Enhanced Multi-Task Scheduling in MEC-Enabled Industrial Systems: Integrating Deep Reinforcement Learning with Optimizer Experiences

In industrial multi-access edge computing (MEC), novel collaborative systems involving multiple automated guided vehicles (AGVs) require the execution of both critical tasks and computational tasks, such as AI-based collision avoidance. However, previous research focused mainly on scheduling process-related tasks and overlooked computational tasks. Moreover, scheduling tasks between AGVs in collaborative systems poses an integer problem, which is challenging to solve in polynomial time, highlighting the need for computationally efficient algorithms. This paper proposes a deep reinforcement learning (DRL)-based approach to multi-task scheduling (DRL-MTS) in multi-AGV systems. It involves dynamically applying a catalog of DRL models, each tailored to different numbers of AGVs. Evaluation results demonstrate that the proposed inter-AGV DRL-MTS strategy closely approaches the optimal solution, reaching up to 96% task completion compared to a 98% of the optimal solution, while significantly reducing decision times. Moreover, the training time for these models has been reduced threefold using datasets from existing optimization solvers, and transfer learning has further cut training times by up to 51%.