This dissertation explores the application of multi-agent reinforcement learning (MARL) for handling deadlocks in intralogistics systems that rely on autonomous mobile robots (AMRs). AMRs enhance operational flexibility but also increase the risk of deadlocks, which degrade system throughput and reliability. Existing approaches often neglect deadlock handling in the planning phase and rely on rigid control rules that cannot adapt to dynamic operational conditions. To address these shortcomings, this work develops a structured methodology for integrating MARL into logistics planning and operational control. It introduces reference models that explicitly consider deadlock-capable multi-agent pathfinding (MAPF) problems, enabling systematic evaluation of MARL strategies. Using grid-based environments and an external simulation software, the study compares traditional deadlock handling strategies with MARL-based solutions, focusing on PPO and IMPALA algorithms under different training and execution modes. Findings reveal that MARL-based strategies, particularly when combined with centralized training and decentralized execution (CTDE), outperform rule-based methods in complex, congested environments. In simpler environments or those with ample spatial freedom, rule-based methods remain competitive due to their lower computational demands. These results highlight that MARL provides a flexible and scalable solution for deadlock handling in dynamic intralogistics scenarios, but requires careful tailoring to the operational context.

The machine is designed to inspect industrial sites, respond to disasters, carry out logistics operations and support scientific research. Deep Robotics, a company from China, has unveiled a durable four-legged robot built to operate in extreme environments that humans struggle to traverse. It's called the Lynx M20, and it builds upon the agility of its predecessor, the Lynx robot dog. This versatile machine is designed to handle anything from inspecting industrial sites and responding to disasters to carrying out logistics operations and supporting scientific research. Here's what you need to know.

Artificial intelligence (AI)-based technologies are just about everywhere these days, making electronic devices, equipment, and business processes more streamlined and, of course, smarter. As it is commonly understood, AI uses computers and machines to mimic the problem-solving and decision-making capabilities of the human mind--and it exists on many levels. Common examples include speech recognition, online virtual agents, computer vision, and even "recommendation engines"--those systems that tell you what "you may also like" when you're shopping online. AI is also being applied in the warehouse, in the form of emerging technologies designed to increase output, reduce errors, maximize equipment uptime, and help companies bridge the labor gap by accomplishing more work with fewer people. One of the newest terms to hit the warehouse floor is a warehouse management system "accelerator," which is a software solution that sits above a company's warehouse management system (WMS) to help optimize and orchestrate the broader operations of a warehouse, according to Keith Moore, CEO of AutoScheduler, a WMS accelerator founded in 2020.

Shipsy enables digital transformation across the logistics ecosystem. It's artificial intelligence (AI)-powered platform and mobility suite seamlessly connects cross- border and local logistics by automating operations and ensuring intelligent third-party logistics (3PL) management. Shipsy's intuitive platform provides complete visibility of first, middle and last-mile operations, unlocks operational efficiency, and leverages real-time analytics to make informed decisions. "The idea behind starting Shipsy was to solve the challenges and inefficiencies of the last- mile logistics. But we later realised that it was just the tip of the iceberg. We saw an urgent need to marry the physical and digital parts of the goods' movement. Hence, we started to build and expand the capabilities of our platform to drive the digital inclusion of siloed logistics aspects. So, although we created by focusing on the last mile, we gradually directed the solution to include the first and middle mile, intermodal and cross-border supply chain stakeholders, and even customers," said Soham Chokshi, the chief executive officer (CEO) and co-founder, Shipsy, which was founded in 2015 in Gurugram, Haryana, India.

That equates to 31 kilograms (68 pounds) per person of perfectly good food that gets tossed each year. Madrid is planning to bring this number down with a new set of regulations to rein in food waste. The government approved a draft bill that would see supermarkets fined for throwing away surplus food ― by up to €60,000 ($57,000), or as high as €500,000 for repeat offenders. The law, if passed by parliament, would also make it mandatory for restaurants to offer so-called "doggy bags" for guests to take home their left-overs. Spain hopes to have the law in place by early 2023 to curb the amount of food that lands in the garbage instead of on someone's plate, and to reduce environmental costs.

A futuristic cargo drone that could be used to deliver packages over distances of up to 25 miles has been unveiled as a design concept. The uncrewed eVTOL (electric vertical takeoff and landing) aircraft features six battery powered omni-directional CycloRotors that generate thrust. They are designed to allow the drone to land on a 16-foot platform in crosswinds of up to 40mph. This is important, its designers say, because precision landing in confined areas and the ability to handle challenging wind conditions are key for operating in urban areas. The hope is that the drone will be able to travel at speeds of 80mph at almost 5,000ft (1,500 metres).

With companies facing labor challenges and rising inflation across all industries, automation and robotics offer measurable relief, enabling increased productivity and a more efficient use of human capital. Two to three years ago, only about 5% of warehouses in the U.S. relied heavily on automation, a percentage that has not increased much to date. But with fewer available workers and increased costs, the business case for implementing these technologies to aid the available workforce has become all the more compelling. In non-automated facilities as large as one million square feet, 30% of a worker's time can be spent traveling from one area of the warehouse to another to perform assigned tasks. Cutting down on employee transit time can not only increase productivity and service levels, but also save money.

AI reduces errors in common semi-skilled tasks such as sorting and categorizing products. Autonomous mobile robots (AMRs), for instance, improve package delivery, including the last mile of delivery which is typically the most expensive. AI helps AMRs with route planning and feature recognition, such as people, obstacles, delivery portals, and doorways. Integrating logistics automation into any environment comes with challenges. It can be as simple as replacing a repetitive process with a powered conveyor or as complex as introducing a collaborative, autonomous robot into the workplace.

"For more technology and data that one integrates into a software, in the end always experience and learning are the fundamental pillars. The important thing is to understand how to extract them intelligently ". With that phrase, Álvaro Echeverría, co-founder and CEO of SimpliRoute, recalls the need that shaped the idea of creating an AI virtual assistant to optimize its logistics platform. The startup is dedicated to optimizing routes for dispatch vehicles. The problem, according to Echeverría, was that despite the fact that logarithms and data science effectively optimize logistics a lot, "there are things that no default software can evaluate, such as whether a street is in poor condition, whether it is too narrow for a truck. This valuable information is held by the drivers ".

Artificial intelligence (AI) is more accessible than ever and is increasingly used to improve business operations and outcomes, not only in transportation and logistics management, but also in diverse fields like finance, healthcare, retail and others. An Oxford Economics and NTT DATA survey of 1,000 business leaders conducted in early 2020 reveals that 96% of companies were at least researching AI solutions, and over 70% had either fully implemented or at least piloted the technology. Nearly half of survey respondents said failure to implement AI would cause them to lose customers, with 44% reporting their company's bottom line would suffer without it. Simply put, AI enables companies to parse vast quantities of business data to make well-informed and critical business decisions fast. And, the transportation management industry specifically is using this intelligence and its companion technology, machine learning (ML), to gain greater process efficiency and performance visibility driving impactful changes bolstering the bottom line.