Artificial Bee Colony Optimized Scheduling Framework Based on Resource Service Availability in Cloud Manufacturing

2014 ◽  
Author(s):  
Jorick Lartigau ◽  
Xiaofei Xu ◽  
Dechen Zhan
Keyword(s):  
Artificial Bee Colony ◽  
Cloud Manufacturing ◽  
Service Availability ◽  
Bee Colony ◽  
Resource Service

scholarly journals Task Decomposition Based on Cloud Manufacturing Platform

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1311
Author(s):  
Yanjuan Hu ◽  
Ziyu Zhang ◽  
Jinwu Wang ◽  
Zhanli Wang ◽  
Hongliang Liu
Keyword(s):  
Decomposition Method ◽  
Optimal Allocation ◽  
Service Providers ◽  
Search Algorithm ◽  
Cloud Manufacturing ◽  
Complex Task ◽  
Task Decomposition ◽  
Bee Colony ◽  
Modular Production ◽  
Resource Service

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.

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