A novel method for design services matching under uncertain preferences in Cloud Manufacturing

Cloud manufacturing, as an advanced form of manufacturing, meets the requirement of highly effective production. As the platform is desirable for the matching of manufacturing tasks and manufacturing services, it is crucial to keep the stability of platform. However, compared to the manufacturing of product, the requirement of design is more uncertainly, and ordinal preference is always adopted the describe the uncertain preferences. The preferences of design is always determined by multiple decision makers, while, the multiple makers of decision is always different, the consideration of conflict decision should be considered. In our study, we take the diverse decision into account in the form of multi-choices ordinal preferences. Meanwhile, literature shows that current researches are more biased towards customer satisfaction, but the satisfaction of manufacturing task is ignored, leading to fewer customers on the platform side and affect the stability of the platform. In order to keep the stability of platform, satisfaction of two-sided customers is considered, and prospect theory is applied to measure it by perceived utility value. Two references from social and personal perspectives are adopted to calculate the perceived utility value. A model of maximizing the perceived utility value is constructed to find the matching result, and genetic algorithm is proposed to deal with the model. Finally, the model is verified by a case.