Article
Computer Science, Interdisciplinary Applications
Ahmad Al Hanbali, Haitham H. Saleh, Omar G. Alsawafy, Ahmed M. Attia, Ahmed M. Ghaithan, Awsan Mohammed
Summary: The upcoming industrial revolution 4.0, based on the internet of things and prescriptive analytics, paves the way for the spread of continuously monitored condition-based maintenance (CBM) in the industry. In CBM implementations, the impact of spare parts quality, lead-time, and inspection errors on maintenance cost and system availability must be considered. A new maintenance model is proposed to incorporate these factors along with different cost factors and optimize the degradation level at which spare parts are ordered.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Social Sciences, Interdisciplinary
Yuanchang He, Zhenhua Gao
Summary: This paper presents a maintenance management method that optimizes preventive maintenance and spare parts ordering strategies using a dynamic early warning period model based on different equipment states. The proposed method effectively reduces maintenance costs.
Article
Computer Science, Interdisciplinary Applications
Farshid Nasrfard, Mohammad Mohammadi, Mohammad Rastegar
Summary: This study proposes a probabilistic approach that considers correlations and uncertainties to find the optimal inspection rates for power systems. The results show that considering these factors can change the optimal inspection rates and reduce costs and unavailability in comparison to conventional approaches. The method is simple and accurate and can be integrated into asset management tools for maintenance decision-making.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Engineering, Industrial
Sha Zhu, Willem van Jaarsveld, Rommert Dekker
Summary: In this study, we propose a spare parts optimization model and algorithm to balance the cost of ordering spare parts and the cost of project delay. Using two-stage stochastic programming, we determine spare parts ordering policies and develop a detailed project schedule. Our sample average approximation method solves large instances quickly.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Hongyan Dui, Xingju Yang, Meng Liu
Summary: This paper analyzes the problem of spare parts storage configuration in two-echelon maintenance and supply support systems. By studying the state transition rates of different parts and establishing an optimization model, a system-level comprehensive support rate and a site importance measure are proposed to determine the quantity and location of spare parts, aiming to maximize the system support efficiency.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Chemistry, Multidisciplinary
Rene Lastra, Alejandro Pereira, Miguel Diaz-Cacho, Jorge Acevedo, Antonio Collazo
Summary: This research focuses on the applicability of additive manufacturing (AM) to spare parts in the automotive manufacturing industry, specifically in the field of preventive maintenance. Through a case study conducted at the Stellantis factory in Vigo, Spain, the technical and economic feasibility of replacing polymer spare parts with AM parts made of polyamide material (PA12) using HP Multi-Jet and Selective Laser Sintering technology (SLS) is evaluated. The study provides observations, recommendations, and conclusions on the use of AM techniques to enhance preventive maintenance.
APPLIED SCIENCES-BASEL
(2022)
Article
Operations Research & Management Science
Yi-Kuei Lin, Cheng-Fu Huang, Chin-Chia Chang
Summary: A stochastic flow network can be used to describe practical systems such as computer networks, with the quickest path problem focusing on finding the minimum transmission time path. Algorithms assuming disjoint minimal paths may lead to incorrect outcomes when paths intersect. In practice, spare routing improves system reliability, and Monte Carlo simulations are used for evaluating system reliability.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Han Mengying, Yang Jianhua, Zhao Xiao
Summary: This paper proposes a joint inspection-based maintenance and spare ordering optimization policy for a manufacturing system, considering the integrated issues of inspection, preventive maintenance, spare ordering, and quality control. The policy involves two ordering modes and introduces a threshold level to determine preference for emergency orders. By minimizing the expected cost per unit time, the optimal policy is formulated, demonstrating superior performance over existing models in terms of expected cost per unit time through numerical examples.
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
(2021)
Article
Economics
Song Ding, Jiaqi Hu, Qianqian Lin
Summary: In this study, a novel convolution-based multivariate time-delay grey model is proposed to accurately forecast CO2 emissions in China. The model shows higher applicability and flexibility compared to traditional models, and achieves lower prediction errors.
Article
Computer Science, Information Systems
Ahmet Kolus
Summary: The objective of this study is to integrate human factors that affect maintenance performance in delay-time modelling to obtain an accurate and realistic optimal inspection interval. A list of human factors is identified through a literature review and three significant factors are selected to be incorporated into the model. Fuzzy modeling is used to estimate a time allowance for human factors. Two inspection models are developed and validated against a realistic case study. The results show that failing to account for human factors increases inspection frequency and interruptions of production, resulting in decreased inspection time and operator performance. The developed models and conceptual framework can help decision makers set an accurate inspection duration and design maintenance systems with superior long-term performance.
Article
Engineering, Industrial
Bingxin Miao, Qianwang Deng, Like Zhang, Zhangwen Huo, Xiaoyan Liu
Summary: This study analyzes the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. By integrating spare parts production and worker allocation, the study aims to optimize customer satisfaction and total service cost. A modified algorithm is proposed to solve the problem, and extensive experiments demonstrate its superiority.
