A cloud-based manufacturing control system with data integration from multiple autonomous agents

Răileanu, Silviu; Anton, Florin Daniel; Borangiu, Theodor; Anton, Silvia; Nicolae, Maximilian

doi:10.1016/j.compind.2018.08.004

Cited by 33 publications

(10 citation statements)

References 15 publications

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“…Their researches were more focused on how to coordinate operations between agents through computer simulation, and how to pass this distributed structure calculation results in theoretically optimized scheduling results. Later, in order to improve the overall performance of the system, the researchers further proposed various functional agents including supervisor agent (SA), 28 database agent (DA), 29 job agent (JA), 30 etc.…”

Section: Related Workmentioning

confidence: 99%

A practical approach for multiagent manufacturing system based on agent computing nodes

Zhang

Tang

Zhu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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With the increasing requirement for personalized customization service, discrete manufacturing workshop, as the parts processing unit in manufacturing system, is expected for more agile and fast adaptation to environment changes, dynamically handling production tasks according to resource conditions. Simultaneously, distributed artificial intelligence system (e.g. multiagent manufacturing system and the holonic manufacturing system) has been considered as an important approach for developing industrial applications to solve the problems of complexity, uncertainty, and dynamic in the modern manufacturing environment. But the lack of universality and the difficulty in deployment have restricted the use of distributed artificial intelligence in actual industrial sites. For this issue, a new concept of agent computing node is proposed in this paper to enable the realization of multiagent manufacturing system. Adaptation layer, information development layer, and intelligent analysis layer are investigated for standardizing the configuration mode of agent computing node. Cooperating agent computing node with the radio frequency identification-based dynamic recognition technology for workpiece machining process is presented in this paper, and a practical approach for multiagent manufacturing system is considered, which can apply the functions regarding to deployment of dynamic scheduling and plug-and-play. A laboratory discrete manufacturing workshop system is used as a case study to prove the feasibility of this approach. In addition, a verification in industry is carried out, and the result proves the universality of this approach.

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Section: Related Workmentioning

confidence: 99%

A practical approach for multiagent manufacturing system based on agent computing nodes

Zhang

Tang

Zhu

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…rough Delphi research on the attributes of cloud service quality, Lang et al [6] determined that service function, legality, contract, geographical location, and flexibility were the highest service quality evaluation criteria for cloud service selection. Rȃileanu et al [7] combined energy consumption with product scheduling and resource allocation and proposed a design method of high availability production management system based on cloud. Helo and Hao [8] proposed a dynamic optimization model of production planning and control based on cloud for sheet metal processing and developed a scheduling prototype system based on the genetic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Yao

Liu

2020

Mathematical Problems in Engineering

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Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

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“…[87] proposes a reliability assessment model of CM systems, assuming a service oriented architecture that is not specified in more detail. Agent-based manufacturing methods have been adapted to the cloud, aiming at advantages in virtual manufacturing cell formation [88] and improved performance in energy consumption, scheduling, resource allocation and real time data collection [89]. [90] propose a distributed blockchain-based architecture to handle the trust and security issues in a scalable manner.…”

Section: Cloud Manufacturingmentioning

confidence: 99%

Adapting an agile manufacturing concept to the reference architecture model industry 4.0: A survey and case study

Yli-Ojanperä

Sierla

Papakonstantinou

et al. 2019

Journal of Industrial Information Integration

124

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Industry 4.0 architecture has been studied in a large number of publications in the fields of Industrial Internet of Things, Cyber Physical Production Systems, Enterprise Architectures, Enterprise Integration and Cloud Manufacturing. A large number of architectures have been proposed, but none of them has been adopted by a large number of research groups. Two major Industry 4.0 reference architectures have been developed by industry-driven initiatives, namely the German Industry 4.0 and the US-led Industrial Internet Consortium. These are the Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture, which are being standardized by the International Electrotechnical Commission and the Object Management Group, respectively. The first research goal of this article is to survey the literature on Industry 4.0 architectures in a factory context and assess awareness and compatibility with Reference Architecture Model Industry 4.0 and Industrial Internet Reference Architecture. The second research goal is to adapt a previously proposed advanced manufacturing concept to Reference Architecture Model Industry 4.0. With respect to the first research goal, it was discovered that only a minority of researchers were aware of the said reference architectures and that in general authors offered no discussion about the compatibility of their proposals with any internationally standardized reference architecture for Industry 4.0. With respect to the second research goal, it was discovered that Reference Architecture Model Industry 4.0 was mature with respect to communication and information sharing in the scope of the connected world, that further standardization enabling interoperability of different vendors' technology is still under development and that technology standardization enabling executable business processes between networked enterprises was lacking.

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A cloud-based manufacturing control system with data integration from multiple autonomous agents

Cited by 33 publications

References 15 publications

A practical approach for multiagent manufacturing system based on agent computing nodes

A practical approach for multiagent manufacturing system based on agent computing nodes

Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

Adapting an agile manufacturing concept to the reference architecture model industry 4.0: A survey and case study

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