Abstract
With the introduction and application of new information technology in manufacturing, various advanced manufacturing models and national strategies have received more and more attention. The goal of cloud manufacturing is to closely link the resources and capabilities of manufacturers through a variety of services to create a dedicated platform for complex manufacturing process needs. How to achieve effective matching of various manufacturing resources and capabilities in the form of services will be a common problem in the future. In order to effectively improve cloud manufacturing tasks and resource matching efficiency and save resources, this study considers the common aspects of cloud manufacturing resource matching as service quality indicators, and extends the scope to the requirements of manufacturing resources, and the matching pattern of traditional service resources. There are additional restrictions on the resource service matching process. At the same time, the resource service matching is usually asymmetric. Therefore, we introduce the concept of task complexity of demand resources, and propose a combination system based on task complexity and service quality evaluation. The artificial bee colony algorithm (ABC) is used for analysis and verification. The experimental paper further validates the proposed the feasibility and efficiency of the method.
Highlights
With the development of emerging manufacturing technologies and Internet technologies, production has gradually developed into multi-role and multi-field collaborative design and production, and there are many cross-regional and even cross- domain resources and technologies combined with production
The problem of matching cloud manufacturing service resources [18] is that the users enter the platform search and compare the manufacturing resource requirement ontology with the description body of the existing manufacturing resources of the cloud platform, so as to find the manufacturing resources that meet the requirements
The matching process of tasks and resources [20,21] is the key link in cloud manufacturing, so the problem of efficiently and accurately matching manufacturing resources for manufacturing tasks is the research focus of this paper
Summary
With the development of emerging manufacturing technologies and Internet technologies, production has gradually developed into multi-role and multi-field collaborative design and production, and there are many cross-regional and even cross- domain resources and technologies combined with production. The existing manufacturing models, such as application service provider (ASP) [1] and manufacturing grid [2], can no longer meet the production requirements well In this context, the concept of “cloud manufacturing” was proposed and discussed [3,4], aimed at solving the problems of poor manufacturing innovation, backward manufacturing models, low resource utilization rate, decentralized manufacturing resources and regionalization, realizing the optimal matching of manufacturing resources, and improving the independent innovation and market competitiveness of enterprises. By establishing a car/motorcycle attachment ontology database, combining quantitative methods, matching algorithms and semantic similarity, effective matching of cloud manufacturing resources is realized. Considering the above factors, based on the research and in reference to the existing research results, this article focuses on the problems of resource matching in the cloud platform system
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