Contaminated consignment simulation to support risk-based inspection design

Invasive nonnative plant pests can cause extensive environmental and economic damage and are very difficult to eradicate once established. Phytosanitary inspections that aim to prevent biological invasions by limiting movement of nonnative plant pests across borders are a critical component of the biosecurity continuum. Inspections can also provide valuable information about when and where plant pests are crossing national boundaries. However, only a limited portion of the massive volume of goods imported daily can be inspected, necessitating a highly targeted, risk-based strategy. Furthermore, since inspections must prioritize detection and efficiency, their outcomes generally cannot be used to make inferences about risk for cargo pathways as a whole. Phytosanitary agencies need better tools for quantifying pests going undetected and designing risk-based inspection strategies appropriate for changing operational conditions. In this research, we present PoPS (Pest or Pathogen Spread) Border, an open-source consignment inspection simulator for measuring inspection outcomes under various cargo contamination scenarios to support recommendations for inspection protocols and estimate pest slippage rates. We used the tool to estimate contamination rates of historical interception data, quantify tradeoffs in effectiveness and workload for inspection strategies, and identify vulnerabilities in sampling protocols as changes in cargo configurations and contamination occur. These use cases demonstrate how this simulation approach permits testing inspection strategies and measuring quantities that would otherwise be impossible in a field-based setting. This work represents the first steps toward a decision support tool for creating dynamic inspection protocols that respond to changes in available resources, workload, and commerce trends.     

Cocoa Pod Borer (Conopomorpha cramerella Snellen) in Papua New Guinea: Biosecurity Models for New Ireland and the Autonomous Region of Bougainville

Cocoa Pod Borer (Conopomorpha cramerella Snellen) (CPB) is an important pest of cocoa. Following its emergence as a pest in East New Britain, Papua New Guinea, in 2006, it was considered relevant to assess its potential spread to other cocoa growing regions. Its likelihood of introduction to the islands of Bougainville and New Ireland from East New Britain Province, Papua New Guinea, was modeled using Monte Carlo simulation. This dispersal model was based around different scenarios, identifying trends rather than explicitly attempting to encapsulate true values. The model suggested that CPB is far more likely to establish on New Ireland than on Bougainville. More important, incertitude resulting from incomplete knowledge of the amount and frequency of cocoa transported between islands had a significant effect on model outputs. Quarantine and agriculture officials will be able to refine these parameter values, and then use the relevant scenarios from those presented here as a guide to develop quarantine procedures. In addition, a contingency model was employed to estimate the optimal sampling effort to use following an incursion of CPB into Bougainville or New Ireland and the seemingly successful implementation of an initial eradication program. The model suggests that at a 1% infestation level, sampling should continue for 2.5–2.7 years (90% CI) after claiming eradication, and this estimate changed little for higher infestation levels. Through modeling variations in sampling intensity, the model also suggested that determining the full spread of CPB is more important than increased sampling within one region.     

Sample Size Allocation for Food Item Radiation Monitoring and Safety Inspection

The objective of this study is to identify a procedure for determining sample size allocation for food radiation inspections of more than one food item to minimize the potential risk to consumers of internal radiation exposure. We consider a simplified case of food radiation monitoring and safety inspection in which a risk manager is required to monitor two food items, milk and spinach, in a contaminated area. Three protocols for food radiation monitoring with different sample size allocations were assessed by simulating random sampling and inspections of milk and spinach in a conceptual monitoring site. Distributions of ¹³¹I and radiocesium concentrations were determined in reference to ¹³¹I and radiocesium concentrations detected in Fukushima prefecture, Japan, for March and April 2011. The results of the simulations suggested that a protocol that allocates sample size to milk and spinach based on the estimation of ¹³¹I and radiocesium concentrations using the apparent decay rate constants sequentially calculated from past monitoring data can most effectively minimize the potential risks of internal radiation exposure.     

Statistical Evaluation of the New Zealand Food Safety Authority Sampling Protocol for Imported Food

The New Zealand Food Safety Authority sampling protocol for compliance inspection of imported food products is evaluated for its ability to provide consumer protection. The sampling protocol involves both partial testing of imported consignments and complete skipping inspection of consignments based on the quality history. The risk posed by the strategies of partial testing and skipping inspection of imports is evaluated using the average outgoing quality limit and other performance measures. The cost dimension of sampling inspection is also considered. Suggestions for improvement, which include tightening the skipping inspection parameters, are made.     

