Application of Queueing Theory to Analyze the Performance Metrics of Manufacturing Systems
Manufacturing is a processing medium used to transform raw materials into consumable finished goods. Manufacturing systems entail numerous interactions between personnel, data, supplies, and equipment. There is an urgent need for a queueing theory-based solution to handle the blocking and delay issues in production systems. The theory of queuing is a useful tool for analyzing and simulating the operation of production processes because it provides a variety of performance metrics, such as throughput, waiting time, and resource utilization. By considering the arrival pace of jobs and the service rate of different components, queuing models can offer valuable insights into the system's expected performance. The present study aims to analyze manufacturing systems within various frameworks, evaluate various performance metrics using the M/M/1 queueing techniques, and identify the optimal choice using typical optimization tools. We have described various performance metrics like utilization, throughput, cycle time, and queue length. To validated, we have calculated these performance metrics in practical situations. This project includes all intermediate steps required to create and include the product mechanism. The findings of this work have the potential to inspire future research directions, encouraging interdisciplinary collaborations and the development of innovative solutions. This work serves as a vital resource for academics, practitioners, and policymakers aiming to expand and improve the knowledge in Queueing Theory.
Authors
- Kailash Chand Sharma (ORCID: https://orcid.org/0000-0002-3834-7718)
- Dharamender Singh (ORCID: https://orcid.org/0000-0001-5601-7790)
- Balveer Saini
Institutions
- Department of Higher Education (IN)
Publication Details
- Journal
- Asian Research Journal of Mathematics
- Published
- 2024-12-24
- DOI
- https://doi.org/10.9734/arjom/2024/v20i12876
- Citations
- 4
- Primary Topic
- Scheduling and Optimization Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 1.40