Vehicle Routing Problem (VRP) is a common challenge delivery stakeholders face when executing their last-mile operations. It can be addressed leveraging advanced digital logistics tools. These tools not only resolve complex routing problems but at the same time optimizes overall transportation costs and improves the delivery experience a brand provides.
What is Route Optimization and How Does it Address the Vehicle Routing Problem?
Route optimization is key with regards to addressing the VRP. Route optimization is the term used for the overall process of determining the most suited route for the delivery of goods. This well-suited route is not only the shortest route between points A and B but in fact, it is the route that is most optimal in terms of costs, the number and location of all stops on the route, arrival/departure time gap, effective loading and similar factors alike.
What is the Vehicle Routing Problem?
VRP is the problem of determining an optimal route from one originating point (point A) to a various set of destinations (points X, Y, Z) while accounting the business-specific constraints like vehicle and resource limitations, time windows, etc. for each route. A modern routing platform powered by disruptive technologies like Machine Learning, IoT and predictive analytics is your best bet to addressing the vehicle routing problem.
The Traveling Salesman Problem
A classic example of the vehicle routing problem would be the 'Traveling Salesman Problem (TSP)'. TSP is the issue of determining the shortest possible route between destinations, without having them to travel the same route twice. Though there was no solution to address the traveling salesman problem back in the 1800s, owing to technology limitations, things have changed now. thanks to rapid evolution in logistics technology.
Evolution of the Vehicle Routing Problem
The Vehicle Routing Problem (VRP) was first reported some 50 years ago by Dantzig and Ramser under the title "The Truck Dispatching Problem." Needless to say, the world and technology have advanced to unimaginable levels over these past 50 years. While stakeholders relied on manual planning of routes and manual tracking of fleets in the olden days, all processes are now much more sophisticated, automated, and technologically enhanced in the present day.
Having said that, customer expectations and the competition for logistics stakeholders have also been on the rise over years, making it imperative for the business owners to digitalize the core delivery routing operations. Digitalization is not only important in order to meet the business objectives, but also to maintain relevance, to meet customer expectation, and to be able to effectively scale the operations.
What are the Top 5 Vehicle Routing Problems?
From increasing dependency on manual processes, lack of real-time tracking, faster delivery expectations to inability to eliminate avoidable delays, vehicle routing problems have been weighing down logistics operations for a long time now. Here are some of the major challenges.
Vehicle routing problem #1: Increasing dependency on manual intervention
As a logistics company dependent on the manual routing of vehicles, one is bound to face the brunt of inefficient performance and relevance. You might have the best route planners, but again, they are still human. Manual dependencies act as a catalyst when it comes to increasing the impact of the vehicle routing problem. Fleet managers responsible for manual routing increasingly face the challenge of responding to customer demands including short lead times and convenient delivery slots. As an efficient solution, while companies should bring in automation to route planning, manual route planning is heavily relied on which increases the dependency on manual intervention.
Vehicle routing problem #2: Lack of real-time tracking and tracing
Inability to track delivery fleets in real-time results in poor logistics visibility. This inturn leads to bad delivery experiences, inadequate SLA adherence, increased logistics risks, and unmanageable financial dents.
Vehicle routing problem #3: Shrinking delivery turnaround-time
With people getting used to being indoors, the demand for same-day (or at least 1-day) delivery is on the rise. It's almost a necessity now! While as many as 86% of customers are willing to pay more for a preferable customer experience, according to the Digital Commerce Institute, "77% of shoppers are willing to pay more for same- or next-day delivery. Delivery is a key purchase decision point and consumer expectations." A survey by PwC further revealed how "older shoppers may place an even greater premium on narrow delivery time slots." Hence, especially in a pandemic scenario where time is valued more than money, it is difficult for logistics companies to manage a vehicle routing problem. Social distancing amongst other guidelines only adds to the problem.
Vehicle routing problem #4: Inability to eliminate unnecessary delays
As a result of a lingering lack of visibility, organizations often have to pay the price of unnecessary diversions and unplanned stoppages taken by the drivers on-road. Manual dependencies like manual route planning, manual allocation, and scheduling of delivery jobs further add to the delays. Using a sophisticated, automated vehicle routing solution can create optimal routes that not only help tackle the unaccounted delays but also address the urgency to reduce fuel consumption.
Vehicle routing problem #5: Growing need to reduce fuel consumption & carbon emissions
The carbon crisis and global warming are real existential challenges. As aggregators of a logistics supply chain, it only becomes necessary to comply with the regulation around carbon emissions and to embrace greener means of executing logistics activities. A manual or traditional vehicle routing technique, by virtue of the lack of machine learning capabilities, makes it impossible to achieve this goal.
Can Free Tools be a Vehicle Routing Problem Solver?
Free tools including basic map applications cannot solve the vehicle routing problem as the only solution to this issue is the optimization of routes which is not possible using free tools. Free tools and apps can effectively help you find the quickest route from point A to point B, however, they cannot optimize these routes by considering external factors like weather, one-ways, empty miles and more. For example, you can find a route that takes only 30 minutes from point A to point B via a free tool; however, you cannot find an optimal route from point A to point C (with a stop at B) accounting for on-road conditions using free routing tools or manual methods.
An effective vehicle routing problem solver would be a full-stack product that integrates AI with automation to find optimal routing solutions.
What is the Pickup and Delivery Vehicle Routing Problem (PDVRP)?
