particular locations in real time? 

What is a Dynamic Vehicle Routing Problem?

Static solutions to TSP are now irrelevant for many vehicle routing tasks. Third-party location data have increased the complexity of vehicle routing problems. This complexity and dynamic objective function could only be solved using a new model and approach. Something robust enough to answer questions like:

• How can you update vehicle routes to make them optimal based on current traffic or random times?
• How can your drivers reroute their deliveries to circumvent unforeseen delivery issues?
• How can you update delivery routes after a vehicle has left the warehouse?

Today’s dynamic VRP can only be solved with third-party data, geo-intelligence, and online databases.

Ant Colony Optimization (ACO): A Primer

Ants are, arguably, the world’s best route finders. They are efficient at finding the shortest, most direct route and rerouting around obstacles. Scientists have long analyzed how ant colonies use individual marching ants to find, collect, and return food to the colony. But how do they do this?

When an ant leaves its colony, it will wander completely randomly, searching for resources (e.g., food) or pheromones left by another ant. Each ant literally goes its own way in search of food.

Humans tend to do the opposite. It’s much more likely a group of humans looking for a particular resource will send workers in different directions. Just as moving from point to nearest point in the Travelling Salesman Problem seems rational at first, sending people in specific directions is often inefficient.

Solving the Dynamic Vehicle Routing Problem with Digital Pheromones

Ants have a wayfinding advantage over humans. When an individual ant finds a resource it wants, it will grab a bit and immediately return to its colony. As the ant returns, it emits a pheromone. This scent acts as a trail of crumbs that other ants can subsequently smell and follow.

From there, the entire ant colony can make haste back and forth to the resource – each ant adding to the pheromone scent, reinforcing the most direct route. Longer routes emit weaker pheromones than shorter ones, which leads ants to follow the most efficient route – biological route optimization.

Humans don’t leave pheromones that others can follow. Since the turn of the century, we leave something better – digital footprints.

The Development of Ant Colony Optimization Algorithms

Mathematicians and scientists expanded on the Travelling Salesman Problem by exploring ACO. They have since developed route optimization algorithms to solve various optimization problems, including dynamic vehicle routing. The most robust solutions make use of artificial intelligence and real-time mobile device geolocation.

All ACO software algorithms follow the same general pattern. The software sends out virtual ants (data packets) along a weighted grid. Each virtual ant wanders the grid randomly. If it stumbles upon the desired location (e.g., a route destination), it returns directly to its origin – creating an optimized route. 

The computerized ant leaves virtual pheromones along the grid vertices it travels. The route-finding software calculates the optimized route based on which edges have the most pheromones.

ACO allows businesses, and their fleets, to make real-time adjustments while carrying out deliveries. For example, if there is a major traffic jam, ACO software can help you plan a new route on the fly. It will likely use third-party traffic data, your delivery database, and perhaps your truck inventory to create a new, more optimized route.

The availability of location-based data, and third-party APIs, allowed newer, more powerful dynamic vehicle routing software to be created. Almost as fast as researchers find solutions, however, businesses present new issues to solve.

The Time-Dependent Vehicle Routing Problem

The key difference between ant colony optimization and dynamic vehicle route optimization isn’t pheromone-related. It’s about time supply systems and volume.

Unlike your delivery service, an ant colony has thousands of delivery vehicles, each worker ant carrying a single package. Quality of service requires optimizing routes based on a limited number of identical vehicles with certain package capacities.

Punctual deliveries depend on efficiently managing routes with fixed delivery capacities. ACO represents another example of scientists creating a new problem by solving a previous one – the time-dependent vehicle routing problem. How do you make sure your scheduled deliveries are as efficient as possible?

ACO Solutions to the Time-Dependent Vehicle Routing Problem

Scientists quickly began to adapt their ACO algorithms to improve dynamic vehicle route optimization. The new algorithmic problem was given a name only a scientist could love: time-dependent vehicle routing problem with time windows (TDVRPTW). Time windows and vehicle capacity data were added as variables to the ACO vehicle routing algorithms.

Delivery time frame– e.g., morning, midday, afternoon, evening – allow companies to optimize their deliveries based on customer demand. ACO can therefore be reconfigured to find the most efficient delivery routes across a sequence of time periods. It can also consider vehicle capacity constraints to maximize the number of deliveries possible in a specific time window.

Today businesses delivering perishables or time-specific services can incorporate ACO-based software with time windows considering transportation mode. Using this software breaks down the complexity of routing different items throughout the day, allowing routes to be easily adjusted and modified dynamically.

The Multi Ant Colony Routing Problem

The Travelling Salesman Problem took more than 150 years to resolve when it was put to rest for good in 1993. It seems downright quaint given the complexity of the routing issues that have arisen over the past 30 years. 

Multi ACO allows companies to solve the dynamic vehicle routing problem from multiple origins (i.e., warehouses) simultaneously. This breakthrough will allow routing software vendors to create apps that can simplify company logistics. No longer does a salesman’s route need to be plotted individually. Nor is a company limited to planning the routes of all the sales people from a single office.

Today’s software allows businesses to plan their entire fleets’ dynamic vehicle delivery routes (transportation process) with real-time sync in approach. The performance criteria is based on the either expected wait time, customer visits or customer demand serviced successfully. 

How does Upper help to Create Dynamic Vehicle Routes?

Upper Route Planner is an efficient route planning software to streamline delivery operations. It reduces the manual work of planning routes through a fully automated process.

Customers also receive automatic notifications of delivery status and the estimated time of arrival (ETA). This ensures guaranteed on-time delivery to avoid missed time windows and thus poorer service. Some of the other benefits of Upper are:

• Increased earning potential with maximized productivity
• User-friendly interface
• Cost-effective delivery (low fuel usage)
• Tracks and analyses driver’s performance
• Improves customer experience

At Upper, the electronic proof of delivery (ePOD) is recorded through an electronic signature or photograph. It helps ensure that the package is in the right hands and marks a great service. Therefore, the Upper Route Planner is the right approach for planning dynamic routes.

To learn more details about how Upper can help your business, sign up for a 7-day free trial today!

Conclusion

Vehicle routing problems are nothing new, researchers are finding software solutions to dynamic VRP as fast as impatient demand arises. The demands for service arrive based on the operator cost and quality of service perceived by the users. Now that you know how to identify and explain what types of dynamic vehicle routing problems your delivery business may face to stay competitive. 

The next time you want a dynamic vehicle routing app, please pause a moment and think about Upper Route Planner. This software is ideal for all types of delivery businesses and helps to improve deliveries and keep their customers satisfied. Upper has all in-built routing features for performing multi-stop deliveries. 

To know more about our route planner, you can book a demo now.