As a Product Manager who has worked on the development of delivery route optimisation software for 10+ years, I see that modern technologies can significantly improve the optimisation process and deliver better solutions. AI, machine learning, and other modern technologies have the potential to revolutionise the way delivery routes are optimised in the future.
With the increasing availability of data and the advancement of AI and machine learning algorithms, it is becoming possible to develop more sophisticated prediction models that can be integrated into optimisation algorithms to make more accurate and informed decisions about route planning and scheduling. Machine learning algorithms can be trained to predict customer demand based on historical sales data and other market trends, allowing businesses to optimise their delivery schedules and routes accordingly. AI can also be used to optimise delivery schedules based on customer preferences and other relevant factors.
Blockchain technology could be used to create a secure, decentralised database of information about deliveries, including information about the products being shipped, the route they are taking, and the status of the delivery. This could help increase transparency and accountability in the delivery process as well as reduce the risk of fraud and theft.
Internet of Things (IoT) devices, such as sensors and GPS trackers, may collect real-time data about delivery vehicles and their surroundings. This data could be analysed and used to optimise delivery routes in real time, as well as to track the location of deliveries and monitor the condition of the products being shipped.
The use of real-time data and sensors can allow for more dynamic and adaptive route planning. By constantly monitoring traffic, weather, and other factors, delivery companies can adjust their routes and schedules in real time to avoid delays and optimise delivery times.
Overall, AI, machine learning, and other modern technologies have the potential to significantly improve the efficiency, accuracy, and sustainability of delivery route optimisation. As these technologies continue to evolve and become more widely adopted, we can expect to see significant improvements in the delivery industry in the future.
However, the question remains: can AI do all the delivery route optimisation without human intervention now? Is it just a matter of time before AI fully substitutes a human in the delivery route optimisation process?
AI-based delivery route optimisation software can create optimal routes in seconds, a task that would take humans hours or even days to complete. Additionally, these systems can consider and process vast amounts of data and provide real-time updates on the best routes.
Despite these advantages, there are still limitations to the use of AI in the delivery route optimisation process. For example, AI may not have the contextual knowledge and intuition that humans possess. Humans can make decisions based on experience, personal preferences, or ethical considerations, which AI may not be able to replicate.
Moreover, AI-based delivery route optimisation systems require significant data input and maintenance to work effectively. The accuracy and reliability of these systems depend on the quality and quantity of data they receive. Human intervention may be necessary to ensure that the data provided to the system is correct and up-to-date.
Therefore, while AI can optimise delivery routes effectively, it is unlikely that it can fully substitute humans in the delivery route optimisation process. A combination of AI and human expertise is likely to produce the best results. As technology advances, AI may be able to handle more complex delivery route optimisation tasks, but for now, humans remain an essential part of the process.
Moreover, it is important to carefully consider the potential risks and challenges associated with modern technologies and to implement them in a way that is safe, ethical, and sustainable.
Is route optimisation a hard job?
Efficient delivery route planning can help logistics companies reduce transportation costs, minimise delivery times while maximising the number of deliveries, and improve customer satisfaction. The optimisation of delivery routes can lead to significant cost savings, improve resource utilisation, and enhance the overall performance of logistics operations. The optimisation process involves finding the most cost-effective and efficient routes for delivery drivers, taking into account various factors such as distance, time, traffic, and vehicle capacity. That is why the delivery routes optimisation problem is crucial in logistics. Therefore, logistics companies need to invest in delivery route optimisation solutions to improve their competitiveness and profitability.
The delivery route optimisation process is complicated due to the number of variables involved, the need to balance competing priorities, and the constantly changing nature of delivery operations. Effective route optimisation requires sophisticated software, careful planning, and ongoing monitoring and adjustment to ensure that routes remain efficient and cost-effective over time.
The rise of advanced technology has led to the development of Route Optimisation Software that can significantly improve the efficiency of the delivery process. However, this has raised questions about whether it is possible to fully automate the delivery process and whether a human is still a necessary part. This article examines the above theses in more detail. Let me begin with the keystone of delivery route optimisation.
Types of goods
Different companies have different requirements for their delivery processes, depending on the type of goods they are shipping, the industry they operate in, the markets they serve, and their customers' needs. For example:
● Perishable goods, such as fresh produce, dairy products, or pharmaceuticals, have very strict requirements for temperature control and delivery speed to ensure that the products are delivered fresh and in good condition. Specialised transportation and storage facilities are the ground rules for them.
● Companies that ship high-value goods, such as jewellery, cash, or art, need to take extra security measures. Delivery routes should minimise the risk of theft or damage.
● Fragile goods, such as electronics, glassware, or art need to be protected during transportation by specialised packaging materials. Shipping companies plan delivery routes avoiding bumpy roads or other conditions that could damage the goods.
● To deliver heavy or oversized goods, such as construction equipment, vehicles, or furniture, companies use specialised transportation methods and equipment, such as flatbed trucks or cranes. The delivery route should avoid low bridges or other obstacles that could prevent the goods from being damaged.
