Tool For Allocation Research Articles

Sustainable Allocation Model of Construction Workforce for Work Resumption during COVID-19

COVID-19 has posed challenges for the construction industry, such as precise pandemic control, sustainable labor relations, and loss minimization. In response to these challenges, this study has developed a decision model that optimizes workforce allocation for projects to achieve sustainable workforce management, a tradeoff between pandemic prevention and work resumption. The priority of project resumption was evaluated using basic characteristics, the long- and short-term strategies, and the regional pandemic situation. The energy level of skilled workers was graded according to construction team size, skill level, and experience. Sustainable allocation principles and paths were explored to target four different types of work resumption plans. We used the cellular automaton (CA) technique to simulate the sustainable allocation model. We also analyzed the similarity function of energy levels and the time-cost function of allocation. The case study of the SGJ Construction demonstrates that this allocation model can accurately simulate work resumption and provide a sustainable allocation decisions and tools under pandemic. Also, it implies balanced interests and concerns between construction companies and the society for work resumption during COVID-19.

Development and validation of the OUTCoV score to predict the risk of hospitalisation among patients with SARS-CoV-2 infection in ambulatory settings: a prospective cohort study

ObjectivesTo develop and validate a rule-out prediction model for the risk of hospitalisation among patients with SARS-CoV-2 infection in the ambulatory setting to derive a simple score to determine outpatient...

Ethical decision making during a healthcare crisis: a resource allocation framework and tool

The COVID-19 pandemic has strained healthcare resources the world over, requiring healthcare providers to make resource allocation decisions under extraordinary pressures. A year later, our understanding of COVID-19 has advanced,...

Volatility Targeting: The Bridge Between Options-Based and Traditional Defensive Strategies

Defensive strategies have been in high demand since the Global Financial Crisis. For almost a decade, investors mainly had two traditional strategies from which to choose: minimum volatility and low volatility. Buffer strategies have become a third option, using derivatives to create known payoffs at maturity. The author demonstrates that volatility targeting is an effective fourth option. Although the payoff certainty of the buffer strategy at maturity may appeal to some investors, the equivocal decision to allocate to one monthly series versus another may leave investors with uncertainty. Backtested simulations highlight the restrictive upside participation of the buffer series, with only the July series delivering investors a higher Sharpe ratio than the three other defensive strategies. Finally, factor replicated portfolios demonstrate that volatility targeting is not synonymous with either low/minimum volatility or buffer-type strategies but, rather, has characteristics of all three, making it an effective, best-of-both-worlds allocation tool for defensive investors going forward, especially given the current level of interest rates. TOPICS:Portfolio construction, derivatives, options, simulations, performance measurement Key Findings ▪ Ambiguous portfolio characteristics result in uncertainty regarding which monthly buffer series is optimal for portfolio allocation. ▪ Risk control circumvents many disadvantages of other defensive strategies (including interest rate risk) while also providing additional tail-risk benefits. ▪ A risk control series can be replicated from factor exposures strictly from a mixture of a buffer series and two traditional defensive series (low volatility and minimum volatility), indicating a best-of-both-worlds strategy.

Towards a decision-support tool for airport slot allocation: Application to Guarulhos (Sao Paulo, Brazil)

In the vast majority of airports where demand exceeds capacity, landing and take-off slots are allocated according to the process established by the Worldwide Slot Guidelines (WSG). An optimization-based decision-support tool is introduced in this paper to assist slot coordinators in the initial slot allocation – the most important stage of that process. The WSG comprises both primary and additional criteria to be taken into account by slot coordinators at this stage. Considering all additional criteria specified in the WSG, this tool generates, in an acceptable amount of time, alternative efficient slot allocations against which the coordinators can evaluate the solutions obtained through the heuristic methods they apply. The decision-support tool also offers solution analysis features, such as visualization, ranking, comparison of multiple optimal solutions, and comparison of solutions regarding important characteristics not reflected in the WSG additional criteria. The tool is being tested at one of the largest airports worldwide – Guarulhos (Sao Paulo, Brazil) – with results that clearly illustrate its usefulness in practice.

