Abstract:

In the context of large-scale customized production, flexible job-shop scheduling faces several critical challenges, including sluggish responses to dynamic demands, limited optimization performance formulti-task integration, and insufficient adaptability touncertain changes and unseen environments. To effectively enhance the optimization quality, generation speed, and adaptive capability of scheduling solutions, this study focused on dynamic flexible job-shop scheduling problems involving multi-resource coordination and multi-task integration in dynamic environments. A multi-agent deep reinforcement learning-based adaptive real-time scheduling method was proposed, along with the development of an adaptive real-time scheduling solver. First, an adaptive real-time scheduling framework based on multi-agent deep reinforcement learning was constructed; an interaction mechanism between the simulation environment and scheduling agents was established, and the distributed decision-making process was formalized as a partially observable Markov decision process. Second, an object-oriented scheduling simulation environment was developed by defining resource and simulation classes (e.g., machines, workpieces, and automated guided vehicles), to comprehensively represent the dynamic characteristics and interactions of typical resources in flexible job shops. Third, scheduling agents supporting machine assignment and job sequencing were designed; a library of deep reinforcement learning algorithms covering value-based, policy-based, and hybrid value-policy approaches was developed to accommodate various scheduling scenarios and complex constraints. Finally, an adaptive solver supporting multi-task and multi-constraint flexible job-shop scheduling was developed, incorporating strategy training and problem-solving modules to achieve adaptive optimization across diverse application settings. Numerical experiments demonstrated that the proposed method exhibited strong generalization and real-time responsiveness in both known and unseen scenarios, effectively handling complex and dynamically changing scheduling problems. In case studies, the developed solver showed significant advantages in response speed while maintaining high optimization performance. The results indicated that the proposed adaptive real-time scheduling method and solver provided an effective solution to multi-resource scheduling challenges and offered solid technical support for the intelligent and autonomous transformation of discrete manufacturing systems.

Key words: flexible job-shop scheduling, adaptive real-time scheduling, deep reinforcement learning, solving tool, intelligent manufacturing