scholarly journals Research on optimal selection of services and optimal allocation of resources in cloud manufacturing environment

2019 ◽  
Vol 1314 ◽  
pp. 012167
Author(s):  
Xianxian Cai ◽  
Minghai Yuan ◽  
Zhuo Zhou ◽  
Chao Sun
Keyword(s):  
Optimal Allocation ◽  
Cloud Manufacturing ◽  
Optimal Selection ◽  
Allocation Of Resources ◽  
Manufacturing Environment ◽  
Selection Of

scholarly journals Optimal selection policies for a sequence of candidate drugs

2008 ◽  
Vol 40 (02) ◽  
pp. 359-376 ◽  
Author(s):  
C. Charalambous ◽  
J. C. Gittins
Keyword(s):  
Pharmaceutical Companies ◽  
Optimal Selection ◽  
Efficient Allocation ◽  
Allocation Of Resources ◽  
Preclinical Research ◽  
Research Project ◽  
Starting Point ◽  
Optimal Policies ◽  
Efficient Allocations ◽  
Selection Of

Pharmaceutical companies have to face huge risks and enormous costs of production before they can produce a drug. Efficient allocation of resources is essential to help in maximizing profits. Yu and Gittins (2007) described a model and associated software for determining efficient allocations for a preclinical research project. This is the starting point for this paper. We provide explicit optimal policies for the selection of successive candidate drugs for two restricted versions of the Yu and Gittins (2007) model. To some extent these policies are likely to be applicable to the unrestricted model.

Research on the Selection Problem of Machine Tools in Cloud Manufacturing Environment

2014 ◽  
Vol 800-801 ◽  
pp. 649-653 ◽  
Author(s):  
Shu Qi Wang ◽  
Yun Xia Jiang ◽  
Min Li Zheng ◽  
Dong Nan Sun ◽  
Xiao Liang Cheng
Keyword(s):  
Genetic Algorithms ◽  
Machine Tools ◽  
Selection Process ◽  
Cloud Manufacturing ◽  
Optimal Selection ◽  
Cloud Environment ◽  
Manufacturing Environment ◽  
On Demand ◽  
Matching Process ◽  
Multi Level

This paper proposes a method to choose machine resources in order to realize the on-demand use of machine resources in cloud manufacturing environment. A convergence mode of the machine resources is described and the selection process is given. A multi-level matching process of machine tools is proposed. Different matching methods are designed for different parameter types of machining tasks and machine resources, and then machine resources are screened according to the requirements of machining tasks to form the machine resources candidate sets. Then a multi-objective optimal selection model of machine resources is constructed, regarding minimization of costs and time and maximization of service quality and reputation as the target, which is solved by using genetic algorithms. Finally, the algorithm is analyzed and validated with an example, and a kind of solution thinking and method is provided to select machine tools in manufacturing cloud environment.

Research on the Measurement Method of Flexibility of Resource Service Composition in Cloud Manufacturing

2010 ◽  
Vol 139-141 ◽  
pp. 1451-1454 ◽  
Author(s):  
Hua Guo ◽  
Lin Zhang ◽  
Fei Tao ◽  
Lei Ren ◽  
Yong Liang Luo
Keyword(s):  
Service Composition ◽  
Measurement Method ◽  
Cloud Manufacturing ◽  
Added Value ◽  
Optimal Selection ◽  
Dynamic Changes ◽  
Service Oriented ◽  
Manufacturing Resource ◽  
Resource Service ◽  
Selection Of

In order to overcome the bottlenecks of traditional network manufacturing, a new service-oriented networked manufacturing model, i.e. the cloud manufacturing (CMfg), was proposed recently. As an effective method for the realization of the added value of manufacturing resource, resource service composition (RSC) plays an important role in the implementation of CMfg. In view of the issue of dynamic changes occurred during RSC in CMfg, this paper presents the concept of flexibility of RSC as well as the idea of optimal-selection of RSC based on flexibility, which can enable RSC to have the ability to adapt to the dynamic changes. Meantime, the measurement method of flexibility of RSC is investigated. The optimal-selection of RSC based on flexibility can be achieved through quantitative evaluation of flexibility.

scholarly journals Optimal selection policies for a sequence of candidate drugs

2008 ◽  
Vol 40 (2) ◽  
pp. 359-376 ◽  
Author(s):  
C. Charalambous ◽  
J. C. Gittins
Keyword(s):  
Pharmaceutical Companies ◽  
Optimal Selection ◽  
Efficient Allocation ◽  
Allocation Of Resources ◽  
Preclinical Research ◽  
Research Project ◽  
Starting Point ◽  
Optimal Policies ◽  
Efficient Allocations ◽  
Selection Of

Pharmaceutical companies have to face huge risks and enormous costs of production before they can produce a drug. Efficient allocation of resources is essential to help in maximizing profits. Yu and Gittins (2007) described a model and associated software for determining efficient allocations for a preclinical research project. This is the starting point for this paper. We provide explicit optimal policies for the selection of successive candidate drugs for two restricted versions of the Yu and Gittins (2007) model. To some extent these policies are likely to be applicable to the unrestricted model.

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