TY - JOUR
T1 - Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach
AU - Wu, Yanxia
AU - Jia, Guozhu
AU - Cheng, Yang
PY - 2020
Y1 - 2020
N2 - The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.
AB - The process of service composition and optimal selection (SCOS) is an important issue in cloud manufacturing (CMfg). However, the current studies on CMfg and SCOS have generally focused on optimising the allocation of resources against quality of service (QoS), in terms of e.g. cost, quality, and time. They have seldom taken the perspective of sustainability into discussion, although sustainability is indispensable in the CMfg environment. Addressing this gap, we aim to (1) propose a comprehensive method to assess the sustainability of cloud manufacturing (SoM) in terms of the economic, environmental, and social aspects; (2) establish a multi-objective integer bi-level multi-follower programming (MOIBMFP) model to simultaneously maximise SoM and QoS from the perspectives of both platform operator and multiple service demanders; and (3) design a hybrid particle swarm optimisation algorithm to solve the proposed MOIBMFP model. The experimental results show that the proposed algorithm is more feasible and effective than the typical multi-objective particle swarm optimisation algorithm when solving the proposed model. In other words, the proposed model and algorithm suggest better alternatives to meet the needs of the platform operator and service demanders in the CMfg environment.
KW - cloud manufacturing
KW - cloud service composition
KW - multi-objective integer bi-level multi-follower programming
KW - particle swarm optimisation
KW - quality of service
KW - sustainability of cloud manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85073811439&partnerID=8YFLogxK
U2 - 10.1080/00207543.2019.1665203
DO - 10.1080/00207543.2019.1665203
M3 - Journal article
SN - 0020-7543
VL - 58
SP - 6024
EP - 6042
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 19
ER -