To optimise their delivery processes, companies need to take these factors into account and develop customised strategies that meet their specific requirements.
Delivery types
When it comes to delivery route optimisation, the specific type of delivery service being offered can have a significant impact on the approach that is used. A delivery driver may have to visit dozens of stops in a single day, each with its own set of constraints and requirements. Some deliveries have to be made during specific time windows, while others may require special handling. Several types of delivery services exist, each with its unique characteristics and challenges:
● Point-to-point delivery is the most basic type of delivery service, where a single item or a small package is transported from one location to another.
● Last-mile delivery involves transporting goods from a local hub to the final destination, which is often a residential address.
● Same-day delivery means that goods should be delivered on the same day they are ordered. This type of delivery is becoming increasingly popular in the e-commerce industry.
● Scheduled delivery involves delivering goods at a pre-scheduled time, such as regular delivery of goods to a retailer or a grocery store.
In summary, different types of delivery services have different characteristics and requirements, and the choice of optimisation algorithm and approach will depend on the specific context and constraints of the delivery operation.
Conditions and human factors
Another challenge is the constantly changing conditions that affect delivery routes, such as road construction, bad weather conditions, and traffic patterns. These conditions can make it difficult to predict how long it will take to travel from one stop to another, making it challenging to plan the most efficient delivery route.
When something goes wrong in the delivery process, the delivery route and schedule should be rearranged to ensure that deliveries are made as efficiently as possible.
One way to rearrange it is to use specialised software that can recalculate the route and schedule and determine the best alternative based on the new parameters. This may involve resequencing the delivery orders, adjusting the delivery windows, and assigning new drivers or vehicles to the affected routes.
One of the most unpredictable things for any planning is the human factor. The behaviour of the driver, such as speeding, taking breaks, or deviating from the route, can influence the schedule. Moreover, if a driver is experiencing a health issue, they may not be able to drive safely, which can delay or disrupt the delivery process and put the driver at risk of car accidents. Accidents also cause delays, damage to the goods being delivered, and even result in injuries to the driver. This leads to additional costs for the business, and can negatively impact customer satisfaction.
To mitigate these risks, businesses should have contingency plans in place in case of driver illness or accidents, such as backup drivers or alternate delivery routes. Nevertheless, the human factor is usually not taken into account as a formula or a coefficient when calculating delivery routes. While optimisation algorithms and route planning software can take into account factors such as traffic patterns and vehicle capacity, they cannot directly incorporate the human element such as driver preferences, needs, or limitations.
Tools and approaches
As you can see, one of the most significant challenges associated with optimising delivery routes is the sheer number of variables that need to be considered. Special methods, approaches, and sophisticated software systems help to solve this problem:
● Geographic Information Systems (GIS) is software that is used to map out delivery locations and visualise the optimal routes, taking into account factors such as traffic, road conditions, and delivery schedules. GIS can also be used to analyse data and optimise routes based on factors such as distance, time, and cost.
● Real-time traffic data are used to optimise delivery routes based on current traffic conditions, which can help minimise delivery times and reduce fuel consumption.
● Vehicle telematics provides real-time data on vehicle location, speed, and other factors that can be used to optimise delivery routes.
● Data on customer location, delivery preferences, and order history can be used to optimise delivery routes and improve customer satisfaction.
● Optimisation approaches, such as divide and conquer nearest neighbour, and branch and bound can be used to optimise delivery routes manually, although this approach can be time-consuming and may not be suitable for complex problems.
● Human Expertise can also play a role in optimising delivery routes. Experienced logistics professionals can use their knowledge and expertise to identify opportunities for route optimisation, such as consolidating deliveries or rerouting vehicles to avoid traffic congestion.
There is a golden rule for any logistics company, that successful optimisation of delivery routes can be achieved only as a combination of all those methods and approaches. By utilising these tools, businesses can improve the efficiency and cost-effectiveness of their delivery operations, while also enhancing customer satisfaction.
Route optimisation software and algorithms
Route Optimisation Software is designed specifically for optimising delivery routes and combining all the best practices. This software uses algorithms to calculate the most efficient delivery routes based on a range of factors, including the number of stops, vehicle capacity, and delivery priorities. These software solutions use algorithms that can consider all of the variables and constraints:
● Nearest Neighbour Algorithm selects the closest delivery point to the current location and repeats the process until all delivery points have been visited.
● Clarke-Wright Algorithm is a heuristic method that builds routes by combining two delivery routes that are close to each other.
● Genetic Algorithm uses a genetic approach to create optimised routes. It works by creating a population of potential solutions and using selection, mutation, and recombination techniques to evolve the population toward the best solution.
● Ant Colony Optimisation is based on the behaviour of ants, which lay down pheromone trails to communicate with other ants. In this algorithm, many artificial ants are used to build routes by laying down and following pheromone trails.
