Personal use of this material is permitted. Abstract --Efficient planning of activities is essential for modern industrial assembly lines to uphold manufacturing standards, prevent project constraint violations, and achieve cost-effective operations. While exact solutions to such challenges can be obtained through Integer Programming (IP), the dependence of the search space on input parameters often makes IP computationally infeasible for large-scale scenarios. Heuristic methods, such as Genetic Algorithms, can also be applied, but they frequently produce suboptimal solutions in extensive cases. This paper introduces a novel mathematical model of a generic industrial assembly line formulated as a Markov Decision Process (MDP), without imposing assumptions on the type of assembly line a notable distinction from most existing models. The proposed model is employed to create a virtual environment for training Deep Reinforcement Learning (DRL) agents to optimize task and resource scheduling. T o enhance the efficiency of agent training, the paper proposes two innovative tools. The first is an action-masking technique, which ensures the agent selects only feasible actions, thereby reducing training time. The second is a multi-agent approach, where each workstation is managed by an individual agent, as a result, the state and action spaces were reduced. A centralized training framework with decentralized execution is adopted, offering a scalable learning architecture for optimizing industrial assembly lines. This framework allows the agents to learn offline and subsequently provide real-time solutions during operations by leveraging a neural network that maps the current factory state to the optimal action. The effectiveness of the proposed scheme is validated through numerical simulations, demonstrating significantly faster convergence to the optimal solution compared to a comparable model-based approach.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I once witnessed a friend going through a severe midlife crisis. Basically overnight, this formerly serious and well-adjusted middle-aged man dumped his wife for a much younger girlfriend, got a face tattoo, and built a full-sized halfpipe in his house. Soon, we were barraged with music recommendations (all stuff he'd listened to in high school and college) and life updates laden with "hip" "slang" ("Despite the age gap, my situationship with Triniteigh is lowkey lit"). It was a transparent--and, from a certain perspective, even sympathetic--response to a universal anxiety: He'd seen that the good times were over, and that only decline lay ahead. But, like all nostalgists, he didn't realize that you can't ever truly go back; you can only go backward. The United States, under President Donald Trump, seems to be undergoing a similar midlife crisis, as this reactionary administration attempts to brute-force the country back to a golden age that many people are realizing either didn't exist in the first place or has been permanently lost to the mists of time and modernization.

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

With the continuous improvement in the computational capabilities of edge devices such as intelligent sensors in the Industrial Internet of Things, these sensors are no longer limited to mere data collection but are increasingly capable of performing complex computational tasks. This advancement provides both the motivation and the foundation for adopting distributed learning approaches. This study focuses on an industrial assembly line scenario where multiple sensors, distributed across various locations, sequentially collect real-time data characterized by distinct feature spaces. To leverage the computational potential of these sensors while addressing the challenges of communication overhead and privacy concerns inherent in centralized learning, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm. Tailored to the industrial assembly line scenario, DAO-VFL effectively manages continuous data streams and adapts to shifting learning objectives. Furthermore, it can address critical challenges prevalent in industrial environment, such as communication noise and heterogeneity of sensor capabilities. To support the proposed algorithm, we provide a comprehensive theoretical analysis, highlighting the effects of noise reduction and adaptive local iteration decisions on the regret bound. Experimental results on two real-world datasets further demonstrate the superior performance of DAO-VFL compared to benchmarks algorithms.

Boston Dynamics has unveiled a new version of its Atlas humanoid robot, showing its creepy movements that make it look like something out of a sci-fi horror movie. The Massachusetts-based robotics company shared a video of the latest humanoid, showing it pulling its leg behind its heads to stand up - in a way that the public said'looked like something out of The Exorcist.' This new version boasts joints that let the machine bend and move in ways that the human body can't - unlike the original, rigid Atlas that was famous for dancing and doing parkour. The company also plans to sell the latest humanoid robot, but the price has yet to be disclosed, and it is set to begin its first job at Hyundai's factories next year. Boston Dynamics announced the new version of its humanoid robot Atlas, featuring a ring light as its face.

Highly automated assembly lines enable significant productivity gains in the manufacturing industry, particularly in mass production condition. Nonetheless, challenges persist in job scheduling for make-to-job and mass customization, necessitating further investigation to improve efficiency, reduce tardiness, promote safety and reliability. In this contribution, an advantage actor-critic based reinforcement learning method is proposed to address scheduling problems of distributed flexible assembly lines in a real-time manner. To enhance the performance, a more condensed environment representation approach is proposed, which is designed to work with the masks made by priority dispatching rules to generate fixed and advantageous action space. Moreover, a Monte-Carlo tree search based soft shielding component is developed to help address long-sequence dependent unsafe behaviors and monitor the risk of overdue scheduling. Finally, the proposed algorithm and its soft shielding component are validated in performance evaluation.

Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising measurements from an assembly line in a manufacturing context. This line consists of numerous physical processes for which we are able to provide ground truth causal relationships on the basis of a detailed study of the underlying physics. We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods. To accomplish this, we employ distributional random forests in order to flexibly estimate and represent conditional distributions that may be combined into joint distributions that strictly adhere to a causal model over the observed variables. The estimated conditionals and tools for data generation are made available in our Python library $\texttt{causalAssembly}$. Using the library, we showcase how to benchmark several well-known causal discovery algorithms.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' Tesla CEO Elon Musk often refers to the automobile factory as "the machine that builds the machine," but there are plenty of human workers involved in even the most highly automated plants. They remain a key part of the exceedingly complex process that is automobile assembly but need to operate as efficiently as their mechanical counterparts to keep cars and trucks coming off the line with a combination of quality and speed. Weeding out issues and making sure everything is running smoothly has traditionally meant sending quality control personnel up and down the lines to get eyes on the action. WHAT ARE THE FOUR MAIN TYPES OF AI? Palo Alto-based Invisible AI was founded by veterans of the autonomous car industry who saw an alternative for the artificial intelligence-driven machine vision technology they were working on that could come to market long before the mass acceptance of self-driving cars.

One afternoon not long ago, I sat entering notes into a patient's medical record. She was in her forties, and her labs showed anemia. The causes of anemia range from menstruation to cancer, and so pinpointing the correct underlying diagnosis is critical. Physicians are trained to formulate a full roster of possibilities, known as the differential diagnosis, and then to work down the list systematically. We're taught to cast a wide net--celiac disease, parasitic infections, thalassemia, lead poisoning, liver disease, B12 deficiency, myeloma, sickle-cell disease, G6PD deficiency--because you'll never make a diagnosis if you haven't included it in your differential.

The manufacturing industry (largely) welcomed artificial intelligence with open arms. Planning for mechanical assemblies still requires more than scratching out some sketches, of course -- it's a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies. Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential for error. In a quest to ease some of said burdens, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that's accurate, efficient, and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multipart assembly, and then searches for a physically realistic motion path for each step.