JOURNAL OF MANUFACTURING SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Jian Wang, Xiang Gao, Zhili Sun
Summary: The paper introduces a method called multilevel Monte Carlo (MLMC) for time-variant reliability analysis, aiming to enhance computational efficiency while maintaining accuracy and robustness. By discretizing the time interval using a geometric sequence of different timesteps and estimating the cumulative probability of failure with corrections from all levels, the method optimizes the number of random samples at each level to minimize computational complexity. Independently computed corrections at each level allow achieving accuracy at a lower cost compared to crude Monte Carlo simulation, while maintaining robustness to nonlinearity and dimensions.
Article
Construction & Building Technology
Dapeng Niu, Lei Guo, Xiaolin Bi, Di Wen
Summary: This paper presents a decision-making method for elevator parts' maintenance, which determines a reasonable maintenance period through analyzing historical fault data and establishing a mixed failure rate model. The study shows that a reasonable maintenance period can reduce the risk of equipment failure, save costs, and avoid excessive waste of resources.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Sergio Gallego-Garcia, Javier Gejo-Garcia, Manuel Garcia-Garcia
Summary: The study analyzed the impact of distribution network design and fixed or dynamic planned maintenance intervals on the overall efficiency of an aircraft fleet in the aviation industry. By developing a conceptual and simulation model, different distribution networks and maintenance strategies can be compared to aid companies and managers in making informed decisions.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Mengying Han, Zhichao Ma, Shugang Ma, Wei Li, Jianhua Yang
Summary: This paper proposes a joint strategy for condition-based maintenance and spare ordering for a multistate system subject to competing failures. The system has hidden failures that can be divided into hard failures and soft failures. The proposed strategy includes periodic inspections to identify system states and preventive or corrective replacements based on the defective or failed state. Spare ordering is done when the system operating time reaches a predetermined threshold. An optimization model is established considering different renewal events and a modified artificial bee colony algorithm and discrete simulation algorithm are used to find optimal solutions. The numerical example validates the effectiveness and applicability of the proposed strategy.
Article
Transportation
Daozheng Huang, Sean Loughney, Jin Wang
Summary: With the progress of the Belt and Road initiative, China's reliance on maritime and onshore transportation has increased, leading to the need for unimpeded transportation for trade and energy resources. This study proposes a framework for the quantitative assessment of key Strategic Transport Passages (STPs) in the context of the Belt and Road, and identifies and ranks China's STPs based on their strategic values.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Transportation
Ozkan Ugurlu, Saban Emre Kartal, Orcun Gundogan, Muhammet Aydin, Jin Wang
Summary: Mobbing is a fundamental problem that disrupts the organization's structure and negatively affects its employees' safe work environment. This study utilizes a dynamic Bayesian network model to analyze seafarers' mobbing behavior and recommends measures to be taken in the maritime industry.
MARITIME POLICY & MANAGEMENT
(2023)
Article
Engineering, Marine
Joshua Cutler, Musa Bashir, Yang Yang, Jin Wang, Sean Loughney
Summary: This paper presents the development and numerical assessment of a novel catamaran Floating Offshore Wind Turbine (FOWT) concept, showing improved dynamic characteristics and performance stability compared to traditional barge-type FOWT.
Article
Engineering, Industrial
Xuri Xin, Kezhong Liu, Sean Loughney, Jin Wang, Zaili Yang
Summary: This paper develops a new maritime traffic clustering approach for enhancing traffic pattern interpretability and discovering high-risk multi-ship encounter scenarios. The method effectively captures high-risk traffic clusters and is robust in various traffic scenarios, supporting collision risk management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Interdisciplinary Applications
Zifei Xu, Musa Bashir, Qinsong Liu, Zifan Miao, Xinyu Wang, Jin Wang, Nduka Ekere
Summary: The research aims to develop an intelligent model that can predict the remaining useful life of bearings without human intervention. The intelligent health indicator model is constructed based on an unsupervised neural network and can extract multi-scale coded features from raw vibration signals. The model is fitted with an ensemble health indicator to create a reliable indicator for RUL prediction, and three neural network-based prognostic models are developed to examine the reliability of the proposed health indicator.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Oceanography
Haijiang Li, Peng Jia, Xinjian Wang, Zaili Yang, Jin Wang, Haibo Kuang
Summary: To accurately calculate ship carbon emissions for shipping suitability, a new methodology is proposed to address the uncertainties in ship technical data and AIS breakpoints, improving the accuracy of estimation. It includes a repair model for ship technical parameters and a trajectory segmentation algorithm for AIS data. The study estimates the carbon dioxide emissions within China's domestic emission control areas and recommends a decarbonization scheme based on the results.
OCEAN & COASTAL MANAGEMENT
(2023)
Article
Engineering, Marine
Yuhao Cao, Xinjian Wang, Yihang Wang, Shiqi Fan, Huanxin Wang, Zaili Yang, Zhengjiang Liu, Jin Wang, Runjie Shi
Summary: This study utilizes a data-driven Bayesian network model to analyze the relationship between the severity of marine accidents and relevant Accident Influential Factors (AIFs). By classifying marine accident investigation reports involving 1,294 ships from 2000 to 2019, a database of factors affecting the severity of marine accidents is established. The Tree Augmented Naive Bayesian algorithm is used to establish a data-driven BN model and analyze the established database of AIFs through data training and machine learning.