● Simulated Annealing is based on the process of annealing in metallurgy, where a metal is heated and slowly cooled to reduce defects. In this algorithm, a solution is heated (or perturbed) and then cooled (or optimised) to find the optimal solution.
Another challenge is to choose the most relevant algorithm. The Nearest Neighbour Algorithm is suitable for small-scale problems and can quickly generate solutions. Otherwise, Genetic Algorithm or Ant Colony Optimisation is better to use for larger and more complex problems. Speaking of perishable goods, Simulated Annealing requires more time to produce optimal solutions, but it is usually used for problems that require maintaining the temperature of the goods being transported. Meanwhile, Clarke-Wright Algorithm effectively solves problems with vehicle capacity constraints.
These algorithms and others can be used in combination and the optimal algorithm may vary depending on the specific needs of the business and the problem at hand. Nevertheless, a mathematically optimised delivery route is not always equal to business-optimised logistics. While mathematical optimisation techniques can be very effective at identifying the most efficient route based on mathematical criteria, they may not always take into account real-world factors that can impact the delivery process.
For example, a mathematically optimised route may not take into account the preferences or requirements of individual customers, which could impact their satisfaction with the delivery service. Additionally, a mathematically optimised route may not take into account the availability of drivers or vehicles or other practical constraints that can impact the delivery process.
In some cases, there may be no optimal mathematical solution for a delivery route optimisation problem. This can occur when the problem is highly complex or when multiple competing objectives need to be balanced, such as minimising delivery time, reducing costs, and maximising customer satisfaction. In these cases, it may be necessary to use heuristic or simulation-based approaches, which can provide approximate solutions that are good enough for practical purposes.
Heuristics is a class of problem-solving techniques that are practical and useful when the optimal solution is unknown or difficult to calculate. They work by providing approximate solutions to complex problems in a reasonable amount of time.
One of the main advantages of heuristics algorithms for delivery route optimisation is that they can quickly generate good-quality solutions that are close to the optimal solution. In many cases, heuristics algorithms can find solutions that are almost as good as the optimal solution in a fraction of the time required by more computationally expensive optimisation algorithms.
Another advantage of heuristics algorithms is that they can be designed to incorporate practical constraints and requirements that are specific to the delivery route optimisation problem being solved.
To illustrate how it all works in real life let’s take a look at Maxoptra. It is a cloud-based software solution that is designed to help businesses optimise their delivery routes and schedules. Maxoptra’s main features are automated scheduling, real-time tracking, and dynamic routing, which can help businesses improve their operational efficiency, reduce delivery times, and minimise costs.
Maxoptra utilises a range of algorithms and techniques to optimise delivery routes, including heuristic algorithms, genetic algorithms, and Monte Carlo simulations. The software takes into account a variety of factors when calculating routes, including traffic patterns, weather conditions, road closures, and vehicle capacity.
One of the unique features of Maxoptra is its ability to dynamically update routes in real time, based on changing conditions. For example, if a driver experiences a delay or traffic jam, the software can automatically reroute the driver to the most efficient path to their destination. Therefore, only algorithms, including heuristics algorithms, in combination with human knowledge and expertise can provide the most effective solutions for delivery route optimisation.
Meet the author: Aleksei Badianov
Aleksei Badianov is an accomplished and respected professional in the field of Product Management, known for his impressive achievements and exceptional leadership. With a primary focus on developing delivery route optimization solutions, Aleksei has successfully served numerous delivery and distribution companies across 12 countries. Through his innovative approach, he has optimized an impressive daily volume of over 100,000 deliveries, resulting in significant environmental benefits by reducing CO2 emissions by a remarkable 1,631,320 kg annually.
Beyond the tangible results, Aleksei's impact goes far beyond numbers. He has played a pivotal role in driving positive transformations within leading companies and cutting-edge startups. His unwavering commitment to personal and professional growth has kept him at the forefront of the rapidly evolving Product Management landscape.Throughout his career, Aleksei has consistently showcased his exceptional leadership skills by effectively leading teams of various sizes, ranging from 10 to 50 members, in both dynamic startup and corporate environments. His deep understanding of Product Management principles, coupled with his visionary approach, has yielded remarkable achievements and successful outcomes.
Aleksei's contributions and expertise have garnered recognition from esteemed Product Management communities, prestigious educational institutions, and mentoring platforms. He has become a trusted advisor and mentor, generously sharing his knowledge and insights to guide and empower aspiring Product Managers on their own career journeys.
With an unrelenting drive for improvement and a passion for innovation, Aleksei Badianov remains a highly sought-after professional in the Product Management field. His exceptional contributions, groundbreaking solutions, and unwavering dedication to personal growth stand as a testament to his remarkable abilities and enduring commitment to making a positive impact within the industry.
Top comments (2)
Articles like these are great 👍 very good explanation of the problem and the solution! Many thumbsup 👍👍👍
Excellent
Thank you, Yury! This problem is really worth solving!