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Modern manufacturing is increasingly challenged by larger product varieties, shorter product life cycles, and unexpected production disruptions. Examples of such disruptions include market uncertainty, machine failures, and delivery backlogs. These disturbances are intricately interrelated, exacerbating system complexity and necessitating the adaptive organization (or re-configuration) of machine networks within factory layouts. However, traditional factory layouts are often stationary and lack the flexibility to rearrange or adjust machine networks in response to volatile markets and unexpected disruptions. Also, layout planning typically emphasizes offline design and configuration of machine networks and resources within a facility to optimize process flow and production performance, but tends to overlook the self-organizing arrangement of machines in a dynamic environment. Therefore, to address this gap, this paper presents a novel Self-organizing Machine Network (SOMN) model that optimizes the spatial layout of machine positions and queue configurations, thereby enhancing the manufacturing system’s resilience to unexpected disruptions. First, as opposed to traditional fixed machine positions, we design intelligent machine agents that communicate and autonomously reorganize in real-time to optimize key performance indicators (KPIs). Second, we develop the machine network model in a Digital Twin (DT) environment, facilitating cyber-physical interactions and capturing variations of state-action space in machine agents. Third, multi-agent reinforcement learning (MARL) algorithms empower these networked machine agents to adapt layouts and minimize the impact of disruptions on production performance. We evaluate and validate the proposed SOMN model through computer experiments, benchmarking it against random search and simulated annealing approaches. Experimental results show that the SOMN model significantly improves material handling efficiency, reduces computational overhead, and maintains productivity in different scenarios of manufacturing disruptions. This research holds strong potential for enabling distributed intelligence within self-organizing machine networks for resilient manufacturing. 

Rapid advances in Digital Twin (DT) provide an unprecedented opportunity to derive data-enabled intelligence for smart manufacturing. However, traditional DT is more concerned about real-time data streaming, dashboard visualization, and predictive analytics, but focuses less on multi-agent intelligence. This limitation hampers the development of agentic intelligence for decentralized decision making in complex manufacturing environments. Therefore, this paper presents a Cognitive Digital Twin (CDT) approach for multi-objective production scheduling through decentralized, collaborative multi-agent learning. First, we propose to construct models of heterogeneous agents (e.g., machines, jobs, automated guided vehicles, and automated storage and retrieval systems) that interact with physical and digital twins. Second, multi-objective optimization is embedded in CDT to align production schedules with diverse and often conflicting objectives such as throughput, task transition efficiency, and workload balance. Third, we develop a multi-agent learning approach to enable decentralized decision making in response to unexpected disruptions and dynamic demands. Each agent operates independently and collaboratively with cognitive capabilities, including perception, learning, and reasoning, to optimize the individual agentic objective while contributing to overarching system-wide goals. Finally, the proposed CDT is evaluated and validated with experimental studies in a learning factory environment. Experimental results demonstrate that CDT improves operational performance in terms of task allocation, resource utilization, and system resilience compared to traditional centralized approaches. This initial study of CDT highlights the potential to bring multi-agent cognitive intelligence into next-generation smart manufacturing. 

Public emergencies pose catastrophic casualties and financial losses in densely populated areas, rendering communities such as cities, towns, and universities particularly susceptible due to their intricate environments and high pedestrian traffic. While simulation analysis offers a flexible and cost-effective approach to evaluating evacuation procedures, conventional evacuation models are often limited to specific scenarios and communities, overlooking the diverse range of emergencies and evacuee behaviors. Thus, there is an urgent need for an evacuation model capable of capturing complex structures of communities and modeling evacuee responses to various emergencies. This paper presents a novel approach to simulating responsive evacuation behaviors for multiple emergency situations in public communities through spatial network modeling and multi-agent modeling. Leveraging a community network framework adaptable to different community layouts based on map data, the proposed model employs a multi-agent approach to characterize responsive and decentralized evacuation decision-making. Experimental results show the model’s efficacy in representing pedestrian flow and pedestrians’ reactive behavior across various campuses based on real-world map data. Additionally, the case study highlights the potential of the proposed model to simulate pedestrian dynamics for a variety of heterogeneous emergencies. The proposed community evacuation model holds strong promise for evaluating evacuation policies and providing insights into resilient plans during public emergencies, thereby enhancing community safety. 

Abstract The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models—job flow graph and AGV traveling graph—to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace.