Efficient stochastic framework for availability improvement of doormat manufacturing plants using grey wolf optimization algorithm

The study aims to introduce an efficient stochastic framework to optimize the availability of the doormat manufacturing plants along with the reliability, availability, maintainability, & dependability as well as steady state analysis of the performance of the plant. The doormat plant is a very complex structure configured in series structure using several subsystems. The reliability, availability, maintainability & dependability analysis methodology is employed to identify critical components that significantly impact the system's overall availability. For this purpose, a stochastic model is developed using Markov birth–death process and Chapman–Kolmogorov differential difference equations derived for steady state availability evaluation. The incorporation of exponential distribution models for failure and repair rates, coupled with the Markovian technique, yields insights into the intricate variations within the system. The proposed stochastic model is optimized using grey wolf optimization algorithm and predicted resulted compared with another algorithms namely dragonfly & Ant lion optimization. The findings showcase the efficacy of the proposed stochastic framework in achieving remarkable improvements in availability. Numerical outcomes, meticulously presented in structured tables, provide tangible evidence of the framework's success. The novelty of the study lies in the strategic combination of these methodologies to achieve enhanced insights into availability improvement. By enhancing availability, the proposed framework directly influences production efficiency and overall plant performance. The findings of present work are valuable insights for industrial practitioners seeking resilient operational strategies.