scholarly journals A Hybrid GA-TS Algorithm for Optimizing Networked Manufacturing Resources Configuration

2013 ◽  
Vol 7 (5) ◽  
pp. 2045-2053 ◽  
Author(s):  
Yan Zhan ◽  
Jiansha Lu ◽  
Shiyun Li
Keyword(s):  
Networked Manufacturing ◽  
Manufacturing Resources

Characteristics of Networked Manufacturing Resources

2010 ◽  
Vol 428-429 ◽  
pp. 528-532
Author(s):  
Kai Yin ◽  
Juan Tu ◽  
Xiao Jun Wang
Keyword(s):  
Resource Management ◽  
Data Exchange ◽  
Technology Development ◽  
Manufacturing Technology ◽  
Networked Manufacturing ◽  
Exchange Form ◽  
Manufacturing Resources ◽  
Network Manufacture ◽  
Classification Management

Along with the networked manufacturing technology development and deep research of resources classification, the question of resource management appears prominently. Resources attribute constitution has been analyzed under the network manufacture environment from the resources classification management; the resources attribute and the data exchange form have been proposed under the isomerism environment

Study on the Multi-Granularity Virtualization of Manufacturing Resources

2013 ◽  
Author(s):  
Chunsheng Hu ◽  
Chengdong Xu ◽  
Xiaobo Cao ◽  
Pengfei Zhang
Keyword(s):  
Large Scale ◽  
Cloud Manufacturing ◽  
Virtual Manufacturing ◽  
Networked Manufacturing ◽  
Manufacturing Resources ◽  
Manufacturing Mode

As a new kind of networked manufacturing mode, Cloud Manufacturing needs to construct a large-scale virtual manufacturing resources pool firstly. For a reasonable and effective construction of the virtual manufacturing resources pool, the point of multi-granularity virtualization is proposed. Firstly, by analyzing the process of resources virtualization, the meanings of manufacturing resources, virtualization modeling and virtualization accessing are stated, and the relationships between them are illustrated; secondly, by analyzing the compositionality of resources, two resources categories are deduced; thirdly, the granularity factor, which have serious impacts on the resources-virtualization, resources-matching and resources-scheduling, are discussed; finally, a multi-granularity virtualization method of manufacturing resources is proposed.

Research on Strategies of Networked Manufacturing Resources Configuration Based on Evolutionary Game

2012 ◽  
Vol 430-432 ◽  
pp. 1330-1334 ◽  
Author(s):  
Yan Zhan ◽  
Jian Sha Lu ◽  
Xue Hong Ji
Keyword(s):  
Complex Dynamics ◽  
Replicator Dynamics ◽  
Evolutionary Game ◽  
Networked Manufacturing ◽  
Evolutionary Mechanisms ◽  
System A ◽  
Long Run ◽  
Alternative Approach ◽  
Existence And Stability ◽  
Manufacturing Resources

Economic theories of managing resources, traditionally assume that individuals are perfectly rational and thus able to compute the optimal configuration strategy that maximizes their profits. The current paper presents an alternative approach based on bounded rationality and evolutionary mechanisms. It is assumed that network node users face a choice between two resource strategies in real networked manufacturing resources configuration problem (NMRCP). The evolution of the distribution of strategies in the population is modeled through a replicator dynamics equation. The latter captures the idea that strategies yielding above average profits are more demanded than strategies yielding below average profits, so that the first type ends up accounting for a larger part in the population. From a mathematical perspective, the combination of resource and evolutionary processes leads to complex dynamics. The paper presents the existence and stability conditions for each steady-state of the system. A main result of the paper is that under certain conditions both strategies can survive in the long-run.

