The global food delivery market provides many opportunities for AI-based services that can improve the efficiency of feeding the world. This paper presents the Cloud Kitchen platform as a decision-making tool for restaurants with food delivery and a simulator to evaluate the impact of the decisions. The platform consists of a Technology-Specific Bridge (TSB) that provides an interface for communicating with restaurants or the simulator. TSB uses a PDDL model to represent decisions embedded in the Unified Planning Framework (UPF). Decision-making, which concerns allocating customers' orders to vehicles and deciding in which order the customers will be served (for each vehicle), is done via a Vehicle Routing Problem with Time Windows (VRPTW), an efficient tool for this problem. We show that decisions made by our platform can improve customer satisfaction by reducing the number of delayed deliveries using a real-world historical dataset.

The k-SERVER problem is one of the most prominent problems in online algorithms with several variants and extensions. However, simplifying assumptions like instantaneous server movements and zero service time has hitherto limited its applicability to real-world problems. In this paper, we introduce a realistic generalization of k-SERVER without such assumptions - the k-FOOD problem, where requests with source-destination locations and an associated pickup time window arrive in an online fashion, and each has to be served by exactly one of the available k servers. The k-FOOD problem offers the versatility to model a variety of real-world use cases such as food delivery, ride sharing, and quick commerce. Moreover, motivated by the need for fairness in online platforms, we introduce the FAIR k-FOOD problem with the max-min objective. We establish that both k-FOOD and FAIR k-FOOD problems are strongly NP-hard and develop an optimal offline algorithm that arises naturally from a time-expanded flow network. Subsequently, we propose an online algorithm DOC4FOOD involving virtual movements of servers to the nearest request location. Experiments on a real-world food-delivery dataset, alongside synthetic datasets, establish the efficacy of the proposed algorithm against state-of-the-art fair food delivery algorithms.

The drone delivery service I was trying out is operated by Meituan, China's most popular food delivery platform. In 2022, the company engaged some 6 million gig delivery workers to deliver billions of orders. But the company has also been developing drone delivery since 2017. And in Shenzhen, a southern city that's home to a mature drone supply chain, Meituan has been regularly operating such delivery routes for the last year and a half. Many big corporations have had their eyes on drone delivery: Amazon first proposed doing it in 2013, but its progress has been limited by regulations and a lack of demand.

Founded in Sydney in 2017, we saw the need to provide short term, flexible leases of electric bikes to people who want to make money from food delivery. We are doubling in size every 6 months! The problems you solve and the impact you have will be key to helping Zoomo lead the transformation of last-mile logistics as we scale across global markets. We are transitioning billions of urban delivery miles from bikes, cars and trucks to smart e-bikes, starting with food delivery. At Zoomo, we celebrate diversity and are committed to creating an inclusive environment and equal opportunities.

Over the past years, Russian search and tech giant Yandex made an effort not to fall behind its Western counterparts and had developed its own smart devices, self-driving cars, as well as its own food delivery and ride-sharing services, among other products. According to The New York Times, though, the West's sanctions against its home country after the invasion of Ukraine has made it impossible to continue developing and improving its projects. That's why Yandex's parent firm, which is registered in Amsterdam, is reportedly looking to sell and sever ties with Russia. Apparently, Yandex is planning to sell the emerging technologies it's working on to markets outside the country, since they require Western technologies and experts to reach their full potential. It's also looking to sell its established businesses, such as its internet browser, its food delivery and its ride-hailing apps.

The food industry is one of the biggest industries in the world, and it's constantly growing. Many people are interested in how things are changing in this industry and how they can take advantage of it. The fact that artificial intelligence has been able to enter this industry and make certain processes far more efficient than they were in the past has a lot of people interested in how learning and AI are changing food. According to market research, the global artificial intelligence market in the food and beverage market is growing rapidly, with a CAGR of 45.4% during the forecast period. This market was valued at USD 4.49 billion in 2021 and is expected to continue growing in the coming years. In this article, we'll understand how AI drives the future of food.

Along with the rapid growth and rise to prominence of food delivery platforms, concerns have also risen about the terms of employment of the gig workers underpinning this growth. Our analysis on data derived from a real-world food delivery platform across three large cities from India show that there is significant inequality in the money delivery agents earn. In this paper, we formulate the problem of fair income distribution among agents while also ensuring timely food delivery. We establish that the problem is not only NP-hard but also inapproximable in polynomial time. We overcome this computational bottleneck through a novel matching algorithm called FairFoody. Extensive experiments over real-world food delivery datasets show FairFoody imparts up to 10 times improvement in equitable income distribution when compared to baseline strategies, while also ensuring minimal impact on customer experience.

Here are some postcards from 2021 and wishing you all the best for 2022! According to Crunchbase, 26 robotics startups were founded and funded in 2021. Many others were founded but not funded, or funded but not founded. AION Prosthetics Electronics, Manufacturing, Medical Device, Robotics AION Prosthetics develops a prosthetic system designed to provide an adjustable, durable, and affordable future for amputees. Atorika Augmented Reality, EdTech, Education, Edutainment, Leisure, Personal Development, Robotics, Subscription Service, Virtual Reality Atorika offers edutainment that adapts to any child.

On a congested sidewalk next to a busy mall in Shenzhen, a 20-something woman uses a smartphone app to order a milk tea on Meituan, a major food delivery company. In under ten minutes, the pearl-white drink arrives, not on the back of one of the city's ubiquitous delivery bikes, but descending from the cloudy heavens, in a cardboard box on the back of a drone, into a small roadside kiosk. The only thing the scene is missing is a choir of angels. Over the past two years, Meituan, one of China's largest internet companies, has flown 19,000 meals to 8,000 customers across Shenzhen, a city with close to 20 million people. The pilot program is available to just seven neighborhoods, each with a three-kilometer stretch, and only from a select number of merchants.

Across the world, mapping technology with Artificial Intelligence (AI) and machine learning allow users to have a variety of choices on their travels. Be it driving, flying, or walking, GPS systems are now a lifesaver in keeping users on track. Before this, most of us often used old maps or would buy travel maps whenever we wanted to move around. Today, map applications are not only available on GPS devices, but also on our mobile phones and are even built into our vehicles to provide better route directions. Despite this, there are still some challenges when it comes to mapping and location tagging.