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

Summary 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 li...

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Bibliographic Details
Published in:Concurrency and computation 2019-10, Vol.31 (20), p.n/a
Main Authors: Jafarnejad Ghomi, Einollah, Rahmani, Amir Masood, Qader, Nooruldeen Nasih
Format: Article
Language:English
Subjects:
Cloud computing
cloud manufacturing
genetic algorithm
Genetic algorithms
Geographical distribution
goal programming
Heuristic methods
Integer programming
Linear programming
Load balancing
Logistics
Manufacturing
Optimization
Production scheduling
Queues
Queuing theory
Scheduling
service load balancing
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Summary:Summary 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.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5329