Efficient computational stochastic framework for performance optimization of E-waste management plant

Abstract

Purpose: Reliability and maintainability are the key system effectiveness measures in process and man-ufacturing industries, and treatment plants, especially in E-waste management plants. The present work is proposed with a motto to develop a stochastic framework for the e-waste management plant to opti-mize its availability integrated with reliability, availability, maintainability, and dependability (RAMD) measures and Markovian analysis to estimate the steady-state availability of the E-waste management plant. In the analysis an effort is also made to identify the best performing algorithm for availability opti-mization of the e-waste plant.Methodology: A stochastic model for a particular plant is developed and its availability is optimized using various metaheuristic approaches like a genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutions (DE). The most sensitive component is identified using RAMD methodology while the effect of deviation in various failure and repair rates are observed by the proposed model. The failure and repair rates follow an exponential distribution. All time-dependent random variables are statistically independent.Originality/Novelties: A novel stochastic model is presented for an e-waste management plant and opti-mum availability is obtained using metaheuristic approaches. The proposed methodology is not so far discussed in the reliability analysis of process industries. Findings: The numerical results of the proposed model compared to identify the most efficient algorithm. It is observed that genetic algorithm provides the maximum value (0.92330969) of availability at a pop-ulation size 2500 after 500 iterations. PSO algorithm attained the maximum value (0.99996744) of avail-ability just after 50 iterations and 100 population size. So, its rate of convergence is faster than GA. The optimum value of availability is 0.99997 using differential evolution after 500 iterations and population size of more than 1000. These findings are very beneficial for system designers. Practical Implications: The proposed methodology can be utilized to find the reliability measures of other process industries.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).