A Decision Support Framework for Maintenance Optimization of a Steam Generation Unit in a Sugar Plant

Abstract

This paper outlines a decision support framework for optimizing maintenance of a steam generation unit in a sugar plant. The framework integrates Reliability, Availability, and Maintainability (RAM) principles, using Markov processes for probabilistic modeling of system behavior under various failure and repair scenarios. A Decision Support System (DSS) is developed with decision matrices to prioritize subsystem maintenance actions and guide optimal resource allocation. Particle Swarm Optimization (PSO) is applied to tune failure and repair parameters, identifying optimal configurations for maximum availability. The analysis provides insights into operational reliability and demonstrates how data-driven optimization supports maintenance planning and improves system availability.