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 emphas...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11008647/ |
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| author | Ehsan Esmaeeli Mohsen Varmazyar Vahid Hekmatshoar Parviz Boroomandfar Mohammad Reza Feylizadeh |
| author_facet | Ehsan Esmaeeli Mohsen Varmazyar Vahid Hekmatshoar Parviz Boroomandfar Mohammad Reza Feylizadeh |
| author_sort | Ehsan Esmaeeli |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-14a7f850ff594380af52005cb5afa968 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-14a7f850ff594380af52005cb5afa9682025-08-20T03:06:04ZengIEEEIEEE Access2169-35362025-01-0113933469335810.1109/ACCESS.2025.357241111008647Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization ApproachEhsan Esmaeeli0https://orcid.org/0009-0001-8705-6300Mohsen Varmazyar1Vahid Hekmatshoar2Parviz Boroomandfar3Mohammad Reza Feylizadeh4https://orcid.org/0000-0002-1382-7328Department of Industrial Engineering, Sharif University of Technology, Tehran, IranDepartment of Industrial Engineering, Sharif University of Technology, Tehran, IranDepartment of Industrial Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Computer Engineering, Sharif University of Technology, Tehran, IranDepartment of Industrial Engineering, Islamic Azad University, Shiraz Branch, Shiraz, IranMaintenance 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.https://ieeexplore.ieee.org/document/11008647/Maintenance backlogmulti-criteria decision makingknapsack problemmaintenance scheduling |
| spellingShingle | Ehsan Esmaeeli Mohsen Varmazyar Vahid Hekmatshoar Parviz Boroomandfar Mohammad Reza Feylizadeh Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach IEEE Access Maintenance backlog multi-criteria decision making knapsack problem maintenance scheduling |
| title | Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach |
| title_full | Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach |
| title_fullStr | Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach |
| title_full_unstemmed | Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach |
| title_short | Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach |
| title_sort | managing maintenance backlogs an integrated multi criteria decision making and optimization approach |
| topic | Maintenance backlog multi-criteria decision making knapsack problem maintenance scheduling |
| url | https://ieeexplore.ieee.org/document/11008647/ |
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