Abstract-- Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. T o embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver . We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines. I. INTRODUCTION Flexible manufacturing is a key objective of the modern manufacturing industry [1]. A smart factory is flexible if it can be easily reconfigured to produce different products.

Smart factories enhance production efficiency and sustainability, but emergencies like human errors, machinery failures and natural disasters pose significant risks. In critical situations, such as fires or earthquakes, collaborative robots can assist first-responders by entering damaged buildings and locating missing persons, mitigating potential losses. Unlike previous solutions that overlook the critical aspect of energy management, in this paper we propose REACT, a smart energy-aware orchestrator that optimizes the exploration phase, ensuring prolonged operational time and effective area coverage. Our solution leverages a fleet of collaborative robots equipped with advanced sensors and communication capabilities to explore and navigate unknown indoor environments, such as smart factories affected by fires or earthquakes, with high density of obstacles. By leveraging real-time data exchange and cooperative algorithms, the robots dynamically adjust their paths, minimize redundant movements and reduce energy consumption. Extensive simulations confirm that our approach significantly improves the efficiency and reliability of search and rescue missions in complex indoor environments, improving the exploration rate by 10% over existing methods and reaching a map coverage of 97% under time critical operations, up to nearly 100% under relaxed time constraint.

-- A modern smart factory runs a manufacturing procedure using a collection of programmable machines. Typically, materials are ferried between these machines using a team of mobile robots. T o embed a manufacturing procedure in a smart factory, a factory operator must a) assign its processes to the smart factory's machines and b) determine how agents should carry materials between machines. Existing smart factory management systems solve the aforementioned problems sequentially, limiting the throughput that they can achieve. In this paper we introduce ACES, the Anytime Cyclic Embedding Solver, the first solver which jointly optimizes the assignment of processes to machines and the assignment of paths to agents. We evaluate ACES and show that it can scale to real industrial scenarios. I. INTRODUCTION Modern smart factories are designed to enable flexible manufacturing [1]. A flexible manufacturing system is a system which can produce a variety of different products with minimal reconfiguration [2]. Flexibility can improve a manufacturer's ability to customize products, reduce the time that it takes to fulfill new orders, and lower the costs of producing a new product. To permit flexible manufacturing, a smart factory needs the following two components: 1) Flexible Machines. Flexible machines are general-purpose machines such as CNC machines which can be programmed to carry out a range of manufacturing processes [4].

As more and more automatic vehicles, power consumption prediction becomes a vital issue for task scheduling and energy management. Most research focuses on automatic vehicles in transportation, but few focus on automatic ground vehicles (AGVs) in smart factories, which face complex environments and generate large amounts of data. There is an inevitable trade-off between feature diversity and interference. In this paper, we propose Distributed Multi-Head learning (DMH) systems for power consumption prediction in smart factories. Multi-head learning mechanisms are proposed in DMH to reduce noise interference and improve accuracy. Additionally, DMH systems are designed as distributed and split learning, reducing the client-to-server transmission cost, sharing knowledge without sharing local data and models, and enhancing the privacy and security levels. Experimental results show that the proposed DMH systems rank in the top-2 on most datasets and scenarios. DMH-E system reduces the error of the state-of-the-art systems by 14.5% to 24.0%. Effectiveness studies demonstrate the effectiveness of Pearson correlation-based feature engineering, and feature grouping with the proposed multi-head learning further enhances prediction performance.

The volume of data is continuously increasing and the ability and demand to efficiently analyze the data has become even more crucial. Machine learning and data mining are suitable techniques and tools to efficiently process and analyze the data. Complementary to both techniques is process mining [Aa16]. Process mining is a promising approach to find additional patterns (e.g., in terms of causal effects or bottlenecks) in data and in that way to give new insights into the data that could not be directly found with techniques like machine learning or data mining. The insights from processes are given by means of events that have been tracked by information systems. Then, this event data that is structured within a log (i.e., an event log), is used as input to any process mining algorithm. Process mining allows both an analysis based solely on event logs as well as a comparison between (manually generated or as-is) process models and an event log reflecting the to-be processes.