Optimal two-stage production-inventory policy with whole lot and split lots

This paper deals with the problem of determining the optimal lot sizes for a two-stage production-inventory system. The first stage has a large lot to be processed in a single batch and is called the ‘whole lot’; this is split in the second stage into smaller lots of equal size called ‘split lots’. The lot in stage 1 is inspected for its conformity to standards before it is passed on to stage 2. We use a single sampling attribute plan for inspection and examine the consequences of rejecting or accepting the lot. The rejected lots are 100 % inspected and defectives are eliminated..The accepted lots contain defectives in the uninspected part which are identified during stage 2 processing. We have combined these aspects of inspection with the usual inventory costs and developed a model to determine the optimal sizes of the whole lot and split lots     

Risk-Based Integrity and Inspection Modeling (RBIIM) of Process Components/System

Process plants deal with hazardous (highly flammable and toxic) chemicals at extreme conditions of temperature and pressure. Proper inspection and maintenance of these facilities is paramount for the maintenance of safe and continuous operation. This article proposes a risk-based methodology for integrity and inspection modeling (RBIIM) to ensure safe and fault-free operation of the facility. This methodology uses a gamma distribution to model the material degradation and a Bayesian updating method to improve the distribution based on actual inspection results. The method deals with the two cases of perfect and imperfect inspections. The measurement error resulting from imperfect inspections is modeled as a zero-mean, normally distributed random process. The risk is calculated using the probability of failure and the consequence is assessed in terms of cost as a function of time. The risk function is used to determine an optimal inspection and replacement interval. The calculated inspection and replacement interval is subsequently used in the design of an integrity inspection plan. Two case studies are presented: the maintenance of an autoclave and the maintenance of a pipeline segment. For the autoclave, the interval between two successive inspections is found to be 19 years. For the pipeline, the next inspection is due after 5 years from now. Measurements taken at inspections are used in estimating a new degradation rate that can then be used to update the failure distribution function.     

Robust Resource Allocation Decisions in Resource‐Constrained Projects*

The well‐known deterministic resource‐constrained project scheduling problem involves the determination of a predictive schedule (baseline schedule or pre‐schedule) of the project activities that satisfies the finish–start precedence relations and the renewable resource constraints under the objective of minimizing the project duration. This baseline schedule serves as a baseline for the execution of the project. During execution, however, the project can be subject to several types of disruptions that may disturb the baseline schedule. Management must then rely on a reactive scheduling procedure for revising or reoptimizing the baseline schedule. The objective of our research is to develop procedures for allocating resources to the activities of a given baseline schedule in order to maximize its stability in the presence of activity duration variability. We propose three integer programming–based heuristics and one constructive procedure for resource allocation. We derive lower bounds for schedule stability and report on computational results obtained on a set of benchmark problems.     

Optimal Food Safety Sampling Under a Budget Constraint

Much of the literature regarding food safety sampling plans implicitly assumes that all lots entering commerce are tested. In practice, however, only a fraction of lots may be tested due to a budget constraint. In such a case, there is a tradeoff between the number of lots tested and the number of samples per lot. To illustrate this tradeoff, a simple model is presented in which the optimal number of samples per lot depends on the prevalence of sample units that do not conform to microbiological specifications and the relative costs of sampling a lot and of drawing and testing a sample unit from a lot. The assumed objective is to maximize the number of nonconforming lots that are rejected subject to a food safety sampling budget constraint. If the ratio of the cost per lot to the cost per sample unit is substantial, the optimal number of samples per lot increases as prevalence decreases. However, if the ratio of the cost per lot to the cost per sample unit is sufficiently small, the optimal number of samples per lot reduces to one (i.e., simple random sampling), regardless of prevalence. In practice, the cost per sample unit may be large relative to the cost per lot due to the expense of laboratory testing and other factors. Designing effective compliance assurance measures depends on economic, legal, and other factors in addition to microbiology and statistics.     

Inspecting a Vital Component Needed upon Emergency

We consider a stochastically failing component that will be needed at a random future time when an emergency occurs. If the component is not operational at that time, the system incurs a large penalty, which we want to avoid through inspections and replacements. We propose a model and solution algorithm for finding an inspection policy that minimizes the infinite horizon discounted expected penalty, replacement, and inspection costs. We also discuss structural properties of the solution, as well as insights based on numerical results.     

When Does Poor Governance Presage Biosecurity Risk?

