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The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing

Author

Listed:
  • Gilseung Ahn

    (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea)

  • You-Jin Park

    (School of Business Administration, College of Business and Economics, Chung-Ang University, Seoul 06974, Korea)

  • Sun Hur

    (Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea)

Abstract

As a service oriented and networked model, cloud manufacturing (CM) has been proposed recently for solving a variety of manufacturing problems, including diverse requirements from customers. In CM, on-demand manufacturing services are provided by a temporary production network composed of several enterprises participating within an enterprise network. In other words, the production network is the main agent of production and a subset of an enterprise network. Therefore, it is essential to compose the enterprise network in a way that can respond to demands properly. A properly-composed enterprise network means the network can handle demands that arrive at the CM, with minimal costs, such as network composition and operation costs, such as participation contract costs, system maintenance costs, and so forth. Due to trade-offs among costs (e.g., contract cost and opportunity cost of production), it is a non-trivial problem to find the optimal network enterprise composition. In addition, this includes probabilistic constraints, such as forecasted demand. In this paper, we propose an algorithm, named the dynamic enterprise network composition algorithm (DENCA), based on a genetic algorithm to solve the enterprise network composition problem. A numerical simulation result is provided to demonstrate the performance of the proposed algorithm.

Suggested Citation

  • Gilseung Ahn & You-Jin Park & Sun Hur, 2016. "The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing," Sustainability, MDPI, vol. 8(12), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1239-:d:83931
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    References listed on IDEAS

    as
    1. Wang, Zhiqiang & Zhang, Min & Sun, Hongyi & Zhu, Guilong, 2016. "Effects of standardization and innovation on mass customization: An empirical investigation," Technovation, Elsevier, vol. 48, pages 79-86.
    2. Tsang, Eric W. K., 2014. "Old and New," Management and Organization Review, Cambridge University Press, vol. 10(03), pages 390-390, November.
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    Cited by:

    1. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    2. Gilseung Ahn & You-Jin Park & Sun Hur, 2017. "Probabilistic Graphical Framework for Estimating Collaboration Levels in Cloud Manufacturing," Sustainability, MDPI, vol. 9(2), pages 1-17, February.

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