Intelligent Collaborative Resource Allocation for Mechanical Manufacturing Edge Networks: A Deep Reinforcement Learning Approach

Industry 4.0 is transforming mechanical manufacturing systems into edge-enabled, networked, and intelligent environments, where concurrent task execution, heterogeneous resource coordination, and dependency-aware scheduling have become critical requirements. In such scenarios, resource allocation must jointly consider computational demand, storage demand, business priority, deadline urgency, and inter-task dependencies, while enabling coordinated decisions among distributed edge agents. However, existing single-agent reinforcement learning methods have limited capability to model complex dependency relationships and heterogeneous resource collaboration under concurrent workloads, whereas conventional multi-agent systems often rely on coarse-grained task modeling and simplified cooperation mechanisms. To address these limitations, this paper proposes MIRA, a multi-agent deep reinforcement learning-based method for resource allocation in mechanical manufacturing edge networks. MIRA first decomposes tasks into fine-grained dependent subtasks, constructs a deadline-aware multi-metric priority function, and introduces a dynamic weight adjustment mechanism to balance computational demand, storage demand, normalized business priority, and deadline urgency. It then employs an adjacency matrix to characterize topology-aware agent interactions, enabling coordinated decision-making between computing agents and storage agents. Furthermore, MIRA incorporates an event-triggered state-exchange mechanism that updates subtask priorities and agent policies under changing workload, deadline, resource, and topology conditions. Experimental results on a PCB-derived simulated scheduling workload show that MIRA outperforms the selected baselines across multiple scheduling metrics.