Optimal Prioritization Research Articles

Determining optimal prioritization system for food oral immunotherapy in a tertiary care setting

Determining optimal prioritization system for food oral immunotherapy in a tertiary care setting

The challenge of characterising food waste at a national level—An Australian example

This study critiques available methods for the national-level, sector specific, characterisation of food waste. Such estimates are required to account for the environmental and socio-economic implications of food waste, and to identify the highest impact and most cost-effective solutions to reduce those negative outcomes associated with wasted food. Australian results are compared using three fundamentally different approaches taken from the literature, along with two variants implemented for this study. The results are extremely inconsistent, suggesting that our current quantitative knowledge on Australian food waste may not be sufficient for optimal prioritisation of mitigation options. While these ‘conventional’ methodologies may not be sufficient in isolation, their strengths are complementary and would ideally be integrated into a single analytical framework that incorporates the best available top-down and bottom-up datasets.

Enhancing Efficiency of the Test Case Prioritization Technique by Improving the Rate of Fault Detection

Test case prioritization techniques organize test cases for implementation in a manner that enhance their efficacy in accordance with some performance goal. The main aim of regression testing is to test the amended software to assure that the amendments performed in software are correct. It is not always feasible to retest entire test cases in a test suite due to limited resources. Therefore, it is necessary to develop some effective techniques that can enhance the regression testing effectiveness by organizing the test cases in an order following some testing criterion. One possible criterion of such prioritization is to enhance a test suite’s fault detection rate. It aspires to arrange test cases in an order that higher priority test cases run earlier than lower ones. This paper proposed a methodology for prioritizing regression test cases based on four factors namely the rate of fault detection, the number of faults detected, the test case ability of risk detection and the test case effectiveness. The proposed approach is implemented on two projects. The resultant test case order is analyzed with other prioritization techniques such as no prioritization, random prioritization, reverse prioritization, optimal prioritization and along with previous works for project 1. We have applied our proposed approach for prioritizing test cases in an order that maximize fault coverage with least test suite execution and compared its effectiveness with other orderings. The result of proposed approach shows higher average percentage of fault detected value and outperforms all other approaches.

Assessing climate adaptation options and uncertainties for cereal systems in West Africa

In the coming decades, the already fragile agricultural system in West Africa will face further challenges in meeting food security, both from increasing population and from the impacts of climate change. Optimal prioritization of adaptation investments requires the assessment of various possible adaptation options and their uncertainties; successful adaptations of agriculture to climate change should not only help farmers deal with current climate risks, but also reduce negative (or enhance positive) impacts associated with climate change using robust climate projections. Here, we use two well-validated crop models (APSIM v7.5 and SARRA-H v3.2) and an ensemble of downscaled climate forcing from the CMIP5 models to assess five possible and realistic adaptation options for the production of the staple crop sorghum (Sorghum bicolor Moench.): (i) late sowing, (ii) intensification of seeding density and fertilizer use, (iii) increasing cultivars’ thermal time requirement, (iv) water harvesting, and (v) increasing resilience to heat stress during the flowering period. We adopt a new assessment framework to account for both the impacts of proposed adaptation options in the historical climate and their ability to reduce the impacts of future climate change, and we also consider changes in both mean yield and inter-annual yield variability. We target the future period of 2031–2060 for the “business-as-usual” scenario (RCP8.5), and compare with the historical period of 1961–1990. Our results reveal that most proposed “adaptation options” are not more beneficial in the future than in the historical climate (−12% to +4% in mean yield), so that they do not really reduce the climate change impacts. Increased temperature resilience during the grain number formation period is the main adaptation that emerges (+4.5%). Intensification of fertilizer inputs can dramatically benefit yields in the historical/current climate (+50%), but does not reduce negative climate change impacts except in scenarios with substantial rainfall increases. Water harvesting contributes to a small benefit in the current climate (+1.5% to +4.0%) but has little additional benefit under climate change. Our analysis of uncertainties arising from crop model differences (conditioned on the used model versions) and various climate model projections provide insights on how to further constrain uncertainties for assessing future climate adaptation options.

