INFINITYQ’S LAST MILE DELIVERY API, OPTIMIZES PARCEL DELIVERY ROUTES...
Reducing operational costs, increasing customer satisfaction
and growing your business.
4th November 2023, Montreal
How to Optimize Parcel Delivery in Dense Areas
In the vast and complex network of supply chains, last mile delivery stands as the ultimate frontier, where efficiency, precision, and customer experience intersect. It's the critical phase of the delivery process, representing the final link between a product and its recipient. Despite its apparent simplicity, the last mile is often the most challenging of the entire logistical journey. In this blog, we delve deep into the serious and significant realm of last mile delivery, exploring the nuances, challenges, and innovative solutions that shape this pivotal stage in modern commerce.
Parcel Last Mile Delivery
Parcel last mile delivery refers to the final stage of a package's journey. It involves the transportation and delivery of packages from a distribution center or local hub to the customer’s doorstep or a designated delivery location, completing the supply chain cycle. The "last mile" is so named because it's the final part of the delivery process, encompassing the shortest distance between the logistics center and the customer's location.
Benefits of Route Optimization
Parcel delivery route optimization offers numerous benefits that significantly impact operational efficiency, customer satisfaction, and cost-effectiveness. Some of the key advantages include:
1. Reduced Delivery Time: Optimized routes enable faster and more efficient delivery, reducing the time taken to complete deliveries. This results in quicker and more punctual service, meeting or exceeding customer expectations for timely deliveries.
2. Lower Operational Costs: By minimizing unnecessary mileage, fuel consumption, and idle time, route optimization reduces operational costs associated with fuel, maintenance, labor, and vehicle wear and tear. It helps in maximizing the efficiency of resources, leading to cost savings.
3. Increased Productivity: Efficient routes allow drivers to complete more deliveries within the same time frame, thereby improving productivity. Drivers can cover more stops without overextending their working hours, leading to a higher volume of deliveries.
4. Environmental Impact: By reducing unnecessary mileage and fuel consumption, route optimization contributes to environmental sustainability. It leads to lower carbon emissions, promoting eco-friendly delivery practices.
5. Competitive Advantage: Implementing efficient route optimization techniques can give companies a competitive edge in the market. By providing faster, more reliable, and cost-effective services, companies can attract and retain customers.
Overall, route optimization for parcel delivery plays a crucial role in improving the overall efficiency of logistics operations. It not only benefits the company in terms of cost savings and productivity but also leads to higher customer satisfaction and loyalty, contributing to the success and growth of the business.
Vehicle Routing Problem
The Vehicle Routing Problem (VRP) is a combinatorial optimization challenge in the field of logistics and operations research. It involves determining the most efficient set of routes for a fleet of vehicles to deliver goods or services to a set of customers while meeting various constraints and minimizing certain costs.
The VRP typically involves the following elements:
1. Depot: A central location where vehicles start and end their routes. This is where vehicles are loaded with goods before heading out for delivery.
2. Customers: Locations that need to be serviced or supplied. These can represent delivery destinations, such as residences, businesses, or any point that requires goods or services.
3. Fleet of Vehicles: A set of vehicles with defined capacities and other specific characteristics. These vehicles travel from the depot to service customers while obeying various constraints.
These constraints often include:
Vehicle Capacity: Each vehicle has a limited carrying capacity, and the total demand of customers visited by a vehicle should not exceed this capacity.
Time Windows: Some customers may have specific time windows within which they can be serviced. Deliveries must occur within these time constraints.
Distance or Time Limitations: Vehicles may have a maximum distance they can travel or a maximum working time during a day. Routes need to be planned within these limits.
Solving the VRP involves making two kinds of decisions:
Assignment: Each parcel must be assigned to a vehicle in the fleet.
Sequencing: For each vehicle, the customer locations must be sorted in optimal sequence. The sequence of customer locations has an impact on the driving distance or time to complete a tour.
This problem is known to be NP-hard, meaning as the number of customers or variables increase, the problem's complexity grows exponentially, making it computationally challenging to solve for large problem instances. For instance, for just 10 customer locations there are millions of possible combinations of routes, and for 100 customer locations there are more combinations than atoms in the universe.
Specific Challenges of Route Planning for Parcel Delivery
Route planning poses challenges that are specific to parcel delivery, including the following:
Time Limitation: Delivering small parcels using delivery vans in dense areas, where the distance between delivery addresses is often short, can lead to routes with hundreds of stops per vehicle. In this case, the most likely constraint is a time limitation, rather than vehicle capacity. The driver has a maximum working time to respect labour regulations. Work shift limits are built into InfinityQ’s Last Mile Delivery API, helping you maximize deliveries within the limits of your drivers' work shift.
Same Day Delivery of Late Shipments: Including a late incoming shipment of hundreds of parcels into already planned routes can be complicated. If your route planning process takes 60 minutes or more, re-planning might not be a viable option to deliver these additional parcels on the same day. Our Last Mile Delivery API can optimize parcel delivery routes for 10,000 parcels in around 3 minutes. This allows you to quickly get new optimized routes to deliver late incoming parcel on the same day.
Geographical Obstacles: Large cities are sometimes built around complex river networks. Metropolitan areas near Montreal, Vancouver and New York City are some examples.
Considering rivers in route optimization is crucial for finding the most efficient path. Planning a route that minimizes detours or time-consuming diversions due to rivers can save time and resources. This can be accomplished by considering driving distances rather than flying (geodesic) distances between delivery locations before assigning them to vehicles. Our powerful Last Mile Delivery API can optimize routes for geographical obstacles for up to 15,000 delivery locations, providing up to 8% advantage on total driving distance.
Traffic Restrictions: Dense cities often have lots of traffic restrictions, such as one-way streets, no left turns, etc. In this context, the driving distance to go from point A to point B is often different than from point B to point A, especially if the distance is small. Our solution considers the complexity and intricacies of road networks to more accurately solve the VRP.
Optimize your Fleet Usage: Depending on your needs, you might want to use your whole fleet of vehicles every day and share the load between them, or perhaps minimize the number of vehicles needed on any given day. Our solution can do both.
If you have encountered these challenges, InfinityQ’s Last Mile Delivery API solution is a great tool to optimize your parcel delivery routes. It can help you reduce your operational costs, increase customer satisfaction, and grow your business.
About the Author: Sébastien Delorme, PhD
As Head of Product, and with over 25 years of experience in research and development, Sébastien leads the productization of InfinityQ quantum-inspired technologies. Prior to joining InfinityQ, he held leadership roles in several privately held companies, including Director of Development at Montreal’s Ubisoft Entertainment, where he led more than 15 teams developing a micro-services platform used by all Ubisoft game titles, Director of Engineering and R&D at OSSim Technologies (now Symgery), a Montreal-based start-up developing surgical simulators to train medical students, and Director of Product Development at Sologlobe (now Genetrix North America).