Border inspection, and the challenge of deciding which of the tens of millions of consignments that arrive should be inspected, is a perennial problem for regulatory authorities. The objective of these inspections is to minimize the risk of contraband entering the country. As an example, for regulatory authorities in charge of biosecurity material, consignments of goods are classified before arrival according to their economic tariff number. This classification, perhaps along with other information, is used as a screening step to determine whether further biosecurity intervention, such as inspection, is necessary. Other information associated with consignments includes details such as the country of origin, supplier, and importer, for example. The choice of which consignments to inspect has typically been informed by historical records of intercepted material. Fortunately for regulators, interception is a rare event; however, this sparsity undermines the utility of historical records for deciding which containers to inspect. In this article, we report on an analysis that uses more detailed information to inform inspection. Using quarantine biosecurity as a case study, we create statistical profiles using generalized linear mixed models and compare different model specifications with historical information alone, demonstrating the utility of a statistical modeling approach. We also demonstrate some graphical model summaries that provide managers with insight into pathway governance.     

Assessing Risk Factors in the Organic Control System: Evidence from Inspection Data in Italy

Certification is an essential feature in organic farming, and it is based on inspections to verify compliance with respect to European Council Regulation—EC Reg. No 834/2007. A risk‐based approach to noncompliance that alerts the control bodies to activate planning inspections would contribute to a more efficient and cost‐effective certification system. An analysis of factors that can affect the probability of noncompliance in organic farming has thus been developed. This article examines the application of zero‐inflated count data models to farm‐level panel data from inspection results and sanctions obtained from the Ethical and Environmental Certification Institute, one of the main control bodies in Italy. We tested many a priori hypotheses related to the risk of noncompliance. We find evidence of an important role for past noncompliant behavior in predicting future noncompliance, while farm size and the occurrence of livestock also have roles in an increased probability of noncompliance. We conclude the article proposing that an efficient risk‐based inspection system should be designed, weighting up the known probability of occurrence of a given noncompliance according to the severity of its impact.     

Bayesian Networks to Compare Pest Control Interventions on Commodities Along Agricultural Production Chains

The production of an agricultural commodity involves a sequence of processes: planting/growing, harvesting, sorting/grading, postharvest treatment, packing, and exporting. A Bayesian network has been developed to represent the level of potential infestation of an agricultural commodity by a specified pest along an agricultural production chain. It reflects the dependency of this infestation on the predicted level of pest challenge, the anticipated susceptibility of the commodity to the pest, the level of impact from pest control measures as designed, and any variation from that due to uncertainty in measure efficacy. The objective of this Bayesian network is to facilitate agreement between national governments of the exporters and importers on a set of phytosanitary measures to meet specific phytosanitary measure requirements to achieve target levels of protection against regulated pests. The model can be used to compare the performance of different combinations of measures under different scenarios of pest challenge, making use of available measure performance data. A case study is presented using a model developed for a fruit fly pest on dragon fruit in Vietnam; the model parameters and results are illustrative and do not imply a particular level of fruit fly infestation of these exports; rather, they provide the most likely, alternative, or worst‐case scenarios of the impact of measures. As a means to facilitate agreement for trade, the model provides a framework to support communication between exporters and importers about any differences in perceptions of the risk reduction achieved by pest control measures deployed during the commodity production chain.     

Shortcuts in complex engineering systems: a principal-agent approach to risk management

In this article, we examine the effects of shortcuts in the development of engineered systems through a principal-agent model. We find that occurrences of illicit shortcuts are closely related to the incentive structure and to the level of effort that the agent is willing to expend from the beginning of the project to remain on schedule. Using a probabilistic risk analysis to determine the risks of system failure from these shortcuts, we show how a principal can choose optimal settings (payments, penalties, and inspections) that can deter an agent from cutting corners and maximize the principal's value through increased agent effort. We analyze the problem for an agent with limited liability. We consider first the case where he is risk neutral; we then include the case where he is risk averse.     

具有时间转换约束的离散时间-费用权衡问题研究

离散时间-费用权衡问题(DTCTP)是项目进度中研究最多的双目标优化问题,它通常以三种形式出现:(1)P1:截止日期问题,在项目截止日期约束下使完成项目的总费用最小;(2)P2:预算问题,在费用预算约束下使项目工期最短;(3)P3:工期-费用曲线问题,找出全部有效的工期-费用模式集合。然而,考虑时间转换约束(TSC)的DTCTP却很少被关注。本文首先介绍时间转换约束的问题描述,在此基础上,建立具有活动类型时间转换约束的DTCTPTSC-P2模型;从实用角度出发,设计求解模型的遗传算法;最后,用一个真实项目实例说明模型的合理性和算法的有效性,对算例分析结果表明,该模型对承包商更准确地进行项目工期-费用权衡决策具有借鉴意义。     

Bias‐Corrected Estimation in Continuous Sampling Plans

Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia's Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias‐corrected estimators and confidence intervals, and evaluate their performance in a numerical study.     

