Diverse task scheduling for individualized requirements in cloud manufacturing

Zhou, Longfei; Zhang, Lin; Zhao, Chun; Laili, Yuanjun; Xu, Lida

doi:10.1080/17517575.2017.1364428

Cited by 95 publications

(26 citation statements)

References 22 publications

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“…eir method incorporates task workload modelling and a number of other essential service attributes such as service efficiency and service quality. Focusing on diverse manufacturing tasks and aiming to address the scheduling issue in CMfg, Zhou et al [25] build a mathematical model of task scheduling based on analysis of the scheduling process in CMfg. A scheduling method aiming for diverse tasks is also proposed to solve this scheduling problem.…”

Section: Service Matching Under Environment Of Cloudmentioning

confidence: 99%

A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

Zhang

2020

Mathematical Problems in Engineering

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A framework for the algorithm-based CL platform is established, based on which, the operational mode of it is described in detail. An integrated logistics task assignment model is built to optimally match logistics service resources and task of large scale in the algorithm-based CL. Particularly, an improved grasshopper optimization-based bitarget optimization algorithm (GROBO) is proposed to solve the biobjective programming model for service matching in CL. The case of Linyi small commodity logistics is taken as an application. Simulation results show that the proposed GROBO provides better solutions regarding to searching efficiency and stability in solving the model.

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Section: Service Matching Under Environment Of Cloudmentioning

confidence: 99%

A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

Zhang

2020

Mathematical Problems in Engineering

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“…Generally speaking, this process can be broken down into three steps as depicted in Figure 2. (1) Task decomposition: First of all, the submitted task which can be considered as a complex manufacturing project, should be decomposed into n subtasks denoted by 12 , ,..., n ST ST ST according to certain logic rules. 2 optimal selection: Finally, it becomes the time for service composition and optimal selection which can be defined as selecting one candidate service from each corresponding candidate set to compose the service while ensuring the overall QoS is optimal.…”

Section: Description Of Scos Problemmentioning

confidence: 99%

“…Through equations (12)(13)(14) below, alpha, beta, and delta estimate the position of the prey, and other wolves update their positions randomly about the prey [25].…”

Section: Huntingmentioning

confidence: 99%

“…Solving a combinatorial optimization problem is very different from solving a problem with a continuous solution space. In a continuous search space, the search agents of GWO are able to update their position vectors according to the hunting process mimicked by equations (12)(13)(14). In a discrete space, however, the position of wolves cannot be updated likewise, since the position vectors can only take on discrete values.…”

Section: Embedded Evolutionary Operatorsmentioning

confidence: 99%

“…Cloud manufacturing (CMfg) has been proposed as the panacea to implement this groundbreaking idea in recent years. Nonetheless, to fully achieve this goal, there remains significant issues with regard to enabling technologies [3][4][5][6][7], trust evaluation [8], task and resource description [9,10], scheduling [11][12][13] and service composition, among which the last one, is the key to offering composite services through the above-mentioned collaboration among partners. This has to be done by selecting the optimal resource for each sub-task out of the existing candidates at the resources pool and composing them to achieve the highest quality of service (QoS).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

Bouzary¹,

Chen²,

Shahin³

2019

Preprint

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Emerging cloud manufacturing paradigm aims towards providing highly integrated solutions via enabling cooperation between distributed manufacturing resources and capabilities. Implementation of this groundbreaking idea, however, is facing serious challenges springing from the currently centralized industrial structures. In order to help it make its way out, researchers have to address a number of pivotal issues in this regard. Service composition and optimal selection (SCOS), which tackles the problem of optimally selecting and combining available resources into a composite service is one of them. To deal with this NP-hard problem, we developed a novel hybrid algorithm based on the recentlyintroduced grey wolf optimizer (GWO) in which evolutionary operators are also embedded into the hunting mechanism of the basic algorithm.This approach not only makes it possible to adapt an algorithm with continuous structure such as GWO to solve a combinatorial problem such as SCOS, but also empowers it with providing higher exploration through crossover and mutation operators. Experiments conducted clearly proves the superior performance of the proposed algorithm over existing discrete variations of GWO and genetic algorithm, especially in large-scale SCOS problems.

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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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Diverse task scheduling for individualized requirements in cloud manufacturing

Cited by 95 publications

References 22 publications

A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

A Grasshopper Optimization-Based Approach for Task Assignment in Cloud Logistics

Optimal composition of tasks in cloud manufacturing platform: a novel hybrid GWO-GA approach

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

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