Open Access

ARTICLE

Bilateral Collaborative Optimization for Cloud Manufacturing Service

Bin Xu^{1, 2}, Yong Tang¹, Yi Zhu¹, Wenqing Yan¹, Cheng He³, Jin Qi^{1, *}

1 Nanjing University of Posts and Telecommunications, Nanjing, 21000, China.
2 Nanjing pharmaceutical Co., Ltd., Nanjing, 21000, China.
3 University of New South Wales, Sydney, 2000, Australia.

* Corresponding Author: Jin Qi. Email: .

Computers, Materials & Continua 2020, 64(3), 2031-2042. https://doi.org/10.32604/cmc.2020.011149

Received 22 April 2020; Accepted 14 May 2020; Issue published 30 June 2020

Abstract

Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing, which directly affect the quality of Cloud Manufacturing services. However, the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints. Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time, a Bilateral Collaborative Optimization Model of Cloud Manufacturing (BCOM-CMfg) is constructed in this paper. In BCOM-CMfg, to solve the manufacturing service scheduling problem on the supply side, a new efficient manufacturing service scheduling strategy is proposed. Then, as the input of the service composition problem on the demand side, the scheduling strategy is used to build the BCOM-CMfg. Furthermore, the Cooperation Level (CPL) between services is added as an evaluation index in BCOM-CMfg, which reveals the importance of the relationship between services. To improve the quality of manufacturing services more comprehensively. Finally, a Self-adaptive Multi-objective Pigeon-inspired Optimization algorithm (S-MOPIO) is proposed to solve the BCOM-CMfg. Simulation results show that the BCOM-CMfg model has advantages in reliability and cost and SMOPIO can solve BCOM-CMfg effectively.

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