Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Qiu, Huaxin; Wang, Jingyi; Wang, Dujuan; Yin, Yunqiang

doi:10.1016/j.aei.2023.102090

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…Constraint (8) is imposed to ensure that for every machine requiring maintenance, there is at least one experienced operator available for the task. Further, Constraint (9) ensures that any operator assigned to a maintenance job is currently on duty. Constraint (10) mandates that the assigned operator possesses the requisite experience for the specific maintenance task.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…Researchers have employed a range of methods, from heuristic to classical techniques, for modeling workforce deployment and station selection. For instance, Qiu et al [9] proposed a hybrid heuristic model, integrating genetic algorithms and variable neighborhood search strategies, to enhance the dispatch and routing of multi-skilled technicians in equipment maintenance. Similarly, Li et al [10] developed a multi-objective optimization method that incorporates improved genetic and NSGA-II algorithms for optimizing maintenance, repair, and operations service resources for complex products.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Yang,

Teshome,

Yeh

et al. 2024

CMC

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In this study, we introduce a novel multi-objective optimization model tailored for modern manufacturing, aiming to mitigate the cost impacts of operational disruptions through optimized corrective maintenance. Central to our approach is the strategic placement of maintenance stations and the efficient allocation of personnel, addressing a crucial gap in the integration of maintenance personnel dispatching and station selection. Our model uniquely combines the spatial distribution of machinery with the expertise of operators to achieve a harmonious balance between maintenance efficiency and cost-effectiveness. The core of our methodology is the NSGA III+ Dispatch, an advanced adaptation of the Non-Dominated Sorting Genetic Algorithm III (NSGA-III), meticulously designed for the selection of maintenance stations and effective operator dispatching. This method integrates a comprehensive coding process, crossover operator, and mutation operator to efficiently manage multiple objectives. Rigorous empirical testing, including a detailed analysis from a Taiwanese electronic equipment manufacturer, validated the effectiveness of our approach across various scenarios of machine failure frequencies and operator configurations. The findings reveal that the proposed model significantly outperforms current practices by reducing response times by up to 23% in low-frequency and 28.23% in high-frequency machine failure scenarios, leading to notable improvements in efficiency and cost reduction. Additionally, it demonstrates significant improvements in operational efficiency, particularly in selective high-frequency failure contexts, while ensuring substantial manpower cost savings without compromising on operational effectiveness. This research significantly advances maintenance strategies in production environments, providing the manufacturing industry with practical, optimized solutions for diverse machine malfunction situations. Furthermore, the methodologies and principles developed in this study have potential applications in various other sectors, including healthcare, transportation, and energy, where maintenance efficiency and resource optimization are equally critical.

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Section: Mathematical Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Yang,

Teshome,

Yeh

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…Compared with deep learning, traditional machine learning has the advantages of relatively less data requirements [18][19][20][21], low computing resource consumption, and generalization capabilities [22][23][24][25]. Among them, the BP (Back Propagation) algorithm [26,27], KNN (K-nearest neighbor) algorithm [28] and XGBOOST algorithm [29][30][31] are popular machine learning algorithms used for different purposes.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Qiu,

2024

Electronics

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Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.

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A risk assessment model with dependent failure modes for the manufacturing and design processes of medical equipment

Lo,

Gul,

Yucesan

et al. 2024

Int J Interact Des Manuf

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Service-oriented multi-skilled technician routing and scheduling problem for medical equipment maintenance with sudden breakdown

Cited by 4 publications

References 39 publications

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

A Multi-Objective Optimization for Locating Maintenance Stations and Operator Dispatching of Corrective Maintenance

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

A risk assessment model with dependent failure modes for the manufacturing and design processes of medical equipment

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