A reinforcement learning-based metaheuristic approach to address the dynamic scheduling problem in cloud manufacturing with task cancellation

Recent developments in cloud manufacturing (CMg) have highlighted the need for efficient task scheduling and resource allocation in distributed and dynamic environments. To the best of our knowledge, existing studies have not considered dynamic events such as task cancellation, which can lead to resource inefficiencies and disrupt the initial schedule. To address this gap, this paper introduces a novel dynamic task scheduling and service allocation (DTSSA) problem in CMg that considers task cancellation. The proposed model considers logistics time and different arrival times, which directly impact the tasks’ completion times. Furthermore, a reinforcement learning-based genetic algorithm is developed to tackle the NP-hardness of the model and solve medium- and large-scale problems in a reasonable time. The algorithm dynamically selects search operators using the Q-learning algorithm and applies a ε-greedy approach to improve search capabilities. In this regard, first, the metaheuristic algorithms’ parameters are tuned by the Taguchi method. The proposed algorithms were evaluated using 30 benchmark instances from the literature, as well as example cases inspired by existing studies. Next, the mathematical model is evaluated by implementing small-scale examples using GAMS software. Then, the algorithms are compared with not only some well-known metaheuristic algorithms but also recently developed metaheuristic algorithms using statistical tests and several test problems of different sizes. Additionally, results show that the rescheduling problem provides up to 8.7% better solutions on average than the initial schedule. Lastly, the model's sensitivity analysis reveals that the longer the processing time and logistic time, the longer the maximum completion time for scheduling and rescheduling.