ScholarWorks@동국대학교

초록

In modern electroplating lines, it is crucial to determine the appropriate number of hoists and their feasible movements, as it directly impacts production efficiency and mitigates environmental pollution caused by the corrosion of plated parts. Unfortunately, scheduling decisions are still carried out manually or delegated to outsourcing companies due to a lack of in-house expertise in complicated formulation and optimization of the scheduling problem, causing additional costs. Moreover, most outsourcing companies do not provide visual insights into operational outcomes, such as overall production workflows, bottlenecks, and cost structures, making it difficult for facility operators to plan investments and make informed decisions. To address these challenges, this paper proposes a digital twin-driven decision-support framework that integrates a constraint-based search method and visual analytics. Specifically, the proposed constraint-based search method determines the appropriate number of hoists and their feasible movement schedules while satisfying a given cycle time, which automates the complex computation scheduling process so that facility operators do not need to develop algorithms from scratch. The visual analytics component provides interpretable insights, including Gantt charts, bottleneck identification, and cost-profit estimation, which enables non-expert facility operators to make informed decisions and helps outsourcing providers deliver transparent services. Industrial case studies demonstrate that the proposed framework is effective in determining the appropriate number of hoists and their movements, outperforming the ground truth approach. Furthermore, the findings obtained through visual analytics suggest that the proposed approach enables practitioners to evaluate the long-term financial impact of various configurations for building a sustainable manufacturing system in electroplating facilities.

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