Service Composition in Cloud Manufacturing: A DQN-Based Approach

Haifeng Zhang, Yongkui Liu (), Huagang Liang, Lihui Wang () and Lin Zhang
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Haifeng Zhang: Chang’an University
Yongkui Liu: Xidian University
Huagang Liang: Chang’an University
Lihui Wang: KTH Royal Institute of Technology
Lin Zhang: Beihang University

Chapter Chapter 12 in Scheduling in Industry 4.0 and Cloud Manufacturing, 2020, pp 239-254 from Springer

Abstract: 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’ 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; Service composition; Deep reinforcement learning; Deep Q-network (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-43177-8_12

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DOI: 10.1007/978-3-030-43177-8_12

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