Article
Engineering, Industrial
Xinjian Wang, Guoqing Xia, Jian Zhao, Jin Wang, Zaili Yang, Sean Loughney, Siming Fang, Shukai Zhang, Yongheng Xing, Zhengjiang Liu
Summary: The study aims to develop a new method to identify, quantify, and rank risks in the process of Human Evacuation from Passenger Ships (HEPS) in maritime transport. Based on literature review and accident investigation reports, the risk factors affecting ship evacuation were analyzed, and an analysis framework was proposed. A risk assessment model was proposed to quantify and rank risk factors, and a case study was conducted to verify the applicability of the model. The results indicate that evacuation decision and operation of Life-Saving Appliances (LSAs) are the main risks, while passenger behavior has relatively lower risk priority. Future research should focus on the development of a multi-attribute decision system for HEPS.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Civil
Shamsu Hassan, Christos Kontovas, Musa Bashir, Jin Wang
Summary: This paper presents a decision-support model combining Evidential Reasoning (ER) and Cost-Benefit Analysis (CBA) for assessing risk control measures in pipeline loss of containment caused by third party damage. The model identifies Risk Control Options (RCOs) as basic attributes, categorized into technical, governmental, and managerial solutions. CBA evaluates the costs associated with containment loss in terms of human safety, economy, and the environment. ER is chosen for its ability to handle uncertainties and conflicting information, as identified in this study. The study simplifies decision-making into a hierarchical output and provides guidance to infrastructure operators in selecting risk reduction attributes and understanding the budgetary expenditure required for implementing RCOs.
JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE
(2023)
Article
Oceanography
Shiqi Fan, Eduardo Blanco-Davis, Stephen Fairclough, Jinfen Zhang, Xinping Yan, Jin Wang, Zaili Yang
Summary: Psychological factors are a critical cause of human errors in various sectors, but little research has been done in the maritime industry, despite the significant contribution of human errors to shipping accidents. This study introduces a conceptual framework for assessing seafarer psychological factors using neurophysiological analysis. The framework enables quantitative assessment of psychological factors, and can be used to test, verify, and train seafarers' behaviors for ship safety.
OCEAN & COASTAL MANAGEMENT
(2023)
Article
Management
Onyeka John Chukwuka, Jun Ren, Jin Wang, Dimitrios Paraskevadakis
Summary: This study aims to identify and analyze specific risk factors that can disrupt the normal functioning of the emergency supply chain in disaster relief operations. The research uses the fuzzy-analytical hierarchy process (FAHP) to investigate the weighted priority of these risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules, the impact of cascading disasters, limited quality of relief supplies, and sanctions and constraints that hinder stakeholder collaboration.
JOURNAL OF HUMANITARIAN LOGISTICS AND SUPPLY CHAIN MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Xuri Xin, Kezhong Liu, Sean Loughney, Jin Wang, Huanhuan Li, Zaili Yang
Summary: This study proposes a new traffic partitioning methodology to achieve optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Marine
Daozheng Huang, Tiantian Liang, Shenping Hu, Sean Loughney, Jin Wang
Summary: With the continuous growth of international trade and the number of ships, the pressure on marine safety is increasing. This study investigates the association between marine traffic accidents characteristics using a Weighted Association Rule Mining (WARM) approach. The findings show that Flag of Convenience (FOC) vessels in the Mediterranean have a lower accident rate than non-FOC vessels do, and older general cargo ships with a gross tonnage between 500 and 3000 tons are more prone to accidents. This research provides insights for developing targeted measures for preventing specific types of accidents and enhancing marine safety.
Article
Engineering, Marine
Siming Fang, Zhengjiang Liu, Xihan Yang, Xinjian Wang, Jin Wang, Zaili Yang
Summary: This study proposes a specific framework based on orthogonal experiments to comprehensively investigate the impact of multiple factors on the evacuation time and efficiency. The results show that the heeling angle has a significant effect on both evacuation time and efficiency, with higher angles leading to lower efficiency. Unavailable stairs also have a significant effect on evacuation results, dependent on the number of nearby stairs. The priority of evacuees has a relatively less important impact, but prioritizing individuals with impaired mobility aids in achieving optimal evacuation results. The findings of this study can help managers develop effective evacuation strategies to improve the safe operation of passenger ships in emergencies.
Article
Management
Hani Alyami, Zaili Yang, Ramin Riahi, Stephen Bonsall, Jin Wang, Chengpeng Wan, Zhuohua Qu
Summary: This paper proposes a fuzzy decision-making approach to evaluate risk control options and aid in the selection of operational safety strategies in container terminals. A hybrid model based on fuzzy set theory is developed to optimize operational safety performance under uncertainty. The results suggest that automation and operator training are the most effective measures.
INTERNATIONAL JOURNAL OF SHIPPING AND TRANSPORT LOGISTICS
(2023)
