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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Queueing Theory to Analyze the Performance Metrics of Manufacturing Systems

Kailash Chand Sharma, Dharamender Singh, Balveer Saini
4 citations
Asian Research Journal of Mathematics
Scheduling and Optimization Algorithms
1.40
article

Application of Queueing Theory to Analyze the Performance Metrics of Manufacturing Systems

Kailash Chand Sharma, Dharamender Singh, Balveer Saini
article en
4 citations

Abstract

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.

Asian Research Journal of MathematicsVol. 20(12)
Department of Higher Education (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 16%
Scheduling and Optimization Algorithms
1.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.