Identification of water scarce regions of kunthipuzha river basin using water allocation tool

Water scarcity and consequent droughts are of serious concern for almost all countries around the world. Since climate change is expected to exacerbate the already existing water stresses, the improvement of water efficiency against water scarcity and drought is very much important. The study aims at pin pointing the water stressed areas in Kunthipuzha River basin so that management practices can be suggested for the identified areas to minimize the water scarcity problems. To contribute towards this goal, Water Evaluation and Planning (WEAP) model is used, which is a Decision Support System (DSS) to assess water availability and investigate the impacts of different water allocation scenarios (water demand management strategies) aimed at meeting various sectorial water demands. The results from the study revealed that out of 13 sub basins of Kunthipuzha, 2 comes under severely stressed, 2 under moderately stressed and 3 under lowly stressed regions. The results derived can be used in future to work out feasible management practices for mitigating the water scarcity problems.

A classification tree to assist with routine scoring of the Clinical Frailty Scale.

Backgroundthe Clinical Frailty Scale (CFS) was originally developed to summarise a Comprehensive Geriatric Assessment and yield a care plan. Especially since COVID-19, the CFS is being used widely by health care professionals without training in frailty care as a resource allocation tool and for care rationing. CFS scoring by inexperienced raters might not always reflect expert judgement. For these raters, we developed a new classification tree to assist with routine CFS scoring. Here, we test that tree against clinical scoring.Objective/Methodswe examined agreement between the CFS classification tree and CFS scoring by novice raters (clerks/residents), and the CFS classification tree and CFS scoring by experienced raters (geriatricians) in 115 older adults (mean age 78.0 ± 7.3; 47% females) from a single centre.Resultsthe intraclass correlation coefficient (ICC) for the CFS classification tree was 0.833 (95% CI: 0.768–0.882) when compared with the geriatricians’ CFS scoring. In 93%, the classification tree rating was the same or differed by at most one level with the expert geriatrician ratings. The ICC was 0.805 (0.685–0.883) when CFS scores from the classification tree were compared with the clerk/resident scores; 88.5% of the ratings were the same or ±1 level.Conclusionsa classification tree for scoring the CFS can help with reliable scoring by relatively inexperienced raters. Though an incomplete remedy, a classification tree is a useful support to decision-making and could be used to aid routine scoring of the CFS.

Understanding Resource Allocation Processes in Social Care for Frail Older People: Lessons from a National Survey

Context: Traditionally local authorities in England allocated resources in social care following a professional assessment of need and a costed care plan. With the introduction of personal budgets, resource allocation tools have been used to provide service users with an initial indicative budget for their care. This is promoted as being more transparent, equitable and giving people greater control over decisions about their care. Objective: This study examined the different approaches to resource allocation and the content of resource allocation tools used for social care in England. Methods: Information was obtained from local authorities about their resource allocation systems. An analytic framework was developed and applied to the tools to explore: who identified needs; whether informal and formal support were recorded; and whether 17 need indicators covering functional status, mental health, and health and wellbeing were present and in what detail. Findings: Ninety-one per cent of 152 authorities responded and 61 per cent of authorities’ tools were analysed. Three approaches were identified: points-based self-assessment tools; standardised assessment data (FACE); and non-points-based/ready reckoner tools. Most authorities used a points-based self-assessment tool. All tools included the service user’s views and a high proportion included a professional’s view, while fewer covered the carer’s view on need. Coverage and presence of detail for the 17 need indicators showed high variation and was least on points-based self-assessment tools. Limitations: The study is the first to examine a large sample of resource allocation tools and provides a valuable baseline for future work. However, non-points-based/ready reckoner tools were under-represented in the sample. Implications: Further research could build upon this study to examine key properties of the tools used such as reliability, validity, sensitivity and specificity; and explore their impact upon service users and staff in terms of time use, cost, utility and equity.

Uncertainty Analysis of SWAT Modeling in the Lancang River Basin Using Four Different Algorithms

