Dynamic Queueing Model for Smart Manufacturing: A Priority-Based Performance Study
Keywords:
Dynamic Priority Queueing Model, Priority Function, Scheduling Algorithm, Manufacturing Process, Performance Measures, Lead Time.Abstract
To assess and enhance the performance metrics of the manufacturing system, we devised and implemented a Dynamic Priority Queuing (DPQ) model during this study. By integrating dynamic priority scheduling with queueing theory, this innovative strategy enhances the production processes of manufacturing companies. This approach represents a novel perspective. In real-world industrial applications, traditional queueing methods often ignore dynamic priority scheduling, resulting in inefficiencies, longer lead times, lower productivity, and other issues. This work addresses this practical knowledge gap. This research explores how priority scheduling affects manufacturing performance measures, including average lead time, tardiness, and work-in-progress. We gave several real-world instances to validate this model. The result of this algorithm shows that dynamic priority scheduling improves performance metrics. The simulation analyzes insights by systematically calculating priority values, lead time, tardiness, utilization, and efficiency. The proposed DPQ model provides production managers with useful information that may help them increase productivity, streamline operations, cut expenses, and better optimize production processes. The results of this study provide essential insights necessary for the efficient operation of industrial processes and make a significant addition to the existing body of knowledge regarding waiting scenarios. The findings of this study offer valuable insights for decision-makers and planners, aiding them in achieving their objectives and enhancing the efficiency of the industrial sector.
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