Prioritizing regular demand while reserving capacity for emergency demand

This research is motivated by the capacity allocation problem at a major provider of customized products to the oil and gas drilling industry. We formulate a finite-horizon, discrete-time, dynamic programming model in which a firm decides how to reserve capacity for emergency demand and how to prioritize two classes of regular demand. While regular demand can be backlogged, emergency demand will be lost if not fulfilled within the period of its arrival. Since backlogging cost accumulates over time, we find it optimal for the firm to adopt a dynamic prioritization policy that evaluates the priorities of different classes of regular demand every period. The optimal prioritization involves metrics that measure backlogging losses from various perspectives. We fully characterize the firm’s optimal prioritization and reservation policy. Those characterizations shed light on managerial insights.

Delayed hepatocellular carcinoma model for end-stage liver disease exception score improves disparity in access to liver transplant in the United States.

The current system granting liver transplant candidates with hepatocellular carcinoma (HCC) additional Model for End-Stage Liver Disease (MELD) points is controversial due to geographic disparity and uncertainty regarding optimal prioritization of candidates. The current national policy assigns a MELD exception score of 22 immediately upon listing of eligible patients with HCC. The aim of this study was to evaluate the potential effects of delays in granting these exception points on transplant rates for HCC and non-HCC patients. We used Scientific Registry of Transplant Recipients data and liver simulated allocation modeling software and modeled (1) a 3-month delay before granting a MELD exception score of 25, (2) a 6-month delay before granting a score of 28, and (3) a 9-month delay before granting a score of 29. Of all candidates waitlisted between January 1 and December 31, 2010 (n = 28,053), 2773 (9.9%) had an HCC MELD exception. For HCC candidates, transplant rates would be 108.7, 65.0, 44.2, and 33.6 per 100 person-years for the current policy and for 3-, 6-, and 9-month delays, respectively. Corresponding rates would be 30.1, 32.5, 33.9, and 34.8 for non-HCC candidates. A delay of 6-9 months would eliminate the geographic variability in the discrepancy between HCC and non-HCC transplant rates under current policy and may allow for more equal access to transplant for all candidates.

A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies

Buildings are responsible for some 40% of the total final energy consumption in the European Union and about 40% of the world’s primary energy consumption. Hence, the reduction of primary energy consumption is important for the overall energy chain. The scope of the current work is to assess the energy efficiency measures in the residential and small commercial sector and to develop a methodology and a software tool for their optimal prioritization.The criteria used for the prioritization of energy efficiency measures in this article are the primary energy consumption and the initial investment cost. The developed methodology used is generic and could be implemented in the case of a new building or retrofitting an existing building. A multi-objective mixed-integer non-linear problem (MINLP) needs to be solved and the weighted sum method is used. Moreover, the novelty of this work is that a software tool has been developed using ‘Matlab®’ which is generic, very simple and time efficient and can be used by a Decision Maker (DM). Two case studies have been developed, one for a new building and one for retrofitting an existing one, in two cities with different climate characteristics. The building was placed in Edinburgh in the UK and Athens in Greece and the analysis showed that the primary energy consumption and the initial investment cost are inversely proportional.

The Least Penalty Optimization Prioritization Operators for the Analytic Hierarchy Process: A Revised Case of Medical Decision Problem of Organ Transplantation

ABSTRACTThe Analytic Hierarchy Process (AHP) is one of the popular methods to support human decision making. Prioritization method leading to the possibility of rank reversal is still one of the unsettled issues of the AHP, although many applications of this method have been made. In this study, 10 important prioritization methods are reviewed and compared. To evaluate the fitness of the prioritization operators (POs), on the basis of existing PO measurement criteria, this research proposes the Root Mean Penalty Weighted Square Variance (RMPWSV) as a reasonable measurement criterion. Graphical solution demonstrates the computational concept for the RMPWSV. On the basis of the RMPWSV, two Least Penalty Optimization (LPO) POs are proposed as the new perspectives for solving the prioritization problems: Least Product of Penalty and Direct Squares (LPPDS) and Least Product of Penalty and Weighted Squares (LPPWS). The comprehensive numerical analyses verify that LPPDS is the fittest PO with respect to the RMPWSV. To demonstrate improvement of the proposed method, a medical decision problem for organ transplantation is revised by using 11 developed POs and two proposed POs with respect to RMPWSV measurement. LPPDS is the ideal PO, instead of Eigenvector method, to generate a priority vector for a decision problem in various fields, such as social science, politics, business, finance, medical treatment, resource management, and engineering management.

