Distributed manufacturing resource selection strategy in cloud manufacturing

Wang, Lei; Guo, Sen; Li, Xixing; Du, Baigang; Xu, Wenxiang

doi:10.1007/s00170-016-9866-8

Cited by 63 publications

(20 citation statements)

References 53 publications

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“…CDU member evaluation matrix is constructed by multiplying CDU member index matrix and member weight matrix, as shown in (39).…”

Section: Cdu Evaluation Methods and Its Comprehensive Evaluation Mamentioning

confidence: 99%

“…Multi-granularity resource virtualization strategies were proposed, in which attribute, activity and process oriented resource were mapped into virtualized resources based on interrelated resource aggregation functions. For the question of Manufacturing Resource Combinatorial Optimization (MRCO), Lei Wang et al [39] proposed a manufacturing resource selection strategy based on an improved Distributed Genetic Algorithm (DGA). All the preceding researches about resource aggregate are carried out for the purpose of resource application and there are seldom studies about initiative aggregation.…”

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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“…CDU member evaluation matrix is constructed by multiplying CDU member index matrix and member weight matrix, as shown in (39).…”

Section: Cdu Evaluation Methods and Its 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 are some similar recent works in this research area in comparison with the proposed problem. While Wang et al proposed a manufacturing resource selection strategy based on an improved distributed GA for manufacturing resource combinatorial optimization in CMfg, Zhou et al researched the diverse task scheduling problem for individualized requirements in CMfg . A task scheduling and resource allocation technique for cloud was proposed by Jiang et al that operated on disassembly.…”

Section: Literature Reviewmentioning

confidence: 99%

Service load balancing, scheduling, and logistics optimization in cloud manufacturing by using genetic algorithm

Ghomi

Rahmani

Qader

2019

Concurrency and Computation

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Recent years have seen a great deal of attention in the aspects of cloud manufacturing. Generally, in cloud manufacturing, the capabilities and manufacturing resources that distributed in different geographical places are virtualized and encapsulated into manufacturing cloud services. The literature confirms that applying queuing theory to optimize service selection and scheduling load balancing (SOSL) while taking into account logistics is still scarce and an open issue for practical implementation of cloud manufacturing. This reason motivates our attempts to present a cloud manufacturing queuing system (CMfgQS) as well as a load balancing heuristic algorithm based on task process times (LBPT), simultaneously among the first studies in this research area. Hence, a novel optimization model as mixed-integer linear programming is developed by implementing both CMfgQs and LBPT. Due to the natural complexity of the problem proposed, this study applies a genetic algorithm to solve the developed optimization model in large instances. Finally, the computational results ensure the effectiveness of the proposed model as well as the performance of the employed heuristic algorithm. KEYWORDS cloud manufacturing, genetic algorithm, goal programming, linear programming, service load balancing INTRODUCTIONNowadays, there is much attention in applying cloud manufacturing (CMfg) in the current generation of logistic networks. Accordingly, there are several scientific directions to define a CMfg system. One of them is to investigate a way to transform the production-oriented manufacturing processes into service-oriented manufacturing networks. A key tool is to develop a performance optimization model to cover the manufacturing assets as services and supplying them to the variable demands of customers. 1 From the aspects of cloud manufacturing, information and communication technology and contemporary related technologies such as service-oriented approaches, Internet of things, and semantic web have shown a great deal of changes for the nature of manufacturing industries. [2][3][4] To do this end, CMfg is a novel manufacturing paradigm to support the large-scale resource service sharing by providing manufacturing resources as consumable services. 5 Recent reports about recent trends of this research area not only ensure that CMfg platforms are currently under the consideration of development but also they have not grown enough due to real challenges of todays' world regarding the manufacturing systems. [6][7][8][9][10][11][12] Such findings confirm that the CMfg can be one of great exceptional opportunities for both small companies and customers who have new ideas for developments of new products. In regards to this challengeable trend, there are a few studies in manufacturing resources and capabilities. 13,14 As an opportunity, the effect of CMfg systems is very wide, including all industries, smart manufacturing, and globalization of markets. 15 These properties keep this research area active and motivate our attempts to have a c...

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“…To address the above concerns, a response method of logistics tasks with the mode of "request-schedulingresponse" was developed in this paper. In the model, the logistics scheduling problem is manufacturing resource combinatorial optimization (MRCO) problem which is often solved by evolutionary algorithms (EAs) [42]. A mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO).…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

Guo

et al. 2019

Mathematical Problems in Engineering

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Aiming at the logistics dynamic scheduling problem in an intelligent manufacturing workshop (IMW), an intelligent logistics scheduling model and response method with Automated Guided Vehicles (AGVs) based on the mode of “request-scheduling-response” were proposed, and they were integrated with Internet of Things (IoT) to meet the demands of dynamic and real time. Correspondingly, a mathematical model was developed and integrated with a double-level hybrid genetic algorithm and ant colony optimization (DLH-GA-ACO) to minimize the finish time with the minimum AGVs and limited time. The mathematical model optimized the logistics scheduling process on two dimensions which include the sequence of tasks assigned to an AGV and the matching relation between transfer tasks and AGVs (AGV-task). The effectiveness of the model was verified by a set of experiments, and comparison among DLH-GA-ACO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO), and tabu search algorithm (TSA) was performed. In the experiments, the DLH-GA-ACO ran in a distributed environment for a faster computing speed. According to the comparisons, the superiority and effectiveness of DLH-GA-ACO on dynamic simultaneous scheduling problem were proved and the intelligent logistics scheduling model was also proved to be an effective model.

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Distributed manufacturing resource selection strategy in cloud manufacturing

Cited by 63 publications

References 53 publications

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

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

Service load balancing, scheduling, and logistics optimization in cloud manufacturing by using genetic algorithm

A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop

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