Collaborative Production Task Decomposition and Allocation among Multiple Manufacturing Enterprises in a Big Data Environment

To realize the efficient decomposition and allocation of collaborative production tasks and resources among multiple enterprises, a task decomposition and allocation model for collaborative production among multiple manufacturing enterprises is proposed in a big data environment. The model is designed for the efficient and fast processing of production information using big data technology. This study innovatively applies the 5S management method to conduct data preprocessing for a manufacturing service provider and design the operation process of data cleaning and conversion to improve the efficiency of data processing. A collaborative optimization model, based on a hierarchical model with seven levels and considering time, costs, and services, is established for the task of production to achieve a reasonable match between supply and demand. Finally, the correlation coefficients of manufacturing service providers are configured according to weight order, so that the weight order is symmetrical with that of the manufacturer. The model also engages all manufacturing service providers with different production capabilities in collaborative production. The model is proved to be scientific and effective by using a specific example. In cooperative production activities, the production tasks of small and medium-sized enterprises can be effectively allocated. It can also realize efficient cooperative production among multiple manufacturing enterprises.

Task Decomposition Based on Cloud Manufacturing Platform

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.