Particle Swarm Optimization Method for Optimal Prioritization of Pavement Sections for Maintenance and Rehabilitation Activities

Optimal prioritization of maintenance and rehabilitation (M&R) activities for pavement sections can enable significant time and cost-savings. In this study, we used the particle swarm optimization (PSO) method to achieve optimal prioritization of 135 pavement sections based on eight pavement condition parameters. The parameters included standard deviation (SD) for smoothness, rutting, deflections, cracking, pothole, bleeding, patching, and shoving. SD for smoothness, rutting, and deflections were inspected using instruments, while cracking, pothole, bleeding, patching, and shoving were surveyed visually. The PSO method was used to quickly calculate the synthetic pavement condition for each pavement section and then obtain the optimal prioritization of pavement sections. With this approach, pavement engineers are able to efficiently perform appropriate and timely M&R activities for pavement sections, according to their priority. This study provides an alternative solution to current approaches for prioritization of pavement sections.

SEISMIC RISK ASSESSMENT OF ALGERIAN BUILDINGS IN URBAN AREA

The recent earthquakes in Algeria have caused significant human and economic losses. The frequent occurrence of damaging earthquakes clearly demonstrates the urgent need of study on seismic risk assessment methods of buildings to effectively reduce the impact of earthquake in Algerian cities. This article contributes to the development and the application of a seismic risk method, appropriate to Algerian building specificities. This method considers five damage levels, defined accordingly to macroseismic intensity and the seismic quality of the building by means of a vulnerability index. The present paper summarises the development of a tool to integrate seismic hazard and structural information and support the decision making process in identification of seismic risk. This tool could be used in the inspection of buildings and the optimal prioritisation of strengthening and preventives remedial actions that are necessary prior to a major earthquake event.

Prioritization strategies for patient evacuations

Evacuation from a health care facility is considered last resort, and in the event of a complete evacuation, a standard planning assumption is that all patients will be evacuated. A literature review of the suggested prioritization strategies for evacuation planning-as well as the transportation priorities used in actual facility evacuations-shows a lack of consensus about whether critical or non-critical care patients should be transferred first. In addition, it is implied that these policies are "greedy" in that one patient group is given priority, and patients from that group are chosen to be completely evacuated before any patients are evacuated from the other group. The purpose of this paper is to present a dynamic programming model for emergency patient evacuations and show that a greedy, "all-or-nothing" policy is not always optimal as well as discuss insights of the resulting optimal prioritization strategies for unit- or floor-level evacuations.

Optimal prioritisation of watershed management measures for flood risk mitigation on a watershed scale

AbstractWatershed management includes methods to create, enhance, and maintain vegetation to reduce run‐off and provide flood control in the watershed. The assignment of priority for watershed management measures requires the use of mathematical techniques to attain the most suitable strategies. In the present study, a framework was presented for assigning the optimal combinations of watershed management measures based on simulation‐based optimisation approach. For this purpose, a one‐dimensional hydrodynamic model was used to calculate the potential damages of different flood scenarios under various combinations of watershed management measures and was coupled with the NSGA‐II multi‐objective optimisation model to provide the optimal Pareto solutions between two conflicting objectives of minimising the investment costs of flood mitigation measures and the expected flood damages of the watershed. The proposed model was then applied to a small watershed in the centre of Iran as a case study, and the optimal trade‐off solutions were calculated for flood risk mitigation. Using these trade‐offs, for each level of funding, decision makers can select the optimal combination of watershed management measures considering decision criteria.