Manufacturing & Logistics IT spoke with leading analysts and vendors about current developments within the manufacturing technology space and what future innovations might emerge over the next few years. The world of manufacturing technology is changing, and digital is certainly the watchword. As Rowan Litter, research analyst, enterprise mobility, VDC Research, points out, the primary development within the manufacturing technology space is Digital Transformation. This, he explains, can be as simple as upgrading from outdated/legacy systems or pen-and-paper or enabling an entire smart factory with automation, and machine learning/AI capabilities. "As these technologies become tested and proven, manufacturers are realising that correctly incorporating these innovations will lead to increased operational efficiency and greater production to meet rises in consumer demand," he says. Litter believes a very important piece that needs to be talked about is the enabler of these innovations and technology. "That enabler comes from connectivity and networks; what will allow businesses to adopt and connect more technologies, process data faster and provide the best security from a rise in cybersecurity threats, as well as the overall risks with digitalisation," he says. "Private Wireless Networks have emerged as the enabler for manufacturers who are interested in implementing these new technologies. A challenge for many organisations comes from not knowing what to prioritise and where to start. With labour critical to support operations in many of these environments and organisations challenged with optimising workflows, we find that enabling the mobile worker with digital tools is the optimal jumping off point." In terms of drivers for change, Litter maintains that the impacts of COVID-19 highlighted inefficiencies in the manufacturing sector.

In this special guest feature, Eric Whitley, Director of Smart Manufacturing at L2L, believes that machine learning is so powerful precisely because it grows machine knowledge in a continuous feedback loop and becomes exponentially smarter. But what can it do for your business? This article will provide insights into the five benefits of machine learning for manufacturers. For over 30 years, Eric has been a noteworthy leader in the Manufacturing space. In addition to the many publications and articles Eric has written on various manufacturing topics, you may know him from his efforts leading the Total Productive Maintenance effort at Autoliv ASP or from his involvement in the Management Certification programs at The Ohio State University, where he served as an adjunct faculty member.

South Korean auto major Hyundai Motor Company has partnered TeamViewer, a leading global provider of remote connectivity and workplace digitalisation solutions to digitalise business operations and manufacturing processes for Hyundai Motor Group Innovation Center in Singapore (HMGICS). The partners will cooperate to maximise digitalisation benefits in HMGICS' smart factory using TeamViewer's augmented reality (AR) platform, which includes mixed reality (MR) and artificial intelligence (AI) capabilities. The platform will support assembly, maintenance, quality management, logistics, client experience projects and workforce training. They will also conduct joint research and development (R&D) activities in AR-powered smart factory operations, immersive digital experience for frontline workers and AI support for a future automotive factory. Overall, the partnership aims to drive increased productivity, accuracy, speed and safety of frontline production workers.

Robotics is the combined effort of science, engineering, and technology that results in devices referred to as robots that mimic or replace human behaviors. Robots have long captivated popular culture, including R2-D2, Prime Optimus, and WALL-E. These exaggerated, humanoid representations of robots typically appear like a parody of the real thing, but may they actually be more futuristic than we think? Robots are developing mechanical and intellectual skills that do not rule out the possibility of an R2-D2-like machine in the future. In the industrial sector, robots are becoming more and more common, and some experts predict that this trend will only continue to grow over time.

LG Energy Solution announced the launch of its Advisory Council on Artificial Intelligence (AI). Joining forces with leading scholars specializing in AI technology, the Advisory Council is expected to play a major role in LGES's digital transformation and the establishment of a manufacturing intelligence platform. The newly formed council will set visions and business directions to enhance the company's digital transformation, as well as build the technological partnerships required to realize these ambitions. The company has appointed five AI experts as its committee members: Sungroh Yoon (Ph.D, Electrical Engineering, Stanford University), Byung-Gon Chun (Ph.D, Computer Science, University of California, Berkeley), Jinwoo Shin (Ph.D, Mathematics, Massachusetts Institute of Technology), Frank Chongwoo Park (Ph.D, Applied Mathematics, Harvard University), and Jong Min Lee (Ph.D, Chemical Engineering, Georgia Institute of Technology). Each advisory member will be assigned to a different division, and oversee the selection and execution of strategic assignments within their respective area of expertise.