A hybrid fuzzy-neural-based dynamic scheduling method for part feeding of mixed-model assembly lines. (January 2022)

• Record Type: Journal Article
• Authors: Zhou, Binghai; Zhao, Zhe
• Is Part Of: Computers & industrial engineering. Volume 163(2022)
• Publication Date: 2022-01
• Subjects: Mixed-model assembly lines -- Part feeding -- Dynamic scheduling -- Self-organizing maps -- Fuzzy c-means -- Clustering; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107794
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The dynamic part feeding scheduling problems under the Kanban-based part feeding strategy is studied relatively comprehensively. Propose a novel hybrid fuzzy-neural-based dynamic scheduling method (SOM-FCM-DS) which incorporates a hybrid algorithm (SOM-FCM) integrating the Self-organizing Maps with the Fuzzy c-means algorithm and a knowledge base. The SOM is adopted to optimize the initial clustering centers firstly, then the FCM is availed to guide the clusters, so as to improve the clustering performance of the SOM algorithm simultaneously. The proposed SOM-FCM-DS method can maintain superior scheduling performance in a dynamic scheduling environment. Abstract: Since the mixed-model assembly lines are highly adopted in the automobile industry, the part feeding process has become a critical challenge. Therefore, in this paper the dynamic part feeding scheduling problem under a Kanban system is studied, which aims to optimize a productivity-related objective (the throughput of the assembly lines) and a cost-related objective (the total delivery distance of the automatic guide vehicles (AGVs)) simultaneously. A comprehensive part feeding process considering the part feeding tasks generation, the loading, sequencing and dispatching problems is analyzed and modeled. To properly solve the problem, this study proposes a hybrid fuzzy-neural-based dynamic scheduling method, integrating the Self-organizing maps (SOM) with the Fuzzy c-means (FCM) algorithm and the knowledge base (KB). The SOM is adopted to pre-cluster the system status and optimize the initial clustering centers, then the FCM is availed to guide the clusters, so as to improve the clustering performance simultaneously. Computational experiments are conducted to evaluate its scheduling performance in a dynamic manufacturing environment and verify its superiority over the benchmark algorithms. The method allows decision makers to select more rational scheduling schemes based on their decision impacts in both productivity and part feeding costs and the real-time status of the assembly lines.