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<p>Task allocation aims to assign manufacturing tasks to human operators and automation agents to optimize human-automation collaboration. Unlike conventional approaches that focus solely on production performance, this study identifies cognitive intelligence as a crucial aspect of human-automation task allocation. This involves modeling human cognition, which is characterized by cognitive states and human trust, to evaluate team cognitive performance. Cognitive states represent the operator’s cognitive load and attention, while human trust indicates the operator’s confidence in different levels of automation. To balance the optimization of production performance and team cognitive performance, the task allocation problem is formulated as a non-cooperative game—the Stackelberg game, where the leader optimizes production performance, and the follower optimizes team cognitive performance. The non-cooperative game implies how symbiosis is achieved in a human-automation team through optimizing different team performance measures. This formulation is essentially a bi-level optimization problem. To solve it, a Multi-Environment Genetic Algorithm is proposed. Finally, a case study of medical tube assembly task allocation is presented to validate the feasibility and effectiveness of the algorithm.</p>

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Cognitive intelligent task allocation for human-automation symbiosis in Industry 5.0 manufacturing systems via non-cooperative game theory: a bi-level optimization approach

  • 期刊论文

  • 发表日期：

    2025年1月2日

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  • Shu Wang,
  • Roger J. Jiao

摘要

Task allocation aims to assign manufacturing tasks to human operators and automation agents to optimize human-automation collaboration. Unlike conventional approaches that focus solely on production performance, this study identifies cognitive intelligence as a crucial aspect of human-automation task allocation. This involves modeling human cognition, which is characterized by cognitive states and human trust, to evaluate team cognitive performance. Cognitive states represent the operator’s cognitive load and attention, while human trust indicates the operator’s confidence in different levels of automation. To balance the optimization of production performance and team cognitive performance, the task allocation problem is formulated as a non-cooperative game—the Stackelberg game, where the leader optimizes production performance, and the follower optimizes team cognitive performance. The non-cooperative game implies how symbiosis is achieved in a human-automation team through optimizing different team performance measures. This formulation is essentially a bi-level optimization problem. To solve it, a Multi-Environment Genetic Algorithm is proposed. Finally, a case study of medical tube assembly task allocation is presented to validate the feasibility and effectiveness of the algorithm.