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Sustainability 2016, 8(12), 1239; doi:10.3390/su8121239

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

Department of Industrial and Management Engineering, Hanyang University, Ansan 15588, Korea
School of Business Administration, College of Business and Economics, Chung-Ang University, Seoul 06974, Korea
Author to whom correspondence should be addressed.
Academic Editor: Ilkyeong Moon
Received: 14 October 2016 / Revised: 7 November 2016 / Accepted: 23 November 2016 / Published: 29 November 2016
(This article belongs to the Special Issue Sustainability in Supply Chain Management)
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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. View Full-Text
Keywords: enterprise network composition problem; cloud manufacturing; genetic algorithm; inventory model enterprise network composition problem; cloud manufacturing; genetic algorithm; inventory model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ahn, G.; Park, Y.-J.; Hur, S. The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing. Sustainability 2016, 8, 1239.

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