DSAC-Configured Differential Evolution for Cloud–Edge–Device Collaborative Task Scheduling

Abstract

Industrial Internet-of-things enables various manufacturing processes executed in distributed production lines and flexible workshops. With different cloud-edge-device collaboration ways, interconnected manufacturing tasks and computational tasks are cooperatively completed in manufacturing cells, cloud resources, and edge resources. Large-scale decision variables and complex precedence constraints make the scheduling problem intractable. To this end, this paper proposed a discretized soft actor-critic configured differential evolution algorithm to find a stable solution for the cloud-edge-device collaborative task scheduling problem. A mathematical model is established to describe the relationship between different tasks, the variables, the main constraints in collaboration, and the scheduling targets. A decentralized partially observable Markov decision process is modeled with five neural networks and three discretized loss functions to formulate the discretized soft actor-critic policy efficiently and enable it to find the best differential evolution configurations for different scheduling cases. Experimental analysis of four cloud-edge-device scheduling instances indicates that the proposed method trained in one case is adaptable to the other three cases. In the four cases, the proposed method reduces the total objective by 30.82% and 44.35% at most compared to five deep reinforcement learning-based differential evolution algorithms and seven typical evolutionary algorithms, respectively.