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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.
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This content will become publicly available on June 19, 2026
Cognitive Digital Twin for Multi-objective Production Scheduling
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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- Award ID(s):
- 2302834
- PAR ID:
- 10608684
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Manufacturing Science and Engineering
- ISSN:
- 1087-1357
- Page Range / eLocation ID:
- 1 to 18
- Subject(s) / Keyword(s):
- Digital twin Manufacturing Decision making Automated guided vehicles Automated materials handling equipment Computer-integrated manufacturing Machinery Modeling Pareto optimization Production systems optimization Resilience Simulation Visualization
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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