Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Abstract

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.

Keywords:

• Cloud Manufacturing (CMfg)
• cloud service composition
• Quality of Service (QoS)
• transportation evaluation
• ABC (Artificial Bee Colony)

Acknowledgement

This work has been partly funded by the China Ministry of Science and Technology (MOST) 863 High-Tech R&D Project, ‘The Key Technology of Cloud Manufacturing Service Platforms (Project number SQ2010AA0400507009)’, and the China Natural Science Foundation (NSFC) projects, ‘Intelligent Optimization Theory Research for Enhanced Artificial Bee Colony (Project number 61472106)’ and ‘Value Oriented Software Service Methodology – Theory, Methods and Applications (Project number 61033005)’. The authors wish to acknowledge MOST and NSFC for their support. We also wish to acknowledge our gratitude and appreciation to all the Project partners for their contribution during the development of various ideas and concepts presented in this paper. Especially TNO and Pr. Wout Hofman for their expertise and precious help.

Additional information

Funding

This work has been partly funded by the China Ministry of Science and Technology (MOST) 863 High-Tech R&D Project, ‘The Key Technology of Cloud Manufacturing Service Platforms [Project number SQ2010AA0400507009]’, and the China Natural Science Foundation (NSFC) project, ‘Intelligent Optimization Theory Research for Enhanced Artificial Bee Colony [Project number 61472106]’ and ‘Value Oriented Software Service Methodology – Theory, Methods and Applications [Project number 61033005]’.