From IoT-based cloud manufacturing approach to intelligent additive manufacturing: industrial Internet of Things—an overview

• Both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based algorithms for scheduling of decentralized robot services are proposed. • Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. • Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distributed robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently. 

Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning

Sequence generation for multi-task scheduling in cloud manufacturing with deep reinforcement learning

Abstract Additive Manufacturing (AM) is a disruptive production technology that challenges many well-established, conventional practices in Operations and Supply Chain Management decisions. Consequently, new context-specific decision problems have appeared in this area, while existing decision problems must be adapted or extended to this context. In this overview, these decision problems in AM are highlighted and classified, describing the different decision in an unified manner and citing the underlying OR techniques that have been applied to solve them. The aim of the paper is that, by presenting an overview of the AM-related problems in a systematic, OR- or Decision-Making-centred (rather than technology-centred) manner, the OR community can become more aware of this stream of research and thus be more active and contribute with some high-quality work. Open research challenges, as well as avenues for future research are also discussed. 

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An overview on the use of operations research in additive manufacturing

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Game theory based multi-task scheduling of decentralized 3D printing services in cloud manufacturing

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Game theory based multi-task scheduling of decentralized 3D printing services in cloud manufacturing