Limited Inspection Resources Research Articles

Adaptive sampling method to monitor low-risk pathways with limited surveillance resources.

The rise of globalization has led to a sharp increase in international trade with high volumes of containers, goods, and items moving across the world. Unfortunately, these trade pathways also facilitate the movement of unwanted pests, weeds, diseases, and pathogens. Each item could contain biosecurity risk material, but it is impractical to inspect every item. Instead, inspection efforts typically focus on high-risk items. However, low risk does not imply no risk. It is crucial to monitor the low-risk pathways to ensure that they are and remain low risk. To do so, many approaches would seek to estimate the risk to some precision, but increasingly lower risks require more samples. On a low-risk pathway that can be afforded only limited inspection resources, it makes more sense to assign fewer samples to the lower risk activities. We approach the problem by introducing two thresholds. Our method focuses on letting us know whether the risk is below certain thresholds, rather than estimating the risk precisely. This method also allows us to detect a significant change in risk. Our approach typically requires less sampling than previous methods, while still providing evidence to regulators to help them efficiently and effectively allocate inspectioneffort.

Data-Driven Structural Fire Risk Prediction for City Properties

Fire Departments conduct inspections to prevent fires but it is unclear how to best allocate their limited inspection resources across the properties in a city. Currently, they use their intuition and experience to decide on which properties to inspect and lack a data-driven approach that could lead to a more principled use of inspection resources. The main contribution of this paper is to investigate such an approach, based on machine learning for predicting a fire risk score for properties in a city based on historical fire-incident data. These scores can then be used to help prioritize inspection resources toward higher-risk properties. We present a case study using data from a South Dakota fire department which contains information about properties in a city along with records of fire in- incidents. We use this data consisting of more than 72,000 properties to train a machine learning model to predict fire risk and evaluate its ability to rank the fire risk of properties in the city. We conduct and analyze experiments with variations of XG-Boost, which is an algorithm well-suited to the challenges in application, including missing data and a highly-skewed class distribution. Our evaluation of the model-generated rankings, based on ranking metrics, shows that the model significantly outperforms random rankings and other natural baselines. We also analyze the feature importance computed for the models, which provides further insight into the model behavior. This model has been integrated into an interface for displaying the rankings across a city and is ready for beta testing.

Enhancing detection of labor violations in the agricultural sector: A multilevel generalized linear regression model of H-2A violation counts.

Agricultural workers are essential to the supply chain for our daily food, and yet, many face harmful work conditions, including garnished wages, and other labor violations. Workers on H-2A visas are particularly vulnerable due to the precarity of their immigration status being tied to their employer. Although worksite inspections are one mechanism to detect such violations, many labor violations affecting agricultural workers go undetected due to limited inspection resources. In this study, we identify multiple state and industry level factors that correlate with H-2A violations identified by the U.S. Department of Labor's Wage and Hour Division using a multilevel zero-inflated negative binomial model. We find that three state-level factors (average farm acreage size, the number of agricultural establishments with less than 20 employees, and higher poverty rates) are correlated with H-2A violations. These findings offer valuable insights into where H-2A violations are being detected at the state and industry levels.

Machine learning for data verification in emissions trading system

Data quality is the cornerstone of any emissions trading system (ETS), although developing an effective assurance mechanism is a considerable challenge. To evaluate potential data quality issues of regulated firms and develop a cost-efficient data verification scheme for the authorities, this study uses domain knowledge and data-driven approaches to identify firms with high data quality risks. Using a unique dataset from China's national ETS, each sample obtains an ensemble outlier score generated by several supervised and unsupervised machine learning techniques, and limited inspection resources are allocated to the facilities with higher scores. Our results show that the models make good predictions where potential misreports are found among the predicted high-risk samples, and 70 % of tampered datapoints are detected in the robust test. The method presented here helps in efficiently verifying firms’ self-report emissions and proposes a feasible solution for intelligent data quality management under ETS context.

Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation

<abstract><p>Maritime transportation plays a significant role in international trade and global supply chains. Ship navigation safety is the foundation of operating maritime business smoothly. Recently, more and more attention has been paid to marine environmental protection. To enhance maritime safety and reduce pollution in the marine environment, various regulations and conventions are proposed by international organizations and local governments. One of the most efficient ways of ensuring that the related requirements are complied with by ships is ship inspection by port state control (PSC). In the procedure of ship inspection, a critical issue for the port state is how to select ships of higher risk for inspection and how to optimally allocate the limited inspection resources to these ships. In this study, we adopt prediction and optimization approaches to address the above issues. We first predict the number of ship deficiencies based on a k nearest neighbor (kNN) model. Then, we propose three optimization models which aim for a trade-off between the reward for detected deficiencies and the human resource cost of ship inspection. Specifically, we first follow the predict-then-optimize framework and develop a deterministic optimization model. We also establish two stochastic optimization models where the distribution of ship deficiency number is estimated by the predictive prescription method and the global prescriptive analysis method, respectively. Furthermore, we conduct a case study using inspection data at the Hong Kong port to compare the performances of the three optimization models, from which we conclude that the predictive prescription model is more efficient and effective for this problem.</p></abstract>

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

PurposeThe massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection.Design/methodology/approachA critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations.FindingsThe utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results.Practical implicationsThe availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources.Originality/valueA critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

Development of Two Highly-Efficient and Innovative Inspection Schemes for PSC Inspection

Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed for the inspectors’ reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially with inspecting ships with no less than five deficiency items and limited inspection resources.

Shipping Domain Knowledge Informed Prediction and Optimization in Port State Control

Maritime transportation is the backbone of global supply chain. To improve maritime safety, protect the marine environment, and set out seafarers’ rights, port state control (PSC) empowers ports to inspect foreign visiting ships to verify them comply with various international conventions. One critical issue faced by the port states is how to optimally allocate the limited inspection resources for inspecting the visiting ships. To address this issue, this study first develops a state-of-the-art XGBoost model to accurately predict ship deficiency number considering ship generic factors, dynamic factors, and inspection historical factors. Particularly, the XGBoost model takes shipping domain knowledge regarding ship flag, recognized organization, and company performance into account to improve model performance and prediction fairness (e.g., for two ships that are different only in their flag performances, the one with a better flag performance should be predicted to have a better condition than the other). Based on the predictions, a PSC officer (PSCO) scheduling model is proposed to help the maritime authorities optimally allocate inspection resources. Considering that a PSCO can inspect at most four ships in a day, we further propose and incorporate the concepts of inspection template and un-dominated inspection template in the optimization models to reduce problem size as well as improve computation efficiency and model flexibility. Numerical experiments show that the proposed PSCO scheduling model with the predictions of XGBoost as the input is more than 20% better than the current inspection scheme at ports regarding the number of deficiencies detected. In addition, the gap between the proposed model and the model under perfect-forecast policy is only about 8% regarding the number of deficiencies detected. Extensive sensitivity experiments show that the proposed PSCO scheduling model has stable performance and is always better than the current model adopted at ports.

Ship selection in port state control: status and perspectives

ABSTRACT Port state control (PSC) inspection acts as a safeguard against maritime accidents and marine environment pollution. Due to limited inspection resources and high inspection costs, port states can only select substandard ships with high risk for inspection. Therefore, efficient and accurate identification of substandard ships is important. This study reviews the current ship selection methods used in different ports and proposed in the existing literature, then discusses their advantages and disadvantages. Based on this review, a combined model for ship risk prediction considering ship deficiencies and detention is developed and validated in this study. Reasonable and comprehensive comparisons of the proposed combined model and the current ship selection method at the Port of Hong Kong are conducted. The comparison results provide managerial insights and suggestions for Memorandum of Understandings (MoUs). This study is the first to review the ship selection methods implemented in port states and proposed in the PSC inspection literature. It is also the first study to combine the number of ship deficiencies and the probability of detention in a unified model to calculate ship risk. This study is valuable for improving the efficiency of ship selection in MoUs and thus protecting maritime transport.

A semi-“smart predict then optimize” (semi-SPO) method for efficient ship inspection

Efficient inspection of ships at ports to ensure their compliance with safety and environmental regulations is of vital significance to maritime transportation. Given that maritime authorities often have limited inspection resources, we proposed three two-step approaches that match the inspection resources with the ships, aimed at identifying the most deficiencies (non-compliances with regulations) of the ships. The first approach predicts the number of deficiencies in each deficiency category for each ship and then develops an integer optimization model that assigns the inspectors to the ships to be inspected. The second approach predicts the number of deficiencies each inspector can identify for each ship and then applies an integer optimization model to assign the inspectors to the ships to be inspected. The third approach is a semi-“smart predict then optimize” (semi-SPO) method. It also predicts the number of deficiencies each inspector can identify for each ship and uses the same integer optimization model as the second approach, however, instead of minimizing the mean squared error as in the second approach, it adopts a loss function motivated by the structure of the optimization problem in the second approach. Numerical experiments show that the proposed approaches improve the current inspection efficiency by over 4% regarding the total number of detected deficiencies. Through comprehensive sensitivity analysis, several managerial insights are generated and the robustness of the proposed approaches is validated.

