An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing

Abstract

The cloud manufacturing platform needs to allocate the endlessly emerging tasks to the resources scattered in different places for processing. However, this real-time scheduling problem in the cloud environment is more complicated than that in a traditional workshop because constraints, such as type matching, task precedence, resource occupation, and logistics duration, need to be met, and the internal manufacturing plan of providers must also be considered. Since the platform aggregates massive manufacturing resources to serve large-scale manufacturing tasks, the space of feasible solutions is huge, resulting in many conventional search algorithms no longer being applicable. In this paper, we considered resource allocation as the key procedure for real-time scheduling, and an ANN (Artificial Neural Network) based model is established to predict the task completion status for resource allocation among candidates. The trained ANN model has high prediction accuracy, and the ANN-based scheduling approach performs better than the preferred method in terms of the optimization objectives, including total cost, service satisfaction, and make-span. In addition, the proposed approach has potential in the application for smart manufacturing or Industry 4.0 because of its high response performance and good scalability.

An Advanced LSTM Model for Optimal Scheduling in Smart Logistic Environment: E-Commerce Case

“…That requires effective optimization approaches to meet the high demand, realworld variations, with a greater focus to enhance the functioning of e-commerce systems. [41] 96,43 sec b-Xiaofeng et al [42] 139,5 sec c-Zhang et al [43] 157,24 sec d-Khan et al [25] 162,41 sec e-Ali et al [27] 169,82 sec f-Chen et al [44] 94,26 sec g -Our proposed method 58,84 sec…”

“…: maximizing the revenues, ensuring the workload performance, reducing energy consumption, as well as reducing users' costs and ecological impacts. Chen et al [44] Real-Time Scheduling ANN-based model task completion prediction to manage resource allocation. The proposed method offers promising potentials for smart manufacturing or Industry 4.0. by enhancing the system capacities and ensuring an equitable and effective delivery to promote e-commerce growth.…”

“…Then, a comparative analysis was conducted to evaluate the proposed system compared to the most FIGURE 6: The adopted process for the processing of tasks: This process begins with data pre-processing, which is the phase of data extraction, transformation, and loading, taking into account the list of selected characteristics. Then a phase of data pre-processing is performed for the proper modeling of resource planning in time sequences and resource labeling relevant anterior literature methods, presented in table 1 (a-Zhang et al [41], b-Xiaofeng et al [42], c-Zhang et al [43], d-Khan et al [25], e-Ali et al [27], f-Chen et al [44], g-Our proposed method) For the comparative analysis, the training parameters of all the tested methods were initialized. For the machine learning-based ones, they were set using the initialization of He et al [55] and optimized by stochastic gradient descent(a momentum of 0.9, and an initial learning rate of 10e-6).…”

“…To facilitate the conduct of this study, we adopted the same data sets for a fair comparison. Consequently, the performance of the proposed method was evaluated by comparison with the relevant existing resource allocation strategies already defined in the evaluation baselines (a-Zhang et al [41], b-Xiaofeng et al [42], c-Zhang et al [43], d-Khan et al [25], e-Ali et al [27], f-Chen et al [44], and g-Our proposed method).…”

Impact of digital twin technology on the real-time optimisation of material handling processes

“…The cloud manufacturing platforms represents complex manufacturing systems, where tasks must be allocated to resources scattered in various locations for processing. In this complex system artificial neural networks make it possible to schedule tasks in real-time [8] and these solutions can be added to smart manufacturing solutions because of its high response performance. The real-time optimization has a wide range of application areas.…”