Identifying Dangerous Trucking Firms

The paper develops a statistical procedure for predicting the safety performance of motor carriers based on characteristics of firms and results of two government safety enforcement programs. One program is an audit of management safety practices, and the other is a program to inspect drivers and vehicles at the roadside for compliance with safety regulations. The technique can be used to provide safety regulators with an empirical approach to identify the most dangerous firms and provide a priority list of firms against which educational and enforcement actions should be initiated. The government needs to use such an approach rather than directly observing accident rates because the most dangerous firms are generally small and, despite relatively high accident rates, accidents remain rare events. The technique uses negative-binomial regression procedures on a dataset of 20,000 firms. The definition of poor performance in roadside inspection is based on both the rate of inspections per fleet mile and the average number of violations found during an inspection. This choice was made because selection for inspection has both a random and nonrandom component. The results of the study suggest that both of the government's safety programs help identify the most dangerous firms. The 2.5% of firms that do poorly in both programs have an average accident rate twice that of the mean for all other firms.     

Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities from Different Countries

The present study investigates U.S. Department of Agriculture inspection records in the Agricultural Quarantine Activity System database to estimate the probability of quarantine pests on propagative plant materials imported from various countries of origin and to develop a methodology ranking the risk of country–commodity combinations based on quarantine pest interceptions. Data collected from October 2014 to January 2016 were used for developing predictive models and validation study. A generalized linear model with Bayesian inference and a generalized linear mixed effects model were used to compare the interception rates of quarantine pests on different country–commodity combinations. Prediction ability of generalized linear mixed effects models was greater than that of generalized linear models. The estimated pest interception probability and confidence interval for each country–commodity combination was categorized into one of four compliance levels: “High,” “Medium,” “Low,” and “Poor/Unacceptable,” Using K‐means clustering analysis. This study presents risk‐based categorization for each country–commodity combination based on the probability of quarantine pest interceptions and the uncertainty in that assessment.     

Risk‐Based Sampling: I Don't Want to Weight in Vain

Recently, there has been considerable interest in developing risk‐based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk‐based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean‐variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false “optimal” portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out‐of‐sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk‐based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers.     

Preventing the Importation of Illicit Nuclear Materials in Shipping Containers

We develop a mathematical model to find the optimal inspection strategy for detecting a nuclear weapon (or nuclear material to make a weapon) from being smuggled into the United States in a shipping container, subject to constraints of port congestion and an overall budget. We consider an 11-layer security system consisting of shipper certification, container seals, and a targeting software system, followed by passive (neutron and gamma), active (gamma radiography), and manual testing at overseas and domestic ports. Currently implemented policies achieve a low detection probability, and improved security requires passive and active testing of trusted containers and manually opening containers that cannot be penetrated by radiography. The annual cost of achieving a high detection probability of a plutonium weapon using existing equipment in traditional ways is roughly several billion dollars if testing is done domestically, and is approximately five times higher if testing is performed overseas. Our results suggest that employing high-energy x-ray radiography and elongating the passive neutron tests at overseas ports may provide significant cost savings, and several developing technologies, radiation sensors inside containers and tamper-resistant electronic seals, should be pursued aggressively. Further effort is critically needed to develop a practical neutron interrogation scheme that reliably detects moderately shielded, highly enriched uranium.     

Optimal Strategies for Interception,Detection, and Eradication in Plant Biosecurity

The introduction of invasive species causes damages from the economic and ecological point of view. Interception of plant pests and eradication of the established populations are two management options to prevent or limit the risk posed by an invasive species. Management options generate costs related to the interception at the point of entry, and the detection and eradication of established field populations. Risk managers have to decide how to allocate resources between interception, field detection, containment, and eradication minimizing the expected total costs. In this work is considered an optimization problem aiming at determining the optimal allocation of resources to minimize the expected total costs of the introduction of Bemisia tabaci‐transmitted viruses in Europe. The optimization problem takes into account a probabilistic model for the estimation of the percentage of viruliferous insect populations arriving through the trade of commodities, and a population dynamics model describing the process of the vector populations' establishment and spread. The time of field detection of viruliferous insect populations is considered as a random variable. The solution of the optimization problem allows to determine the optimal allocation of the search effort between interception and detection/eradication. The behavior of the search effort as a function of efficacy or search in interception and in detection is then analyzed. The importance of the vector population growth rate and the probability of virus establishment are also considered in the analysis of the optimization problem.