In the cloud manufacturing environment, smart service units with adaptive learning capacity can conduct resource delivery, information sharing and interaction with other services through social relationships to complete complex manufacturing projects together. According to the new features of manufacturing task relevance and service collaboration, a two-sided matching decision method for manufacturing tasks considering learning and synergy effects is proposed on the cloud platform. Due to the dynamics and repeatability of cloud transactions, services involved in multiple tasks task can accumulate experience and knowledge to improve its service quality. A calculation method of dynamic capability is put forward based on learning effect model, and mutual satisfaction for task and service can be aggregated by applying expected utility theory. Meanwhile, a synergy network is used to describe service social relationships, and collaboration satisfaction among services can be measured through synergy effect based on social network theory. Thus, taking the tasks', services' and inter-service collaborative satisfaction maximization as objective, a one to one two-sided matching muti-objective model is constructed considering the influence of learning and synergy ability on task execution performance. Through automobile cloud manufacturing case study, the optimal matching scheme is obtained to verify the validity of our model. Comparing with other three types of matching model, the proposed model has been obviously proved to be better in line with the requirements of actual manufacturing scene.
REN Lei, REN Ming-lun
. Dynamic Two-sided Matching Method of Cloud Manufacturing Task based on Learning and Synergy Effect[J]. Chinese Journal of Management Science, 2018
, 26(7)
: 63
-70
.
DOI: 10.16381/j.cnki.issn1003-207x.2018.07.008
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