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The modern customer is becoming more demanding and expects free and fast shipping, flexible options for deliveries, 360-degree visibility, and much more. However, challenges such as a vehicle routing problem can make it difficult to meet these expectations and improve customer experience. With the increase in parcel volume, the vehicle routing problem has become even more difficult to solve. Read on to learn what is vehicle routing problem and its most effective solution:
A vehicle routing problem is a combinatorial optimization and integer programming challenge of designing optimal routes for delivery vehicles considering constraints such as delivery time window and route length. It is entirely different from the traveling salesman problem, which intends to find the shortest route for delivery vehicles to cover all stops and then return to the starting point.
VRPPD involves the movement of goods from a specific pickup location to other delivery locations. Goods are picked from location A and delivered to location B; there are no depots involved in a vehicle routing problem with pickup and delivery. This type of routing results in paired pickup and delivery.
In this type of vehicle routing problem, delivery locations have specific time windows, and deliveries must be done within that time frame only. The challenge is to reach a customer within the given time frame. The order can be delivered beforehand but not after the delivery time window closes. Common complications in VRPTW are multiple time windows, disjoint time windows, and soft and hard time windows.
In CVRP, delivery vehicles have restricted carrying capacity for the goods that need to be delivered. The weight and volume of goods is considered while designing delivery routes. The aim is to reduce transportation costs by utilizing a vehicle’s capacity completely and delivering more goods in one trip. Some of the complications in CVRP involve multiple depots, different dimensions of goods to deliver/pick up, and different capacities of vehicles.
This variant of the vehicle routing problem allows vehicles to do more than one trip. It involves extra possibilities of having delivery vehicles engaged in backhauling. Moreover, multiple trips need to be planned in a single planning period.
In this type of VRP, vehicles are not needed to go back to the origin or the depot. The open vehicle routing problem is different from the classic vehicle routing problem as the vehicles are either not needed to report back to the depot or can return by revisiting the customers allotted to them in the reverse direction.
MDVRP allows a vehicle to start and end the trip from different depots. It increases the complexity and makes it challenging to plan delivery routes.
Traditional route planning practices involve manual intervention for order allocation, route planning and optimization, choosing a suitable delivery vehicle, and assigning tasks to riders. Dependency on human resources can also increase the risk of errors/inefficiencies and impact operational efficiency and profitability.
Not being able to track deliveries results in poor shipment visibility that further impacts delivery experience and productivity. It can also lead to delivery delays as drivers may make unnecessary stops, and businesses might not have an idea of it.
Inefficiently planned routes cause delays in deliveries, which further impacts customer experience and can also increase the chances of returns. As much as 69% of customers are less likely to shop from a retailer if the item is not delivered within two days of the promised delivery date.
Efficient delivery route planning and optimization can be done by considering multiple constraints such as weight and volume of products, vehicle capacity, real-time traffic and weather conditions, and more. Considering all of them while manually planning routes is not feasible.
Incomplete or incorrect delivery addresses are a major roadblock to the vehicle routing problem. It can lead to failed delivery attempts and, ultimately, RTO (Return to Origin), which adds to the already-high delivery costs. Moreover, it can also impact a driver’s productivity.
Miles that are covered when a vehicle carries no load (during delivery or return) are considered empty miles. Empty miles are a result of poor vehicle route planning and can lead to increased fuel consumption and costs.
Solving a VRP manually becomes increasingly challenging with the size of nodes or stops. For instance, a VRP with 5 nodes can be solved in seconds, whereas a VRP with 20 nodes/stops can even take years to solve. Relying on heuristic algorithms and matheuristic algorithms resolves the problem faster; however, it may require compromising optimality and precision.
Another way of solving this problem is via preset solvers. However, they can only solve specific fundamental restrictions and are suitable for academic contexts that require in-depth study. VRP is related to real-world situations that preset solvers cannot consider.
The best solution to VRP is to implement route planning and optimization solutions powered by advanced technologies like artificial intelligence and machine learning. Advanced route optimization solutions take into account real-time constraints such as traffic and weather conditions, historical data of routes, delivery location, delivery time window, and various other constraints to design the most optimal route. The solution offers following features to solve VRP:
An AI-based route optimization solution smartly clubs new orders with the ones that are already scheduled for delivery. As soon as the system receives a new order that needs to be delivered in the same direction as any previously scheduled order, the solution assigns it to the same driver.
An advanced route planning and optimization solution minimizes manual intervention and considers multiple parameters to design optimal delivery routes. It also facilitates multi-stop route planning and dynamic route planning to improve delivery productivity and minimize the number of trips.
This feature of a route optimization solution leverages the power of machine learning to overcome address inaccuracies. It converts text-based addresses into latitude and longitude to make them easy to locate.
With the real-time tracking feature, the route optimization solution allows all stakeholders to check the updated status of the consignment. A business can track all its consignments on a single window.
Data and analytics provide insights into various metrics such as the number of orders delivered, total distance traveled, number of failed deliveries or canceled orders, and more. Having such insights help businesses to make data-driven decisions.
Shipsy’s route planning and optimization software is powered by artificial intelligence and machine learning-based algorithms to design the most optimal routes and solve a vehicle routing problem. It considers multiple delivery-related constraints, such as multiple stops, weather conditions and traffic congestion, etc., in real-time while offering real-time tracking to all stakeholders. With this advanced route optimization solution, businesses can unlock benefits such as:
For more information or to get in touch with our experts, schedule a demo today!
The route planning software market is estimated to grow at a CAGR of 10.9% by 2025. Read to know how route planning software provides a smart solution to traditional route planning challenges.
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