View in EDS PDF Full Text

PERFORMABILITY ANALYSIS AND OPTIMIZATION OF A COMPLEX SYSTEM IN THE PHARMACEUTICAL INDUSTRY.

Saved in:
Bibliographic Details
Title: PERFORMABILITY ANALYSIS AND OPTIMIZATION OF A COMPLEX SYSTEM IN THE PHARMACEUTICAL INDUSTRY.
Authors: Sheikh, Mausoof1 shaikhmsf22@gmail.com, Tewari, P. C.2 pctewari1@nitkkr.ac.in
Source: Reliability: Theory & Applications. Mar2026, Vol. 21 Issue 1, p439-452. 14p.
Subjects: Systems availability, Particle swarm optimization, Statistical reliability, Markov processes, Tableting, Mathematical optimization, Reliability in engineering, Pharmaceutical industry
Abstract: This paper presents a comprehensive performance evaluation and optimization study of a tablet manufacturing unit in the pharmaceutical industry. The system consists of four key subsystems namely Tablet Compression Machine, Coating Machine, Strip Packing Machine, and Batch Packing Machine. The prime objective is to determine the optimal failure and repair rates that maximize the system's performability in terms of availability. A Markov modeling approach is employed to construct the state transition diagram, followed by the application of Chapman- Kolmogorov differential equations to derive the performability expressions. The system's performability has been analyzed under various combinations of failure and repair rates. A Maintenance Decision Priority Priorities Matrix (MDPM) is developed based on the analytical results, which identifies the Tablet Compression Machine as the most critical subsystem, contributing significantly (61.11%) to the overall system performability. Conversely, the Batch Packing Machine is found to be the least critical subsystem. To further enhance system performance, Particle Swarm Optimization (PSO) is utilized, revealing that a maximum performability of 98.267% can be achieved under optimized failure and repair rate conditions. These findings provide valuable insights for maintenance planning and reliability improvement in pharmaceutical manufacturing systems. [ABSTRACT FROM AUTHOR]
Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:This paper presents a comprehensive performance evaluation and optimization study of a tablet manufacturing unit in the pharmaceutical industry. The system consists of four key subsystems namely Tablet Compression Machine, Coating Machine, Strip Packing Machine, and Batch Packing Machine. The prime objective is to determine the optimal failure and repair rates that maximize the system's performability in terms of availability. A Markov modeling approach is employed to construct the state transition diagram, followed by the application of Chapman- Kolmogorov differential equations to derive the performability expressions. The system's performability has been analyzed under various combinations of failure and repair rates. A Maintenance Decision Priority Priorities Matrix (MDPM) is developed based on the analytical results, which identifies the Tablet Compression Machine as the most critical subsystem, contributing significantly (61.11%) to the overall system performability. Conversely, the Batch Packing Machine is found to be the least critical subsystem. To further enhance system performance, Particle Swarm Optimization (PSO) is utilized, revealing that a maximum performability of 98.267% can be achieved under optimized failure and repair rate conditions. These findings provide valuable insights for maintenance planning and reliability improvement in pharmaceutical manufacturing systems. [ABSTRACT FROM AUTHOR]
ISSN:19322321
DOI:10.24412/1932-2321-2026-190-439-452