Cloud manufacturing (CMfg) is a new service‐oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on‐demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi‐Batch Subtasks Parallel‐Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE‐CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP‐hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.

Cloud Manufacturing Service Composition (CMSC) is the key issue and taking an important role in solving the interconnection and interoperability of resources and services for Cloud Manufacturing (CMfg). CMSC is a typical kind of NP-hard problems with the characteristics of dynamic and uncertainty. Solving large scale CMSC problem by using the traditional methods might be not efficient because of the massive complex resources and large-scale searching space. To overcome this shortcoming, a novel artificial bee colony algorithm named Multiple Improvement Strategies based Artificial Bee Colony algorithm (MISABC) is proposed. MISABC improves the performance of classical ABC algorithm through several strategies such as (a) differential evolution strategy (DES), (b) oscillation strategy with classical trigonometric factor (TFOS), (c) different dimensional variation learning strategy (DDVLS), (d) Gaussian distribution strategy (GDS). Meanwhile, to address the CMfg scenario, we also propose a manufacturing service composition scheme named as Multi-Module Subtasks Collaborative Execution for Cloud Manufacturing Service Composition (MMSCE-CMSC). Eight benchmark functions with different characteristics, a comparison study with existed improved ABC algorithms and a case study are used to validate the performance of the algorithm. The results demonstrate the effectiveness of the proposed method for addressing complex CMSC problem in CMfg.

Cloud manufacturing is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on demand. Cloud manufacturing is fully recognized as one of the most important manufacturing modes yet created and has received considerable attention in recent years. In practical scenarios, due to the diversity and uncertainty of manufacturing resource, the cloud manufacturing platform should have the ability of manufacturing risk evaluation to search a suitable alternative resource when certain original manufacturing nodes failed suddenly. Such ability may reduce the probability of the alternative resource failing to the lowest and avoid the manufacturing process/system from complete collapse. It ensures the cloud manufacturing be a reliable, robust and flexible manufacturing system with strong self-repair ability. In this article, a new risk evaluation method termed Extension Evaluation based on Reciprocal Judging Matrix with Triangular Fuzzy Numbers (EE-RJMTFN) is proposed. According to the frame of extension theory, the proposed method provides not only the risk level but also the related correlation degree that describes the deviation from the standard risk level. Moreover, the risk index weight definition based on reciprocal judging matrix with triangular fuzzy numbers can better meet user’s customized demand. The results of a case study illustrate the implementation of the proposed method.