Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach

Maintenance backlogs are the accumulation of uncompleted tasks or work orders that may cause significant challenges across capital-intensive industries such as manufacturing, infrastructure, and healthcare. These backlogs can compromise operational efficiency, safety, and service delivery. It emphasizes the need for structured prioritization and scheduling strategies. The current study presents a comprehensive framework that integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Bayesian Best-Worst Method (BWM), and a multi-dimensional knapsack optimization model to address maintenance backlog management challenges. DEMATEL identifies causal relationships among criteria, and BWM prioritizes criteria based on experts’ opinions. The knapsack model optimizes resource allocation under capacity constraints, ensuring the efficient scheduling of high-value tasks. The proposed framework transforms backlog management from a reactive to a proactive approach, improving operational reliability, resource utilization, and long-term sustainability. Results from a practical example demonstrate the model’s ability to maximize maintenance task value and optimize weekly scheduling, highlighting its scalability and applicability across various industrial contexts.