A Novel Service Composition Algorithm for Cloud-Based Manufacturing Environment

Zhu, Linan; Li, Penghang; Shen, Guojiang; Liu, Zhi

doi:10.1109/access.2020.2976164

Cited by 11 publications

(9 citation statements)

References 32 publications

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“…Formula (29) reflects the comprehensive performance of each DCR member in capacity, evaluation and cost. So, the comprehensive capacity index, comprehensive evaluation index and comprehensive cost index of each member resource of DCR are shown in (30), (31), and (32) respectively.…”

Section: B Dcr Evaluation Methods and Dcr Comprehensive Evaluation Mamentioning

confidence: 99%

“…In most papers, fine-grained resources were encapsulated for servitization directly, and then the generated services were utilized. Linan Zhu et al [31] proposed a manufacturing service composition scheme named as Multi-Module Subtasks Collaborative Execution for Cloud Manufacturing Service Composition (MMSCE-CMSC), which was used to select services from service pool to generate a composite service with the given objective optimization. Feng Li et al [32] proposed a two-level multi-task scheduling model, which was used to find a better resource schedule.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Granularity Modeling and Aggregation of Design Resources in Cloud Manufacturing

et al. 2020

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In most Cloud Manufacturing (CMfg) systems, Design Resource (DR) is encapsulated into cloud service under a fine-grained condition. However, due to the small granularity of DRs provided by cloud provider, it is difficult for the cloud services to match with design tasks if there is no initiative resource. For example, because of the lack of initiative perception capabilities, it is difficult for design software to match with design tasks directly. A method of DR multi-granularity modeling with two-stage aggregation is proposed, by which the resource granularity is increased and dynamic design capability is formed. In the proposed DR multi-granularity model, DRs are classified into three granularities: Static Physical Resource (SPR), Dynamic Capacity Resource (DCR), and Cross-functional Design Unit (CDU). Their ontology models are set up to represent the basic function, structure and component of DRs. In the two-stage aggregation of DRs, two strategies are proposed to increase the granularity of DRs. The first is DCR aggregation strategy based on auxiliary resources actively pushing, and the second is CDU aggregation strategy based on meta task and meta capability matching. Using the operation parameters of DRs and the associated evaluation matrix, a method of DCR and CDU evaluation is proposed to optimize the searched DRs. With the help of the preceding multi-granularity DR modeling and the two-stage access strategy, DR granularity is enlarged and initiative design capability is formed, which solves the problem of DRs matching with design tasks because of small resource granularity.

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Section: B Dcr Evaluation Methods and Dcr Comprehensive Evaluation Mamentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Multi-Granularity Modeling and Aggregation of Design Resources in Cloud Manufacturing

et al. 2020

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“…There was no execution time evaluation included. Zhu et al (2020a) introduced a modified Artificial Bee Colony (ABC) algorithm with enhancements on the bee exploring mechanism called Multiple Improvement Strategies based Artificial Bee Colony algorithm (MISABC). They believed that the ABC algorithm was convenient to apply and of few parameters with acceptable results, amply utilized in Cloud manufacturing service composition.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Aghili

Valilai

Haji

et al. 2021

PeerJ Computer Science

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Recently, manufacturing firms and logistics service providers have been encouraged to deploy the most recent features of Information Technology (IT) to prevail in the competitive circumstances of manufacturing industries. Industry 4.0 and Cloud manufacturing (CMfg), accompanied by a service-oriented architecture model, have been regarded as renowned approaches to enable and facilitate the transition of conventional manufacturing business models into more efficient and productive ones. Furthermore, there is an aptness among the manufacturing and logistics businesses as service providers to synergize and cut down the investment and operational costs via sharing logistics fleet and production facilities in the form of outsourcing and consequently increase their profitability. Therefore, due to the Everything as a Service (XaaS) paradigm, efficient service composition is known to be a remarkable issue in the cloud manufacturing paradigm. This issue is challenging due to the service composition problem’s large size and complicated computational characteristics. This paper has focused on the considerable number of continually received service requests, which must be prioritized and handled in the minimum possible time while fulfilling the Quality of Service (QoS) parameters. Considering the NP-hard nature and dynamicity of the allocation problem in the Cloud composition problem, heuristic and metaheuristic solving approaches are strongly preferred to obtain optimal or nearly optimal solutions. This study has presented an innovative, time-efficient approach for mutual manufacturing and logistical service composition with the QoS considerations. The method presented in this paper is highly competent in solving large-scale service composition problems time-efficiently while satisfying the optimality gap. A sample dataset has been synthesized to evaluate the outcomes of the developed model compared to earlier research studies. The results show the proposed algorithm can be applied to fulfill the dynamic behavior of manufacturing and logistics service composition due to its efficiency in solving time. The paper has embedded the relation of task and logistic services for cloud service composition in solving algorithm and enhanced the efficiency of resulted matched services. Moreover, considering the possibility of arrival of new services and demands into cloud, the proposed algorithm adapts the service composition algorithm.

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“…Enterprise manufacturing resources in the cloud manufacturing environment are mapped into mathematical models using metadata and ontology service technology [22][23][24][25][26], as shown in Fig. 5.…”

Section: Standardized Description Model Of Enterprise Manufacturing Resourcesmentioning

confidence: 99%

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

Shi

Tang

Zou³

2021

Teh. vjesn.

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A Novel Service Composition Algorithm for Cloud-Based Manufacturing Environment

Cited by 11 publications

References 32 publications

Multi-Granularity Modeling and Aggregation of Design Resources in Cloud Manufacturing

Multi-Granularity Modeling and Aggregation of Design Resources in Cloud Manufacturing

Dynamic mutual manufacturing and transportation routing service selection for cloud manufacturing with multi-period service-demand matching

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

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