@inbook{c9fc39fa3c20410aab368bc494f153ad,
title = "Service composition in cloud manufacturing: A DQN-based approach",
abstract = "Cloud manufacturing is a new service-oriented manufacturing model that integrates distributed manufacturing resources to provide on-demand manufacturing services over the Internet. Service composition that builds larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers{\textquoteright} complex requirements is an important issue in cloud manufacturing. Meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. However, these algorithms require complex design flows and lack adaptability to dynamic environment. Deep reinforcement learning provides an alternative approach for solving cloud manufacturing service composition issues. This chapter proposes a deep Q-network (DQN) based approach for service composition in cloud manufacturing, which is able to find optimal service composition solutions through repeated training and learning. Results of experiments that take into account changes of service scales and service unavailability reveal the scalability and robustness of the DQN algorithm-based service composition approach.",
keywords = "Cloud manufacturing, Deep Q-network, Deep reinforcement learning, Service composition",
author = "Haifeng Zhang and Yongkui Liu and Huagang Liang and Lihui Wang and Lin Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.",
year = "2020",
doi = "10.1007/978-3-030-43177-8\_12",
language = "英语",
series = "International Series in Operations Research and Management Science",
publisher = "Springer",
pages = "239--254",
booktitle = "International Series in Operations Research and Management Science",
address = "德国",
}