Environmental challenges of delivery

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Delivery of goods produces approximately 24% of global CO₂ emissions, which contribute to climate change. The largest CO₂ emissions come from passenger cars (48%) and trucks (16%). Trucks are more often used for long-haul deliveries, while cars can be used for the “last mile” stage, when goods are delivered directly to the customer. Other significant sources of emissions are shipping (10%), medium-duty trucks (9%), international (6%) and domestic (5%) air travel.

The last mile delivery process mexico whatsapp number database presents a number of challenges: if the customer is absent, the parcel has to be taken back, which increases the mileage. Returns of goods for other reasons and traffic jams also add extra kilometers, increasing the load on logistics systems. This problem is especially relevant for developed countries with high consumption levels. To reduce the negative impact of delivery on the environment, it is important to optimize processes not only with familiar methods, such as electric vehicles and drones, but also with new technologies.

Attack the carbon footprint
AI is often associated with content generation by the general public, but its potential is much larger and more profound. It is expected to help reduce global greenhouse gas emissions by 4% in 2030, which is comparable to the annual emissions of Australia, Canada, and Japan. In addition, its environmental applications could add up to $5.2 trillion to the global economy in 2030, which is 4.4% more than forecasts without these technologies.

To reduce the carbon footprint, AI offers a number of solutions.

Firstly, it is the optimization of logistics routes. The process involves improved planning and distribution of vehicles, and smart algorithms help with this.

Ant algorithms. An optimization method inspired by the behavior of ants in nature, which efficiently find the shortest path to food. The more often ants use a certain route, the brighter the trail becomes and the more attractive this path is for new ants.

In logistics, ant algorithms help AI analyze and select the best routes and methods for loading vehicles. The system evaluates different options and, like ants, leaves a digital trail with which it stores information about the most successful decisions in terms of time and costs. Gradually, the technology strengthens these strategies and finds the most optimal solution. This allows for better use of the carrying capacity of each vehicle, reducing the distance, time, and costs of delivery.

Simulated Annealing Algorithm: This is a method for optimizing cargo delivery, inspired by metalworking techniques where a material is heated and slowly cooled to improve its properties.

In logistics, an algorithm uses random changes in the distribution of loads across vehicles to find the most efficient delivery method. These changes include redistributing orders between routes based on weight, volume, and delivery time. The goal is to make the delivery process more efficient by reducing distances and times, and to make optimal use of truck space. The algorithm can check whether adding or removing an order from a route improves overall efficiency. If so, the change is kept; if not, it is rejected or another option is sought. This approach helps to maximize truck loads, reduce the number of trips, and reduce both costs and emissions.