Performance modelling and optimisation of the rice processing industrial system using PSO

Ajay Kumar

International Journal of Industrial and Systems Engineering, 2020, vol. 34, issue 2, 224-240

Abstract: The present work includes the performance modelling and optimisation of the rice processing industry for achieving the maximum availability. The study presents a methodology for availability evaluation of the process industry. For the increased production rate and resource utilisation, the maintenance plan should be developed which can predict the effect of performance parameters with time. The performance modelling for the steady state availability (SSA) has been done using Markov method using historical failure and repair database followed by the system optimisation using particle swarm optimisation (PSO). The performance parameters are assumed to be negative exponential which are independent to each other and repaired units are considered as fine as original. The uncertainties of failure and repair rates (FRR) are removed by selecting these parameters randomly in PSO. The maximum availability of each system for all possible combinations of FRR within fixed minimum and maximum limits for each sub-system has also been computed. The rice processing plant consists of several sub-systems for which optimum availability level for rice processing plant for random combinations of FRR has been found. The computed results are economically beneficial for the plant personnel in improving the production rate and maintenance planning.

Keywords: performance modelling; rice processing plant; Markov modelling; particle swarm optimisation; PSO; selection; decision support system. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=105292 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:34:y:2020:i:2:p:224-240

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().