Cloud manufacturing service composition and optimal selection with sustainability considerations: a multi-objective integer bi-level multi-follower programming approach

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 ti...

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Vydané v:International journal of production research Ročník 58; číslo 19; s. 6024 - 6042
Hlavní autori: Wu, Yanxia, Jia, Guozhu, Cheng, Yang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 01.10.2020
Taylor & Francis LLC
Predmet:
Algorithms
cloud manufacturing
cloud service composition
Composition
Design optimization
Integers
Manufacturing
multi-objective integer bi-level multi-follower programming
Multiple objective analysis
particle swarm optimisation
quality of service
Social factors
Sustainability
sustainability of cloud manufacturing
ISSN:0020-7543, 1366-588X
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2019.1665203