Redundancy Allocation and Condition-Based Maintenance Optimization in Heterogeneous Multi-State Systems under Imperfect Monitoring and Maintenance

Abstract

Existing redundancy allocation and maintenance optimization models commonly neglect imperfect monitoring and maintenance conditions and often rely on sequential decision approaches. This study proposes a joint optimization framework for redundancy allocation and CBM in heterogeneous repairable multi-state weighted k-out-of-n systems. The objective is to minimize life-cycle cost while satisfying availability requirements under imperfect monitoring and maintenance conditions. Semi-Markov processes and universal generating functions are jointly used to model degradation, false alarms, missed detections, maintenance delays, and imperfect preventive actions. An adapted GA identifies high-quality feasible solutions under strict availability requirements. Results show that explicitly modeling imperfections reduces life-cycle cost by about 13%, while a sequential scheme increases cost by about 7% compared with the joint formulation. Sensitivity analyses reveal that demand and repair duration strongly affect performance and require joint adaptation of thresholds and redundancy.