Title: Collaborative manufacturing operation mode and modelling simulation of manufacturing enterprise based on collective intelligence

Authors: Weiwei Yu; Li Zhang; Ning Ge; Hang Jia; Hui Wang

Addresses: School of Computer Science and Engineering, Beihang University, Beijing 100191, China ' School of Computer Science and Engineering, Beihang University, Beijing 100191, China ' School of Computer Science and Engineering, Beihang University, Beijing 100191, China ' School of Computer Science and Engineering, Beihang University, Beijing 100191, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

Abstract: Collaborative production among manufacturing enterprises is the focus of research in the field of collaborative manufacturing. In order to solve the problem of weak collaborative operation ability of manufacturing enterprises, based on the understanding of collective intelligence, this paper first analyses the evolution and development of collaborative manufacturing, gives the challenges and opportunities faced by collaborative manufacturing, and proposes a collaborative manufacturing operation model of manufacturing enterprise based on collective intelligence (CICoModel). Then based on CICoModel, the existing collaborative manufacturing domain knowledge is summarised, and a collaborative manufacturing process modelling language CoM-PML is proposed for collaborative manufacturing production planning. Finally, for practical application, a modelling and simulation system POMES4CM based on CoM-PML is developed, and three real collaborative manufacturing process data are selected for case verification. The validation results show that POMES4CM can obtain a guiding process model (PM) and production plan, which can improve the production efficiency of manufacturing enterprises.

Keywords: collective intelligence; collaborative manufacturing; operation mode; cross enterprise collaboration; modelling and simulation.

DOI: 10.1504/IJBIC.2023.132786

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.4, pp.218 - 229

Received: 21 Nov 2022
Accepted: 08 Mar 2023
Published online: 09 Aug 2023 *

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