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Printed Circuit Board (PCB) manufacturing is a kind of energy-intensive and pollution-intensive industries. With the increment of individualized requirements, PCB manufacturers face massive customized orders with a variety of specifications. The individualized customization on orders results in large differentials of the profit, energy consumption, and environmental pollution. Making energy-efficient order acceptance decisions can reduce carbon consumption and improve material utilization during the whole manufacturing process. An order acceptance decision model is established based on a loosely-coupled integration of deep learning and reinforcement learning techniques. Firstly, different from a simple assumption of the linear cost function in a small-scale manufacturing system, the deep learning algorithm is presented for accurately predicting the production cost, makespan, and carbon consumption of incoming PCB orders in the large-scale manufacturing system. Secondly, these predicted cleaner production indicators are combined with original order features to perform a reinforcement learning-based order acceptance decision. The proposed loosely-coupled deep reinforcement learning approach is verified with a dataset built based on data collected from a PCB manufacturer in China. This research is expected to provide an environment-friendly order acceptance decision-making approach for sustainable manufacturing in the Industry 4.0 context. [ABSTRACT FROM AUTHOR]

Under the promotion of sustainable development goals, cleaner production (CP) has become an important strategy for energy-intensive manufacturing industries to maintain their competitiveness. Studies have shown that the implementation of Industry 4.0 (I4.0) can effectively promote the CP process in manufacturing. However, existing studies often focus on specific scenarios, limiting a comprehensive assessment of the industry's overall status. This paper aims to provide a comprehensive overview of the main research areas of I4.0 and its key impacts on CP by employing systematic mapping studies. By reviewing 121 studies retrieved from the Web of Science, a hierarchical analysis framework centered on the product life cycle (PLC) has been introduced. This framework provides a holistic analysis of sustainable intelligent manufacturing, summarizing the application of I4.0 and its impact on CP across the PLC stages. The main findings reveal a growing focus on I4.0 and CP, with the manufacturing and maintenance stages of the PLC being primary research scenarios. Moreover, attention should be directed towards integrating clean technologies, bolstering industrial data security, fostering circular economy practices, and exploring emerging fields like Industry 5.0. Moreover, to better explain the impact of this study, management implications are also provided from the three dimensions of theory, practice, and policy. These viewpoints and conclusions are of great significance for guiding future research and practical applications. • A comprehensive review of Industry 4.0 and cleaner production is conducted. • Systematic mapping and bibliometric methods are employed. • A hierarchical framework based on product life cycle is proposed. • The current challenges and future prospects are discussed. • Management implications from theory, practice, and policy are provided. [ABSTRACT FROM AUTHOR]