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Service load balancing, scheduling, and logistics optimization in cloud manufacturing by using genetic algorithm
Author(s) -
Jafarnejad Ghomi Einollah,
Rahmani Amir Masood,
Qader Nooruldeen Nasih
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5329
Subject(s) - cloud manufacturing , cloud computing , computer science , load balancing (electrical power) , distributed computing , scheduling (production processes) , queueing theory , distributed manufacturing , genetic algorithm , mathematical optimization , job shop scheduling , heuristic , industrial engineering , operations research , manufacturing engineering , engineering , artificial intelligence , computer network , machine learning , geometry , routing (electronic design automation) , grid , operating system , mathematics
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.