The transition to electric vehicles (EVs) is critical to achieving sustainable transportation, but challenges such as limited driving range and insufficient charging infrastructure have hindered the widespread adoption of EVs, especially in time-sensitive logistics such as medical transportation. This paper presents a new model to break through this barrier by combining wireless mobile charging technology with optimization. We propose the Wireless Mobile Charging Electric Vehicle Routing Problem (WMC-EVRP), which enables Medical Transportation Electric Vehicles (MTEVs) to be charged while traveling via Mobile Charging Carts (MCTs). This eliminates the time wastage of stopping for charging and ensures uninterrupted operation of MTEVs for such time-sensitive transportation problems. However, in this problem, the decisions of these two types of heterogeneous vehicles are coupled with each other, which greatly increases the difficulty of vehicle routing optimizations. To address this complex problem, we develop a mathematical model and a tailored meta-heuristic algorithm that combines Bit Mask Dynamic Programming (BDP) and Large Neighborhood Search (LNS). The BDP approach efficiently optimizes charging strategies, while the LNS framework utilizes custom operators to optimize the MTEV routes under capacity and synchronization constraints. Our approach outperforms traditional solvers in providing solutions for medium and large instances. Using actual hospital locations in Singapore as data, we validated the practical applicability of the model through extensive experiments and provided important insights into minimizing costs and ensuring the timely delivery of healthcare services.

We'd all be whizzing round in automated taxis by now if Elon Musk had been right. Instead, fully self-driving cars are struggling to get away from the starting grid and some investors are betting that driverless trucks will reach the checkered flag first. Only a year ago, startups developing self-driving taxis were pulling in eight times more funding than firms working on autonomous trucks, buses and logistics vehicles, but the gap has narrowed dramatically in 2021. With fewer regulatory and technological hurdles, trucks operating on major highways, fixed delivery routes or in environments far from cyclists and pedestrians such as mines and ports are now being seen as a faster way to generate returns. In the year through Dec. 6, total investment activity for self-driving logistics vehicles leapt fivefold to $6.5 billion from $1.3 billion in the same period in 2020, according to startup data platform PitchBook.

Retail is an intensely personal business, and the best artificial intelligence (AI) deployments recognize that fact. Amazon and the MIT Center for Transportation & Logistics are co-sponsoring a competition to train machine learning models to predict the delivery routes chosen by experienced drivers. Amazon is providing all information used by existing route optimization algorithms as part of the training data. However, Amazon will also provide more than 4,000 traces of driver-determined routes, which encode the drivers' know-how. Using both sources of information, contestants will be able to build models that identify and predict drivers' deviations from routes computed in the traditional manner.

Every delivery service can benefit from using routing software. After all, the more optimized a delivery route is, the less time delivery drivers or cyclists need to spend on roads. In turn, that means the delivery company saves costs like labor and fuel. The best software for optimizing your delivery route is based on detailed factors like the size of parcels, the vehicle capacity, the number of stops being completed, and much more, enabling delivery companies to run much more accurately and efficiently. As technology continues to grow, routing software is becoming more sophisticated.

There has been a significant surge of Artificial Intelligence (AI) usage in the restaurant industry for providing improved services to elevate operations, trim cost and create a better environment for guests. How will AI use in the restaurant industry continue to evolve? To evaluate future sales, restaurant owners carry out a sales forecasting process. Most compare the sales report of the previous year with the current year, but factors such as holidays, international events, weather conditions and location, affecting the sales are variable so this traditional process of sales forecasting can sometimes go wrong. For more accurate sales reports, restaurant owners must take the help of technology and artificial intelligence can be a helpful tool for predictions.

In the midst of a surge in demand as more people shop online, the parcel delivery sector is struggling to keep up due to a chronic shortage of drivers. Meanwhile, restaurants are struggling to find ways to reduce waste in an industry notorious for razor-thin profit margins. A viable solution to both industries' conundrums could be artificial intelligence. Japan Data Science Consortium Co. (JDSC), a startup incubated at the University of Tokyo, believes it can solve this growing issue using its own AI patent that analyses household electricity data to calculate whether anyone will be home to receive a package during a given time period. In other words, the AI comes up with a delivery route for truck drivers based on the electricity data.

This week, Agility Robotics announced the official release of its two-legged delivery robot called Digit, which it believes could change the'last mile' logistics problems that have long vexed delivery companies. Digit has two robotic legs and two robotic arms and can pick up and hold packages weighing up to 40 pounds. Digit comes equipped with a LIDAR system that will allow it to avoid environmental obstacles and complete basic spatial tasks, such as handing a package its carry to another person (or another Anvil robot). The robot can currently pick up and carry packages autonomously but needs human input to guide it through new or unfamiliar spaces. Last year, Ford announced that it would purchase two units and said it would investigate using Digit to bring packages to customers front steps from self-driving delivery vehicles.

As Linear Programming is a valuable way of displaying real-world data in a mathematical way, it is commonly used in manufacturing and the service industry. For example, many large distribution companies will use linear programming in the analysis of their supply chain operations, similar to the toy example above. Additionally, linear programming can be used outside the warehouse in the optimization of delivery routes. Companies like Amazon and FedEx use linear programming to find the shortest and most efficient delivery routes. Linear programming is also used in machine learning applications where a neural network is trained to fit model of a function in order to label input data and predict unknown future values.

Despite radical advances in technology, many companies still plan routes for their delivery trucks the same way they did a decade ago. Managers create itineraries the day before, and then hand printouts to drivers to follow or add them to the hand-held devices that their drivers carry at their hip. But when drivers get stuck in traffic jams while on their rounds, they're simply out of luck and behind schedule. The same thing happens if there's a surprise snowstorm that makes roads impassable. In short, the routes are inflexible.

Amazon apparently won't be the only company offering drone delivery service: The United Postal Service could follow suit. UPS announced Tuesday it had successfully tested out a drone for residential delivery, a press release said. The company worked with Workhorse Group, a manufacturing company that created both the drone and the electric UPS car used to test the flight. The test drone successfully flew to its designated location, dropped off the package and then proceeded on its delivery route. The drone tested could carry up to 10 pounds.