# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 1 Introduction
As a consequence, the focus of related research turns to find the approximate optimal solution. As a major branch of approximation methods, a search-based heuristic scheduling approach expands the current schedule with well-designed strategies. The keys to designing such an approach are the neighborhood structure and the search direction, such as Simulated Annealing, Tabu-search, Discrete Search, Genetic Algorithm, and so on [19; 20; 21; 22; 23]. However, these algorithms cannot be directly applied to the scheduling problem in the CMfg environment because they will take a long time for the solution searching. In order to solve the real-time scheduling problem, researchers have proposed and developed numerous approaches and algorithms that can be divided into two categories: generative and adaptive. Generative scheduling adopts the idea of integrating partial-schedules, and the key lies in the design of priority rules. However, the priority rules for the determined manufacturing environment are not suitable for dynamic random situations [24]. Hence, adaptive methods have attracted more researchers. Adaptive scheduling, which is also called rescheduling, modifies the existing schedule dynamically according to the decision environment. Proactive and reactive are the two main modes of rescheduling [25]. Due to the uncertain factors, such as task delay, unit failure, and order insertion, the disturbances become difficult to predict, which means that the proactive rescheduling cannot be used in the real-time environments [26]. As a result, more and more researchers have begun to focus on reactive or hybrid rescheduling methods [27]. One group studied the random resource constrained project scheduling problem. They transformed the problem into a multi-stage decision problem, and made dynamic decisions by designing rescheduling strategies [28]. Another group studied the rescheduling problem with new operation insertion in a single machine environment. They set up an event-triggered rescheduling model with the goal of minimizing the absolute deviation of the maximum delay compared with the baseline [29]. For the dynamic manufacturing environment where tasks arrive randomly, a dynamic event-driven task rescheduling method is designed to avoid service rearrangement, and the constructed parallel processing strategy takes service time, logistics time, the earliest available time, and other factors into consideration at the same time for optimal service selection [30]. Researchers have also proposed two reactive scheduling strategies based on the selection and buffering to solve the random resource constrained project scheduling problem. They found that the buffer-based reaction rules combined with the baseline scheduling turned out to be an effective solution [31]. The above reviews indicate that it is difficult to efficiently allocate massive manufacturing resources in the CMfg environment for the real-time scheduling. Research on the real-time scheduling in the CMfg is scarce, and the proposed scheduling methods are based on reactive scheduling strategy, the baseline of which is set in advance and needs to be modified as the environment changes. For the dynamic CMfg environment, such scheduling methods will consume numerous computing resources. Changing schedules often results in the re-allocation of resources, which will lead to additional logistics costs. Generally, real-time scheduling using these approaches requires a trade-off between the solution quality and decision efficiency. With the development of artificial intelligence, more and more teams are trying to apply related technologies to the manufacturing scope [32]. In order to solve the scheduling problem in the manufacturing environment, many neural networks are used to determine the key parameters to improve the performance of the genetic algorithm [33; 34].