Optimal configuration of manufacturing resources based on transportation factors in networked manufacturing

2010 ◽  
Vol 30 (11) ◽  
pp. 2902-2905
Author(s):  
Li BA ◽  
Ming-shun YANG ◽  
Xin-qin GAO ◽  
Xiao-qing WU
Keyword(s):  
Optimal Configuration ◽  
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.

scholarly journals Optimized Configuration of Manufacturing Resources for Middle and Lower Batch Customization Enterprises in Cloud Manufacturing Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yinyun Yu ◽  
Wei Xu
Keyword(s):  
Customer Satisfaction ◽  
Manufacturing Systems ◽  
Programming Model ◽  
Model Simulation ◽  
Cloud Manufacturing ◽  
Economic Benefits ◽  
Optimal Configuration ◽  
Manufacturing Environment ◽  
Kano Model ◽  
Manufacturing Resources

The optimal configuration of manufacturing resources in the cloud manufacturing environment has always been the focus of research on various advanced manufacturing systems. Aiming at the problem of manufacturing resources optimization configuration for middle and lower batch customization enterprises in cloud manufacturing environment, this paper gives a bi-level programming model for manufacturing resources optimization configuration in cloud manufacturing environment which fully considers customer satisfaction and enterprise customization economic benefits. The method firstly identifies the relationship between customer demands and customer satisfaction through questionnaires and quantifies the Kano model effectively. Then, it uses Quality Function Deployment (QFD) to transform customer demand characteristics into engineering characteristics and integrates the qualitative and quantitative results of the Kano model. Next, the method establishes enterprise economic benefits function according to the factors of order quantity and input cost. Furthermore, a comprehensive nonlinear bi-level programming model is established based on cost, time, and quality constraints. The model is solved by intelligent algorithm. Finally, the validity and feasibility of the model are verified by model simulation of actual orders of an enterprise. This method effectively realizes the optimal configuration of manufacturing resources in the cloud manufacturing environment, while maximizing the interests of both suppliers and demanders.

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