Optimal examination and prioritization of the factors affecting customers’ satisfaction using integrated Quality Function Deployment (QFD) and Kano’s model: Case study of ’s Refah bank

Product and service quality is an important factor influencing customers’ satisfaction. Customers’ satisfaction level depends on the level to which customers’ needs are met. Quality function deployment (QFD) is a valuable tool used for the conversion of customers’ voice into product/service. However, Kano’s model provides fundamental, motivating, and functional requirements for customers’ satisfaction. Kano’s model can be used in QFD matrix to ensure satisfaction of the most critical customers’ needs. In the present study, the needs of Refah bank located in Shiraz are collected using Kano’s questionnaire. Then, customers’ requirements to be imported into house of quality are determined. Afterwards, customers’ requirements are transferred to the house of quality matrix where their relationship and technical requirements is examined by experts in the banks. Finally, priority of technical and customers’ requirements is determined. Results show that giving the same attention to different customers’ requirements can waste an organization’s energy. Companies that separate customers and rank customers’ needs, focus on some specifications of their products/services that are of the highest importance for the customers. These companies, with this approach, will succeed in their objectives. Key words: Quality function deployment, Kano’s model, house of quality, customers’ satisfaction.

Health benefit modelling and optimization of vehicular pollution control strategies

This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause–effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific control strategies with maximization of social benefits, when these strategies are applied simultaneously.

A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions

Detection of gene-environment interactions using an exhaustive search necessarily raises the multiple hypothesis problem. While frequently used to control for experiment-wise type I error, Bonferroni correction is overly conservative and results in reduced statistical power. We have previously shown that prioritizing SNPs on the basis of heterogeneity in quantitative trait variance per genotype leads to increased power to detect genetic interactions. Our proposed method, variance prioritization (VP), selects SNPs having significant heterogeneity in variance per genotype using a pre-determined P-value threshold. We now suggest prioritizing SNPs individually such that the optimal heterogeneity of variance P-value is determined for each SNP. The large number of SNPs in genome-wide studies calls for a fast algorithm to output the optimal prioritization threshold for each SNP. In this report, we present such an algorithm, the Gene Environment Wide Interaction Search Threshold (GEWIST), and show that the use of GEWIST will increase power under a variety of interaction scenarios. Furthermore, by integrating over possible interaction effect sizes, we provide a framework to optimize prioritization in situations where interactions are a priori unknown.

Early fault detection model using integrated and cost-effective test case prioritization

Regression testing is one of the important software maintenance activities that let the software tester to ensure the quality and reliability of modified program. Although the regression testing is expensive, it is necessary to validate the software after every modification. To reduce the cost of regression testing, software tester may utilize test cases prioritization techniques. One potential goal prioritization is to increase a test suite’s rate of fault detection. An improved rate of fault detection can provide earlier feedback on the system, enabling earlier debugging. APFD (Average Percentage of Fault Detected) metric is used to measure the test suite’s fault detection rate. This paper presents an integrated test case prioritization approach to increase the test suite’s fault detection rate. Three important factors such as program change level (PCL), test suite change level (TCL) and test suite size (TS) are considered to prioritize test cases. Proposed approach is applied on different in-house programs to validate its accuracy. Model results are found to be promising when compared with optimal prioritization techniques which always results an upper bound of APFD values.

Optimal pandemic influenza vaccine allocation strategies for the Canadian population.

BackgroundThe world is currently confronting the first influenza pandemic of the 21st century. Influenza vaccination is an effective preventive measure, but the unique epidemiological features of swine-origin influenza A (H1N1) (pH1N1) introduce uncertainty as to the best strategy for prioritization of vaccine allocation. We sought to determine optimal prioritization of vaccine distribution among different age and risk groups within the Canadian population, to minimize influenza-attributable morbidity and mortality.Methodology/Principal FindingsWe developed a deterministic, age-structured compartmental model of influenza transmission, with key parameter values estimated from data collected during the initial phase of the epidemic in Ontario, Canada. We examined the effect of different vaccination strategies on attack rates, hospitalizations, intensive care unit admissions, and mortality. In all scenarios, prioritization of high-risk individuals (those with underlying chronic conditions and pregnant women), regardless of age, markedly decreased the frequency of severe outcomes. When individuals with underlying medical conditions were not prioritized and an age group-based approach was used, preferential vaccination of age groups at increased risk of severe outcomes following infection generally resulted in decreased mortality compared to targeting vaccine to age groups with higher transmission, at a cost of higher population-level attack rates. All simulations were sensitive to the timing of the epidemic peak in relation to vaccine availability, with vaccination having the greatest impact when it was implemented well in advance of the epidemic peak.Conclusions/SignificanceOur model simulations suggest that vaccine should be allocated to high-risk groups, regardless of age, followed by age groups at increased risk of severe outcomes. Vaccination may significantly reduce influenza-attributable morbidity and mortality, but the benefits are dependent on epidemic dynamics, time for program roll-out, and vaccine uptake.

