Research on manufacturing grid resource service optimal-selection and composition framework

Tao, Fei; Zhang, L.; Lü, Ke; Zhao, Dongming

doi:10.1080/17517575.2010.540677

Cited by 98 publications

(24 citation statements)

References 27 publications

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“…Further, for overcoming the limitations, more valuable operators should be integrated and applied in complex real-world problems. Also, the design of DC-IOA in distributed and uncertain environments such as grid (e.g., manufacturing grid system [2,3,6] and cloud (e.g., cloud manufacturing system [16][17][18][19][20] is also one of the important future works.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Configuration of Intelligent Optimization Algorithms

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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Since genetic algorithm (GA) presented decades ago, large amount of intelligent optimization algorithms and their improvements and mixtures have been putting forward one after another. However, little works have been done to extend their applications and verify their competence in different problems. For each specific complex problem, people always take a long time to find appropriate intelligent optimization algorithm and develop improvements. To overcome these shortcomings, new dynamic configuration methods for intelligent optimization algorithms (DC-IOA) [1] is presented in this paper on the basis of the requirements of three kinds of algorithm users. It separates the optimization problems and intelligent optimization algorithms, modularizes each step of algorithms and extracts their core operators. Based on the coarse-grained operator modules, three-layer dynamical configurations, i.e. parameter-based configuration, operator-based configuration and algorithm-based configuration, are fully exploited and implemented. Under these methods, dozens of hybrid and improved intelligent optimization algorithms can be easily produced in a few minutes just based on several configurable operator modules. Also, problem-oriented customizations in configurations can further extend the application range and advance the efficiency of the existing operators enormously. Experiments based on the established configuration platform verify the new configuration ways of applying and improving intelligent optimization algorithm for both numerical and combinatorial optimization problems in industries on aspects of flexibility, robustness, and reusability. Concept and Mainframe of DC-IOAIn this section, we propose the mainframe, process and implementation of DC-IOA [1].As we know, researchers have already developed many algorithm libraries for different use. HeuristicLab is an integrated platform for evolutionary algorithm, and EvA2 is a lib for general optimal algorithms.

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Section: Discussionmentioning

confidence: 99%

Dynamic Configuration of Intelligent Optimization Algorithms

Tao

Laili

Zhang

2014

Springer Series in Advanced Manufacturing

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“…Besides, we have also achieved manufacturing cloud formation, aggregation, storage method, and the method that dynamically build and deploy, decompose, Optimal allocation of resources services cooperative scheduling in CMfg (Lin et al 2012a, b;Ren et al 2012;Yang et al 2012a;Tao et al 2011b). The life cycle of CMfg involved the model-based activities.…”

Section: Construction and Management Technologies For Virtual Manufacmentioning

confidence: 98%

“…Existing works on service composition are primarily based on the requirements of service composition, such as describing language supporting service composition, service composition framework, mechanism and method for service composition, and service composition validation. Few works have been carried out from the perspective of combinable relationship among services and service network (Guo et al 2012;Tao et al 2011bTao et al , 2012b.…”

Section: Operation Technology For Virtual Manufacturing Services Envimentioning

confidence: 99%

Research and Applications of Cloud Manufacturing in China

Zhang

Chai³

et al. 2014

Cloud-Based Design and Manufacturing (CBDM)

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This chapter leads to the achievements of research and applications in Cloud Manufacturing (CMfg) carried out by the authors' team. First of all, the meaning of Big Manufacturing is given, and the challenges and strategies for manufacturing industries as well as the content and development of manufacturing informatization are analyzed. Then the definition, concept model, system architecture, technologies, typical technical characteristics, and service object and content of CMfg are put forward. Moreover, discussions are shown to prove that CMfg *Notation: Manufacturing appearing in this chapter refers to Big Manufacturing that covers all the activities and processes in the product lifecycle, from the market demand demonstration, the design and simulation, the production and fabrication, the test and experiment, the operating maintenance to the final scraping process. is a new paradigm and approach to realize manufacturing informatization, which materializes and extends Cloud Computing in the manufacturing domain. The current status of the technologies, applications, and industries for CMfg in China are presented. Concerns are mostly focused on the 12 key technologies for the CMfg System (also called Manufacturing Cloud), including system overall technology, sensing technology for manufacturing resource and capability, virtualization and service technology for manufacturing resource and capability, construction and management technology for virtual manufacturing environment, operation technology for virtual manufacture environment, evaluation technology for virtual manufacturing environment, trusted service technology for virtual manufacturing, management of knowledge, model and data, pervasive human-computer interaction technology, application technology of service platform, informatized manufacturing technology system, and product service technology. The academic research results of the authors' team are presented. Furthermore, four typical CMfg cases which have been successfully implemented in group enterprises and mid-small enterprise clusters are described as well as cases introducing smart manufacturing into smart city. Finally, future works and development prospect for CMfg are raised.

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“…It reaps the benefits of cloud computing [7,8], service-oriented technologies (SOTs) [9], Internet of things (IoT) [10][11][12][13][14][15], and big data [16][17][18] to provide fully scale sharing, free circulation and transaction, and on-demand use of globally distributed manufacturing resources and capabilities [19].…”

Section: Introductionmentioning

confidence: 99%

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

Liu

Zhou

2016

Int J Adv Manuf Technol

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Cloud manufacturing (CMfg) has drawn extensive attentions from industrial community and academia. Quality of service (QoS)-aware service composition is critical to the on-demand use of distributed manufacturing resources and capabilities in CMfg systems. However, most previous work plainly composed composite services by the approach of oneto-one mapping-based service composition (OOM-SC), which leads to drawbacks to both the overall QoS of composite services and the success rate of service composition. To circumvent this, an approach of synergistic elementary service group-based service composition (SESG-SC) is proposed in this paper. It releases the assumption of one-to-one mapping between elementary services and subtasks, allowing a free combination of multiple functionally equivalent elementary services into a synergistic elementary service group (SESG) to perform each subtask collectively, thereby bettering the overall QoS and achieving more acceptable success rate. To introduce an optimal construction of SESGs into the optimization model of QoS-aware service composition, three kinds of redundant structures within SESGs are discussed and the corresponding QoS evaluation formulas are also proposed. To deal with the increasing computing complexity of the optimization model, an algorithm named matrix-coded genetic algorithm with collaboratively evolutional populations (MCGA-CEP) is designed in the current study. The experimental results indicate that the proposed SESG-SC approach significantly outperforms the previous approaches, and the proposed MCGA-CEP is sound on performance-wise.

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Research on manufacturing grid resource service optimal-selection and composition framework

Cited by 98 publications

References 27 publications

Dynamic Configuration of Intelligent Optimization Algorithms

Dynamic Configuration of Intelligent Optimization Algorithms

Research and Applications of Cloud Manufacturing in China

QoS-aware service composition for cloud manufacturing based on the optimal construction of synergistic elementary service groups

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