A very common problem or complication arising as a part of the vehicle routing problem is pickup and delivery vehicle routing problem (PDVRP). PDVRP is the problem that occurs due to simultaneous delivery and pickup attempts. The problem is determining and assigning a route for delivery as well as pick up of goods while optimizing the length of the route.
The objective of the vehicle routing problem with delivery and pickups is to be able to serve multiple suppliers within a specific, pre-determined time window at a minimum cost, while ensuring factors like vehicle capacity and total trip time for each vehicle are not violated.
An easily understandable example of a vehicle routing problem is experienced by on-demand food delivery like UberEats which has to assign drivers to restaurant locations. These companies must effectively assign delivery tasks to drivers in close proximity to the food-serving restaurant as well as the location of the customer. This simultaneous assignment has to be done in a way such that it takes the drivers the minimum possible time to arrive at the restaurant, to pick up the food, and deliver it to the drop location. Grocery and food delivery is one such industry where pickup and delivery vehicle routing problems become critical.
What is the Capacitated Vehicle Routing Problem (CVRP)?
Solving the capacitated vehicle routing problem (CVRP), a challenge under the umbrella of the vehicle routing problem, has gained great interest over the decades and as customer bases grow, it's getting critical. CVRP emerges because of established vehicle capacity limitations. It's a challenge that can be solved by planning delivery trips for vehicles in such a way so that an optimal number of customers can be catered to in the most efficient way. But achieving this is not easy.
The number of possible customer-route combinations to ensure highly efficient deliveries, simply explode and the scale of possible combinations grows exponentially as customers increase. It's close to impossible for a human brain to consider hundreds of possibilities and determine the most efficient combination that solves the capacitated vehicle routing problem. However, advanced analytics tools driven by self-learning algorithms and artificial intelligence can compute such complex possibilities and generate accurate results. Therefore, digitalizing routing processes become key when solving the capacitated vehicle routing problem.
What is Vehicle Routing and Scheduling?
Travel or transportation activities have a significant impact on margins for businesses that are extremely dependent on fieldwork, for instance executing home deliveries and long haul deliveries. From paying for fuel, maintaining vehicles to paying the road warriors, transportation costs can become a significant problem. While there are some areas where one cannot do much about costs, there are multiple aspects of logistics where costs can be significantly optimized and reduced. Vehicle routing and scheduling is one of them.
Vehicle routing refers to the process of discovering the most efficient route that connects multiple spots or delivery drop-off locations. Scheduling includes strategies that help arrange locations or stops in the best possible way by considering a plethora of factors like traffic, one-ways, empty miles, fuel efficiency, customer availability, driver productivity, and weather. By automating vehicle routing and scheduling processes, not only can businesses drastically optimize costs they can also ensure high-levels of delivery productivity.
Why is Solving the Vehicle Routing Problem Critical to Customer Experience?
The robust technology-oriented solutions and tools that help tackle the vehicle routing problem provide the end customer with a range of services that eventually contribute to enhancing the overall customer experience.
Vehicle routing tools offer end-to-end visibility of delivery progress, generates highly accurate ETAs, seamlessly accommodates last-minute changes in delivery location and/or time easily, informs the customers in advance of any potential delays, ensures a quick turnaround time, and even promotes the possibility of same-day deliveries. All of these factors enhance the customer experience and add to customer satisfaction, helping boost brand loyalty.
How Do you Solve VRP Using Modern Logistics Tools?
Modern vehicle routing tools use machine-learning-based solutions which:
1) Increase fleet efficiency
By helping gain 100% visibility of on-ground surface operations to enable delivery stakeholders to prepare for delays, route diversions, and unnecessary stoppages. This yields enhanced fleet visibility further helps fulfil even the most constricted delivery timelines that empower business operations and customers alike.
2) Reduced fuel costs
By finding an optimal route in terms of fuel consumption as well as the distance to be covered, modern vehicle routing tools help optimize the cost of delivery operations. The resulting routing solutions also help monitor fuel consumption and allow better management of routes.
3) Boost consumer satisfaction to ensure profitability per order
Real-time dynamic routing offered by modern vehicle routing software makes it possible for stakeholders to maintain constant two-way communication with customers for a longing positive impact. The customers feel empowered when allowed to pick or modify their delivery slots. At the same time, if a delay occurs, the possibility of keeping them in the loop yields satisfaction to boost tangible as well as intangible profitability per order.
4) Machine Learning-Based Routing
Modern vehicle routing software runs in machine-learning principles and tools that thoroughly analyze collected historical data, KPIs of previously serviced routes to create highly optimized routes between destinations. Consequently, the yielded routes are largely free of hurdles from uncontrollable conditions like weather, traffic congestion, delivery urgency, type of delivery, contents of delivery, etc. Each delivery made by vehicle routing software running on machine-learning based route planning can help companies save money and time.
5) Intelligent Allocation
Unlike traditional vehicle routing methods, integration of technology and automation to the supply chain via vehicle routing software can help intelligently allocate shipments to last-mile agents in a logical sequence. By automatically factoring in requirements and then suggesting an optimal allocation plan, such tools make the whole ecosystem time as well as cost-effective.
A modern last-mile logistics platform like FarEye can help address vehicle routing problems by allowing companies to create highly efficient routes and optimize them in real-time. This helps companies foresee and resolve unpredictable conditions to ensure timely delivery of packages. Book your demo with FarEye to learn how we are empowering enterprises to address the vehicle routing problem.