Manufacturing Resources Optimization Deployment for Complicated Parts Based on Network

2011 ◽  
Vol 186 ◽  
pp. 89-93
Author(s):  
Wen Li Peng ◽  
Wen Ni Zhang ◽  
Hai Ming Jin
Keyword(s):  
Optimization Problem ◽  
Geometric Feature ◽  
Manufacturing Cost ◽  
Networked Manufacturing ◽  
Time Cost ◽  
Time To Market ◽  
Production Type ◽  
Complex Part ◽  
Manufacturing Resources ◽  
Manufacturing Time

Agility of physical manufacturing unit is the competitive advantage in the global manufacturing environment. It is believed that the agility can be realized by dynamically optimization deployment of networked manufacturing resources. To solve this problem, logical manufacturing unit (LMU) and logical manufacturing process (LMP) are proposed and defined to decompose and model networked manufacturing task according to the process of complex part. When selecting manufacturing resources for these manufacturing tasks, many factors should be taken into account. However, manufacturing cost, time to market and manufacturing quality are the most important factors. In this paper, networked manufacturing resources pre-deployment is carried out to find candidate manufacturing resources based on manufacturing resources abilities, such as part family, geometric feature, material type, rough type, dimension range, machining method, precision grade and production type. Then, taking transportation time and cost besides manufacturing time, cost and quality into consideration, the objectives and restrictions of manufacturing resources optimization deployment are analyzed, and manufacturing resources optimization deployment problem is considered as a multi-objectives optimization problem.

GetCM: A Vision of Cloud Manufacturing

2011 ◽  
Vol 213 ◽  
pp. 388-392 ◽  
Author(s):  
Jing Tao Zhou ◽  
Hai Cheng Yang ◽  
Ming Wei Wang ◽  
Shi Kai Jing ◽  
Rong Mo
Keyword(s):  
Large Scale ◽  
Cloud Manufacturing ◽  
Networked Manufacturing ◽  
Economic Aspects ◽  
Basic Principles ◽  
Dynamic Provisioning ◽  
On Demand ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Modern Manufacturing

Surviving in an increasing globalization, distribution and flexibility environment, modern manufacturing requires an extremely flexible, self-adaptive foundation capable of dynamic provisioning, coordinating and using infinite manufacturing resources available on demand over large-scale computer networks. In contrast to the conventional networked manufacturing approach, the cloud manufacturing vision (GetCM) introduced in this paper promises elasticity, flexibility and adaptability through the on-demand provisioning of manufacturing resources as a utility by reflecting the basic principles of cloud computing. The discussion is made from technological, functional, economic aspects to provide evidence of the benefits from GetCM in the context of networked manufacturing resource access, provision, sharing and coordination. A primary architecture for GetCM is introduced based on the analysis of key criteria in realizing the vision of a function for cloud manufacturing. Focuses of this paper are placed on the vision and the outline of GetCM architecture.

scholarly journals Cloud-Edge Collaboration-Based Knowledge Sharing Mechanism for Manufacturing Resources

2021 ◽  
Vol 11 (7) ◽  
pp. 3188
Author(s):  
Xixiang Wang ◽  
Jiafu Wan
Keyword(s):  
Knowledge Sharing ◽  
Comparative Experiment ◽  
Mixed Flow ◽  
Processing Technologies ◽  
Experiment Platform ◽  
Low Degree ◽  
Manufacturing Resources ◽  
Ontological Model ◽  
Manufacturing Environments ◽  
Similar Processing

The development of multi-variety, mixed-flow manufacturing environments is hampered by a low degree of automation in information and empirical parameters’ reuse among similar processing technologies. This paper proposes a mechanism for knowledge sharing between manufacturing resources that is based on cloud-edge collaboration. The manufacturing process knowledge is coded using an ontological model, based on which the manufacturing task is refined and decomposed to the lowest-granularity concepts, i.e., knowledge primitives. On this basis, the learning process between devices is realized by effectively screening, matching, and combining the existing knowledge primitives contained in the knowledge base deployed on the cloud and the edge. The proposed method’s effectiveness was verified through a comparative experiment contrasting manual configuration and knowledge sharing configuration on a multi-variety, small-batch manufacturing experiment platform.

Sign in / Sign up

Export Citation Format

Share Document