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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Main Authors: Ehsan Esmaeeli, Mohsen Varmazyar, Vahid Hekmatshoar, Parviz Boroomandfar, Mohammad Reza Feylizadeh
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
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
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issn 2169-3536
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publishDate 2025-01-01
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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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