Optimal pandemic influenza vaccine allocation strategies for the canadian population.

Background: The world is currently confronting the first influenza pandemic of the 21st century. Influenza vaccination is an effective preventive measure, but the unique epidemiological features of swine-origin influenza A (H1N1) (pH1N1) introduce uncertainty as to the best strategy for prioritization of vaccine allocation. We sought to determine optimal prioritization of vaccine distribution among different age and risk groups within the Canadian population, to minimize influenza-attributable morbidity and mortality.Methodology/Principal Findings: We developed a deterministic, age-structured compartmental model of influenza transmission, with key parameter values estimated from data collected during the initial phase of the epidemic in Ontario, Canada. We examined the effect of different vaccination strategies on attack rates, hospitalizations, intensive care unit admissions, and mortality. In all scenarios, prioritization of high-risk groups (individuals with underlying chronic conditions and pregnant women) markedly decreased the frequency of severe outcomes. Preferential vaccination of age groups at increased risk of severe outcomes following infection resulted in decreased mortality compared to targeting vaccine to age groups with higher transmission, at a cost of higher population-level attack rates. All simulations were sensitive to the timing of the epidemic peak in relation to vaccine availability, with vaccination having the greatest impact when it was implemented well in advance of the epidemic peak. Conclusions/Significance: Our model simulations suggest that vaccine should be allocated to high-risk groups, regardless of age, followed by age groups at increased risk of severe outcomes. Vaccination may significantly reduce influenza-attributable morbidity and mortality, but the benefits are dependent on epidemic dynamics, time for program roll-out, and vaccine uptake.

Prioritization of coastal properties for conservation in New York State

Conservation of coastal lands reduces non-point source pollution loads into oceans and estuaries, retains natural areas and saves ecological communities from disappearance and change. A recent agreement for protection of Long Island Sound waters in New York and Connecticut established 30 environmental and management goals. One of them is establishment of a listing of existing undeveloped properties and their prioritization for natural resource conservation and outdoor recreation. The optimal prioritization approach poses strong constraints and methodological challenges on selection of data for analysis, assignment of a priority score to each property unit and the assessment of this assignment. To be a practical tool, the prioritization model should be reproducible and include a mechanism for evaluation of obtained prioritization scenarios. Presented study uses Geographic Information System (GIS) to assign conservation priority scores to unprotected and undeveloped parcels greater than five acres in size within New York’s Long Island Sound coastal area. The method combines spatial multi-criteria analysis and statistical methods. The results of this project include identification and prioritization of more than 700 undeveloped properties on New York coast. The most important finding of GIS analysis was the discovery of clusters of vacant parcels that together form large areas available for future conservation. These results offer new conservation tools and strategies to coastal managers and government in New York State.

Active Learning Through Sequential Design, With Applications to Detection of Money Laundering

Money laundering is a process designed to conceal the true origin of funds that were originally derived from illegal activities. Because money laundering often involves criminal activities, financial institutions have the responsibility to detect and report it to the appropriate government agencies in a timely manner. But the huge number of transactions occurring each day make detecting money laundering difficult. The usual approach adopted by financial institutions is to extract some summary statistics from the transaction history and conduct a thorough and time-consuming investigation on those suspicious accounts. In this article we propose an active learning through sequential design method for prioritization to improve the process of money laundering detection. The method uses a combination of stochastic approximation and D-optimal designs to judiciously select the accounts for investigation. The sequential nature of the method helps identify the optimal prioritization criterion with minimal time and effort. A case study with real banking data demonstrates the performance of the proposed method. A simulation study shows the method’s efficiency and accuracy, as well as its robustness to model assumptions.