Optimization of Multilevel Inspection Strategy for Nonrepairable Multistate Systems

The reliability function of a specific individual system can be dynamically updated by utilizing inspection data collected over time. However, due to limited inspection resources, such as time, budget, and manpower, it is oftentimes impossible to collect all the inspection data for all the components, subsystems, and the entire system simultaneously. There is an urgent need to optimally allot the limited inspection resources across multiple physical levels of a system, so as to identify the health status of a system and/or its subset of components of interest as accurately as possible to facilitate the ensuing system health management. To address the above research question, a metric is put forth in this paper to quantify the effectiveness of a particular multilevel inspection strategy for multistate systems (MSSs). Based on the proposed metric, an optimization problem is formulated to seek the optimal multilevel inspection strategy which possesses the maximum effectiveness of revealing the true state of a system and/or its subset of components of interest under limited inspection resources. The resulting optimization problem is resolved by a tailored ant colony optimization algorithm. The findings from our illustrative examples are 1) the proposed metric is capable of quantifying the effectiveness of various multilevel inspection strategies; 2) the analytical solution of the proposed metric is exactly the same with the simulation results, but much more computationally efficient; and 3) the optimal inspection strategy varies with respect to the operation time of a specific individual system.

Risk-Based Protocol for Inspection of Transportation Construction Projects Undertaken by State Departments of Transportation

In the last decade, the state departments of transportation in the US have experienced an increase in their construction projects, while the level of their in-house inspection staff and resources has either remained the same or declined. Previous studies have found that one strategy that may reduce the inspection workload is prioritizing construction activities for inspection. However, reducing the number of inspections also has its risks, such as functional failures and reduced design life. Thus, available inspection resources should be allocated to the activities with significant risk consequences if inspection is reduced. The objective of this paper is to develop a risk-based inspection protocol to facilitate efficient allocation of available inspection resources to minimize the risks associated with reduced inspection. First, the risk consequences associated with reduced inspection are identified for various construction activities linked to transportation projects. Based on data collected from 23 state departments of transportation, 58 engineers and inspectors from the Indiana Department of Transportation and 20 inspection consultants in the Midwest, the subjective perceived probabilities associated with the occurrence of each risk consequence are encoded by using fuzzy analysis, from which the risk impacts due to reduced inspection are obtained. The construction activities are prioritized based on the risk impacts associated with reduced inspection into five priority levels. The greater the risk impacts are as a result of reduced inspection, the higher the priority would be for inspection of that activity. Thus, the proposed protocol can be used for resource allocation based on risk impacts. The proposed list of prioritized construction activities can assist project and program managers in state departments of transportation in better allocating their limited inspection resources while reducing the risks due to reduced inspection.

US Food and Drug Administration and European Medicines Agency Launch Good Clinical Practices Initiative

US Food and Drug Administration and European Medicines Agency Launch Good Clinical Practices Initiative

Risk Occurrence Assessment of the Background Conformity Conditions of Inspection and Quarantine Based on FMEA

In order to promote the scientific and normalized work of the inspection and quarantine, limited inspection and quarantine resources have to be used for efficient management of limitless inspection and quarantine objects, which requires the screening of the background conformity conditions of inspection and quarantine. Failure Modes and Effects Analysis (FMEA) is a methodology for potential reliability analysis and safety assessment, and has been widely applied to the protection system of accident prevention. It is of great significance to use FMEA to assess risk in the field of inspection and quarantine. This paper provides an approach for risk occurrence assessment according to actual needs of inspection and quarantine work, based on risk elements analysis in inspection and quarantine work.

Quick decision-making support for inspection allocation planning with rapidly changing customer requirements

Manufacturing products with multiple quality characteristics is always one of the main concerns for an advanced manufacturing system. To assure product quality, finite automatic inspection systems should be available and employed. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources.

Inspection resource assignment in a multistage manufacturing system with an inspection error model

Producing high-quality products at low cost is always one concern for a multi-stage manufacturing system. That is, production costs and inspection efficiency should receive equal importance. Inspection planning to allocate inspection stations should then be performed to manage limited inspection resources during process planning. Product quality and the possible costs can then be concurrently considered when evaluating a manufacturing plan. Except for finite inspection station classes, the limited number of inspection stations of each inspection station class is considered to solve the inspection allocation problem in this research. Rather than utilizing a constant inspection error or a specified inspection error probability distribution determined by previous observations, the inspection allocation problem is solved using relative cost models in which the inspection error model is embedded. The inspection allocation problem can then be solved by practically reflecting the inspection error when tolerances are rapidly changed to satisfy customer requirements. Since determining the optimal inspection allocation plan seems impractical as the problem size becomes quite large, two heuristic methods have been developed by considering the defective rate, manufacturing cost and earliest stage priority in this research. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. A feasible manufacturing plan can then be determined and confirmed during process planning by concurrently solving the inspection allocation problem.