Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing

Yuan, Minghai; Deng, Kun; Chaovalitwongse, Wanpracha Art; Cheng, Shuo

doi:10.1080/10556788.2016.1230210

Cited by 36 publications

(10 citation statements)

References 17 publications

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“…To achieve real-time, data-driven optimised decision-making, proposed a dynamic optimisation model for flexible job shop scheduling based on game theory, in which each machine is an active entity that can request task processing and tasks were assigned to optimal machines based on their real-time status. Yuan et al (2017) considered the problem of multi-objective optimisation scheduling of a reconfigurable assembly line with the aim to minimise the cost of assembly line reconstruction, achieve the production load equalisation and minimise the delayed workload. considered two uniform parallel machine scheduling problems with fixed machine cost under the background of cloud manufacturing.…”

Section: Service and Task Schedulingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

211

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For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

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Section: Service and Task Schedulingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

211

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“…A task scheduling and resource allocation technique for cloud was proposed by Jiang et al that operated on disassembly. Yuan et al focused on the problem of multiobjective optimization scheduling of a reconfigurable assembly line with the aim of minimizing the cost of assembly line reconstruction. Li et al presented an architecture of scientific workflow management system based on a CMfg service platform and proposed a novel workflow scheduling algorithm called max percentages.…”

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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“…However, robustness for the heuristic algorithms is poor that the algorithm rule often needs to be revised with a slight change in problem statement. Meta‐heuristic is another kind of popular algorithms, such as bat algorithm, cuckoo search algorithm, and genetic algorithm. The high computational cost is the main disadvantage using meta‐heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Task scheduling based on deep reinforcement learning in a cloud manufacturing environment

Dong

Xue

Xiao

et al. 2020

Concurrency and Computation

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Cloud manufacturing promotes the transformation of intelligence for the traditional manufacturing mode. In a cloud manufacturing environment, the task scheduling plays an important role.However, as the number of problem instances increases, the solution quality and computation time always go against. Existing task scheduling algorithms can get local optimal solutions with the high computational cost, especially for large problem instances. To tackle this problem, a task scheduling algorithm based on a deep reinforcement learning architecture (RLTS) is proposed to dynamically schedule tasks with precedence relationship to cloud servers to minimize the task execution time. Meanwhile, the Deep-Q-Network, as a kind of deep reinforcement learning algorithms, is employed to consider the problem of complexity and high dimension.In the simulation, the performance of the proposed algorithm is compared with other four heuristic algorithms. The experimental results show that RLTS can be effective to solve the task scheduling in a cloud manufacturing environment. KEYWORDS cloud manufacturing, Deep-Q-Network, deep reinforcement learning, task scheduling INTRODUCTIONThe traditional manufacturing is transforming into the intelligent manufacturing due to the development of information and technology.Cloud manufacturing, 1 as an advanced manufacturing paradigm, is a manifestation of the concept of ''Manufacturing as a Service''. The cloud manufacturing platform aggregates a collection of distributed servers to execute the demand of users. However, it still faces many challenges in some aspects, such as security, sustainability, reliability, and optimized management. Task scheduling optimization between the server resources and the user's demand tasks is the critical factors restricting the development of cloud manufacturing. [2][3][4] In a large-scale distributed cloud manufacturing environment, unreasonable task scheduling will cause the problems of reducing cloud server resource utilization, degrading system performance, and increasing operating costs. Therefore, how to schedule tasks reasonably and effectively has always been the focus in both industrial and academic communities.The task scheduling in this work is simply described. The user submits an application consisting of tasks with the precedence constraint to the cloud platform. When it receives the application, servers in the platform are allocated according to the demand of each task to finish the user's request. It is critical for the platform to efficiently leverage its servers to finish the timely and cost effective delivery to users. SchedulingConcurrency Computat Pract Exper. 2020;32:e5654. wileyonlinelibrary.com/journal/cpe

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Multi-objective optimal scheduling of reconfigurable assembly line for cloud manufacturing

Cited by 36 publications

References 17 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

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

Task scheduling based on deep reinforcement learning in a cloud manufacturing environment

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