Open Access
ARTICLE
Bilateral Collaborative Optimization for Cloud Manufacturing Service
Bin Xu1, 2, Yong Tang1, Yi Zhu1, Wenqing Yan1, Cheng He3, Jin Qi1, *
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.
Keywords
Cite This Article
B. Xu, Y. Tang, Y. Zhu, W. Yan, C. He
et al., "Bilateral collaborative optimization for cloud manufacturing service,"
Computers, Materials & Continua, vol. 64, no.3, pp. 2031–2042, 2020.