Optimized Configuration of Manufacturing Resources for Middle and Lower Batch Customization Enterprises in Cloud Manufacturing Environment

Abstract

The optimal configuration of manufacturing resources in the cloud manufacturing environment has always been the focus of research on various advanced manufacturing systems. Aiming at the problem of manufacturing resources optimization configuration for middle and lower batch customization enterprises in cloud manufacturing environment, this paper gives a bi-level programming model for manufacturing resources optimization configuration in cloud manufacturing environment which fully considers customer satisfaction and enterprise customization economic benefits. The method firstly identifies the relationship between customer demands and customer satisfaction through questionnaires and quantifies the Kano model effectively. Then, it uses Quality Function Deployment (QFD) to transform customer demand characteristics into engineering characteristics and integrates the qualitative and quantitative results of the Kano model. Next, the method establishes enterprise economic benefits function according to the factors of order quantity and input cost. Furthermore, a comprehensive nonlinear bi-level programming model is established based on cost, time, and quality constraints. The model is solved by intelligent algorithm. Finally, the validity and feasibility of the model are verified by model simulation of actual orders of an enterprise. This method effectively realizes the optimal configuration of manufacturing resources in the cloud manufacturing environment, while maximizing the interests of both suppliers and demanders.

Assessment of researches and case studies on Cloud Manufacturing: a bibliometric analysis

“…The great majority of the case studies assessed by this work utilized various computational models, cyberphysical concepts, IT services, and mathematical methods to support decision making and to create collaborative networks. The most common approaches include Genetic Algorithm [34], Ant Colony [52], Bee Colony [53], Particle Swarm optimization [50], K-Nearest Neighbors (KNN) [49], Chaos Theory [54], Fuzzy logic [55], Game Theory [42], TOPSIS [35], Kano model [56], Artificial Neural Networks [57], Grey Wolf optimizer [37], AHP (Analytic hierarchy process) [52], Blockchain Ethereum [49], and multi-agent systems [58]. Besides, the cases used several simulation softwares, such as Simio [59], FlexSim [60], CloudSim [61], SDMSim [62], MathLab [63], Windows Azure [64], ZigBee [65] and other webbased applications.…”