Traveling Salesman Problem: It’s more than cost-efficiency

Travelling Salesman Problem: It's more than cost-efficiency

With a steep increase in on-demand deliveries, modern enterprises are  obsessed with rising costs. Being cost efficient is essential, but it is not the only  element that will differentiate you from your competition. 

Speed, agility, and transparency during delivery are the most crucial elements in supply chain logistics. These elements elevate customer experience. Finding the shortest route of travel between multiple destinations is crucial to enhance this customer experience. The traveling salesman problem comes into play here. 

Dropoff Annual Survey, 2018
99% of US consumers said that fast delivery of products is important for them, as they make online purchases. 

74% of US consumers prefer purchasing again from a company after receiving a  same day delivery of an item.

You can cater to such  massive demand only when you travel the most optimal route. Distance is the biggest constraint as you travel to multiple destinations across multiple cities. The constraint that the last-mile delivery staff face while finding the shortest route is called the Traveling Salesman Problem.

What is Traveling Salesman Problem? 

What is Traveling Salesman Problem?

Traveling Salesman Problem is a challenge that last-mile delivery agents face. It is an attempt to find the shortest distance to travel to several cities/destinations and return to  where you started from. Today, it is a complex issue given the numerous delivery-based constraints like traffic and so on. 

Solving the TSP challenge can make supply chains efficient and cut down the logistics cost. In short, TSP is an easy problem to define, but a difficult one to solve. 

Example: Imagine that there are three possible routes to complete deliveries in six cities. In six cities there may be 360 possible routes. You should not only find the most efficient path, but the one that works. You have to work out every possible route and pick the best one. 

The computational difficulty increases or multiplies as you add cities to the itinerary. Since the early 90s, people have been trying to solve this problem. 

Why is Traveling Salesman Problem challenging to solve? 

Why is Travelling Salesman Problem challenging to solve?

It is easier to solve TSP in theory because you have to find the shortest route for every trip within a city. But it becomes difficult to solve TSP manually as the number of cities increases. The permutations and combinations for 10 cities multifold. 

Adding five more cities can multiply these permutations and combinations. Hence, it may take months to solve this problem.

There is no possibility of finding such an algorithm that solves every TSP problem. Also, some constraints make TSP more challenging to solve. 

Constraints that make TSP more challenging

  • No automated records of on-demand deliveries and scheduled deliveries 
  • Traffic congestion
  • Sudden change of routes 
  • Last-minute order updates and requests 
  • Accurate delivery window timings 
  • Vehicle issues 
Route Constraints that make TSP more challenging

Well, all these constraints give a clear insight that TSP is a real-world problem. It is impossible to solve this challenge with even the best of your manual efforts. Thus, it is necessary to harness the power of technology to manage this TSP problem efficiently. 

How does Artificial Intelligence technology help in solving the Traveling Salesman Problem? 

Solving the Travelling Salesman Problem using AI

Today, the customer demand is steering the growth of modern enterprises. Consumers are demanding a higher-level of delivery service. 

Manpower alone cannot fulfil the variety of customer demands. Digital capabilities will determine the winners in the logistics and ecommerce market. These winners will know when and where to employ the right technology. Among different technologies, Artificial Intelligence plays an active role in solving TSP for modern enterprises.

AI combines human intuition with complex mathematics in real time. It analyzes a massive amount of data clearly and quickly. It mainly helps a modern enterprise to make operational, strategic and tactical decisions. 

Here’s how AI solved TSP in many modern enterprises. 

Optimized decisions for each vehicle and route

Traffic congestion added $74.5 billion to the overall operational cost of the trucking industry. It costs $6500 for every truck in a year. Eliminating 15% of congestion cost results in $11 billion in economic savings – Cost of congestion to trucking industry, 2016

Modern enterprises are striving hard to offer fast and efficient deliveries to their customers. Countering such a huge demand attracts higher delivery costs. Traffic congestion is one of the primary factors that cause a hike in delivery costs. Involving the right technology can save traffic congestion costs considerably and helps in finding the optimized routes for your vehicle. 

Check Out: The Definitive Guide to Logistics Route Planning

Using AI technology helps you execute accurate and timely deliveries. Its real-time updates lets you accommodate customer preferences in the given delivery schedule. Thus, you can travel shorter distances and strictly enhance your adherence to Service Line Agreement (SLA). 

Save fuel and labor costs 

Fuel incurs 25% of the total operational costs in maintaining a commercial truck. Labor costs incur 43% of the total operational costs in maintaining a commercial truck.- American Transport Research Institute, 2018 

Most modern enterprises incur 60%-70% of their total operating costs in fuel and labor. Managing fuel and driver costs has become a headache for modern companies. With the right use of technology, you can reduce these costs significantly. 

AI helps many modern enterprises to allocate deliveries based on distance and vehicle capacity. Its algorithms provide the quickest distance, thereby leading to reduced fuel and labor costs. 

Recognize the right addresses

When you are in a rush to complete your deliveries, unclear addresses can induce delays. If the driver is going to ride in an unfamiliar area, delays will increase. These delays can even cause missed deliveries.

Route optimization software uses AI to help logistics companies. It converts the addresses into geographic coordinates and provides the shortest route. It automatically updates the route, even if your customer changes the delivery address to an unfamiliar area. 

Making delivery turnaround time shorter

61% of customers want faster deliveries- The last mile retail study, 2018 

Today, customers’ satisfaction depends on on-time deliveries. They don’t prefer ordering from companies that do not stick to the Expected Time of Arrival (ETA). Also, the volume of customer demand is skyrocketing every single day. If modern enterprises do not cater to this demand, they stand to lose their customer’s trust. 

Shortest routes help in reaching the customers in a specified time window. AI solutions work out complex algorithms and provide the shortest route to complete your order requests on time. It auto-updates the quickest route in times of new delivery request, traffic, or change of routes. Hence, it reduces your delivery turnaround time consistently. 

Reducing Cost Per Mile 

The average cost per mile of trucks rose to $1.82 in 2019 from $1.69 in 2018.- American Transport Research Institute 

Logistics companies are aware about a crucial fact—around half of the total cost that logistics companies incur fall under first and last-mile deliveries. Hence, the logistics companies should focus on making transportation more reliable, efficient and quicker. 

AI technology takes into consideration all delivery constraints like traffic, vehicle type, tribal knowledge of the driver, etc, and provides the quickest route to reach a number of stops. Reduction in cost per mile cuts down your vehicle maintenance costs considerably. 

Improved productivity 

The impact of AI technologies will increase labor productivity by 40% in businesses- Accenture research on impact of AI

Predictive capabilities are crucial to plan your deliveries and improve productivity. AI technology enhances the predictive capabilities of modern enterprises. It improves your capabilities in areas of demand forecasting and capacity planning in a supply chain. These capabilities provide valuable insights that will help in running your logistics operations. 

The insights on current deliveries in different areas will help you know what to expect. These insights reduce the number of vehicles for delivery and directs them to locations, where higher demand is expected. 


As a modern enterprise, you should not only know the shortest route, you should also know the most cost-efficient route after calculating delivery constraints like traffic, change of routes etc. Route optimization provides optimized routes for last-mile delivery

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