Study on Objects Ordering for Manufacturing Cloud Platform

Jiang, Xu; Ping-lu, Chen

doi:10.1007/978-1-4471-4856-2_52

Cited by 1 publication

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“…Thus, we use the terms CM and MC separately to distinguish the manufacturing model (CM) and platform (MC), clarifying the manufacturing process in CM. The MC is a manufacturing platform made up of universal and renewable manufacturing resources with flexibility [7]. In the MC, diversified manufacturing resources can be intelligently sensed and connected into the wider Internet, and automatically managed and controlled by means of the Internet of Things (IoT) and related technologies (e.g., radio frequency identification (RFID), wireless sensor network, embedded system, etc.).…”

Section: Introductionmentioning

confidence: 99%

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

2016

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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.

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Section: Introductionmentioning

confidence: 99%