Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization
Availability is the key system effectiveness measure in process industries, manufacturing plants, and treatment plants like sewage, e-waste etc. The nut-bolt manufacturing industry is very prominent in manufacturing sector. The present work is proposed with a motto to develop a stochastic framework...
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| Format: | Article |
| Language: | English |
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Austrian Statistical Society
2024-12-01
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| Series: | Austrian Journal of Statistics |
| Online Access: | https://www.ajs.or.at/index.php/ajs/article/view/1922 |
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| author | Naveen Kumar Ashish Kumar Deepak Sinwar Monika Saini |
| author_facet | Naveen Kumar Ashish Kumar Deepak Sinwar Monika Saini |
| author_sort | Naveen Kumar |
| collection | DOAJ |
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Availability is the key system effectiveness measure in process industries, manufacturing plants, and treatment plants like sewage, e-waste etc. The nut-bolt manufacturing industry is very prominent in manufacturing sector. The present work is proposed with a motto to develop a stochastic framework for a nut manufacturing plant to derive steady state availability and its optimization. The Markov birth death approach is applied to develop the stochastic model as well as Chapman-Kolmogorov differential difference equations of the system. The availability function derived using Markov approach is treated as objective function of the optimization problem having decision parameters as failure and repair rates. All the decision variables are considered as exponentially distributed which are i.i.d. in nature. The objective function is optimized using particle swarm optimization to predict the optimal availability and estimated parametric values. The most sensitive component of the system is observed through making variation in failure and repair rates. It is revealed that PSO predicts the optimal availability 0.9999 at population size 50 after 50 iterations. The convergence rate of PSO is very fast in prediction of the availability of nut manufacturing plant. These findings are beneficial for system designers and maintenance engineers to propose the maintenance strategies. The proposed methodology can be utilized to predict the availability of other process industries.
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| format | Article |
| id | doaj-art-69de4790afe84ab5a54db69737fe2426 |
| institution | Kabale University |
| issn | 1026-597X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Austrian Statistical Society |
| record_format | Article |
| series | Austrian Journal of Statistics |
| spelling | doaj-art-69de4790afe84ab5a54db69737fe24262025-01-13T07:12:24ZengAustrian Statistical SocietyAustrian Journal of Statistics1026-597X2024-12-0153510.17713/ajs.v53i5.1922Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm OptimizationNaveen Kumar0Ashish Kumar1Deepak SinwarMonika SainiManipal University JaipurManipal University Jaipur Availability is the key system effectiveness measure in process industries, manufacturing plants, and treatment plants like sewage, e-waste etc. The nut-bolt manufacturing industry is very prominent in manufacturing sector. The present work is proposed with a motto to develop a stochastic framework for a nut manufacturing plant to derive steady state availability and its optimization. The Markov birth death approach is applied to develop the stochastic model as well as Chapman-Kolmogorov differential difference equations of the system. The availability function derived using Markov approach is treated as objective function of the optimization problem having decision parameters as failure and repair rates. All the decision variables are considered as exponentially distributed which are i.i.d. in nature. The objective function is optimized using particle swarm optimization to predict the optimal availability and estimated parametric values. The most sensitive component of the system is observed through making variation in failure and repair rates. It is revealed that PSO predicts the optimal availability 0.9999 at population size 50 after 50 iterations. The convergence rate of PSO is very fast in prediction of the availability of nut manufacturing plant. These findings are beneficial for system designers and maintenance engineers to propose the maintenance strategies. The proposed methodology can be utilized to predict the availability of other process industries. https://www.ajs.or.at/index.php/ajs/article/view/1922 |
| spellingShingle | Naveen Kumar Ashish Kumar Deepak Sinwar Monika Saini Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization Austrian Journal of Statistics |
| title | Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization |
| title_full | Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization |
| title_fullStr | Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization |
| title_full_unstemmed | Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization |
| title_short | Stochastic Modeling and Availability Optimization of Nuts Manufacturing Plant Using Markov Process and Particle Swarm Optimization |
| title_sort | stochastic modeling and availability optimization of nuts manufacturing plant using markov process and particle swarm optimization |
| url | https://www.ajs.or.at/index.php/ajs/article/view/1922 |
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