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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.
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This content will become publicly available on June 23, 2026
Multi-Agent Artificial Intelligence to Self-Organize Machine Networks for Resilient Manufacturing
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.
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- Award ID(s):
- 2302834
- PAR ID:
- 10639952
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8902-2
- Page Range / eLocation ID:
- V002T20A006
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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