The hydrological model is the primary tool for regional water resources management, allocation, and prediction. However, these models always suffer from large uncertainties from multiple sources. Therefore, it is necessary to conduct an uncertainty analysis before performing hydrological simulation. Sequential Uncertainty Fitting (SUFI-2), Parameter Solution (ParaSol), Generalized Likelihood Uncertainty Estimation (GLUE), and Particle Swarm Optimization (PSO) integrated with the SWAT-CUP software were used to calibrate the Soil and Water Assessment Tool (SWAT) model and quantify the parameter sensitivity and prediction uncertainty of the SWAT in the Lancang River (LR) Basin, which is located in the southwest of China. This model was calibrated and validated using the four algorithms both at the daily scale, and the optimal simulation results derived by the four methods showed that the SWAT model performed well over the Yunjinghong station with Nash–Sutcliffe efficiency coefficient (NSE) and coefficient of determination (R2) values greater than 0.8 both in the calibration (1975 to 1989) and validation (1990 to 2004) periods. Among the four algorithms, the ParaSol algorithm produced the best simulation result at the daily scale with NSE values of 0.89 and 0.90 for the calibration and validation periods, respectively. Furthermore, the ParaSol algorithm has the greatest proportion of simulations (94%) with an NSE greater than 0.5. Parameter sensitivity analysis results demonstrated that the four methods all can be used for parameter sensitivity analysis in streamflow simulation, and they all identified that the base flow factor for bank storage (ALPHA_BNK) and effective hydraulic conductivity in the main channel alluvium (CH_K2) were more sensitive. The uncertainty analysis of model parameters showed that the parameter 95PPU (95% prediction uncertainty) width yielded by the ParaSol algorithm was the smallest compared with that of the other methods, followed by PSO, SUFI-2, and GLUE. The uncertainty analysis of the model simulation indicated that the SUFI-2 and PSO methods can achieve satisfactory results (with P-factor > 0.7 and R-factor < 1.5) at the daily scale; among them, SUFI-2 (P-factor = 0.93, R-factor = 1.17) performed much better than PSO (P-factor = 0.78, R-factor = 1.14). In general, by comparing its evaluation criteria (NSE, R2, RE, P-factor, and R-factor) to other methods, ParaSol stood out as the most efficient tool for model calibration. However, SUFI-2 remains the most robust method to perform uncertainty analysis considering its uncertainties of model structure, model inputs, and parameters. This study provides insight into hydrological simulation of the LR Basin using the appropriate algorithm to calibrate the model and implement the uncertainty analysis.

Industrial Insurance Groups Cost Management. Mathematical Modeling and Applied Software

This paper covers acquisition cost control optimization in the insurance sector through managerial tools that include optimization models applied method, and analysis within the insurance companies to achieve higher efficiency. The model development with presented costs volume and supposed market volume in each product branch is needed to optimize acquisition costs in insurance companies. The model has been simplified due to the presence of big data in each product branch. Our study is based on classic works and textbooks on optimization and non-linear programming. The results of digital computer calculations for two types of insurance activity are present here. The applied software tool for the acquisition costs allocation is also considered.

Decision support tool for hospital resource allocation during the COVID-19 pandemic.

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized “what-if” scenarios which are key for real-time experimentation and analysis of current decisions’ down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

Comparison of Selected Subjects of the Non-profit Sector in the Conditions of the Slovak Republic

Non-profit organizations are a specific type of entities in the national economy that significantly helps to improve the living standards of the country’s population. The article focuses on the financial performance of entities in the non-profit sector of the Slovak Republic depending on the financial instrument of tax allocation, taking into account the time factor during which the entities operate in the non-profit sector. The aim of the article is to analyse the financial performance of non-profit organizations providing services of general interest and foundations with the subsequent specification of the results of the issue. We work with the hypothesis that in terms of financial performance, foundations will perform better compared to non-profit organizations. Segmentation, comparison and descriptive methods were used in the solved issue. Only in one of the analysed groups did we come to the result that the non-profit organization was able to obtain more funds than the foundation through the tax allocation tool in 2019. In the case of the other groups analysed, foundations were more financially efficient, whose performance significantly exceeded that of non-profit organizations. In terms of market life, based on the analysis, the most efficient organizations were organizations with a market life of 1-5 years and with a longer market life, their financial performance decreased. In the case of foundations, we found that the highest performance is achieved by foundations with a market existence of 15 - 20 years. We can therefore state that the established hypothesis was only partially confirmed, although in most cases foundations were more financially efficient.

The Gain-Pain Index: Asset Allocation for Individual (And Other?) Investors

Individual investors typically determine their asset allocation using investor questionnaires, which can be viewed as black boxes that generate a result without highlighting the benefits and costs of the portfolios considered. This article introduces an asset allocation tool, the gain-pain index (GPI), that overcomes this shortcoming. The tool proposed incorporates two critical variables found in investor questionnaires, the portfolio holding period and the investor’s risk tolerance, and broadens the definition of risk beyond volatility by also considering the probability of suffering a loss and the magnitude of the loss. The model is used to determine optimal asset allocations for 21 countries and the world market, for different holding periods and levels of risk aversion.

Minimization of Water Use Conflicts in Kunthipuzha River Basin by Use of Water Allocation Tool

Water scarcity and consequent droughts are of serious concern for almost all countries around the world. Since climate change is expected to exacerbate the already existing water stresses, the improvement of water efficiency against water scarcity and drought is very much important. The water stressed areas within the Kunthipuzha river basin were identified by allocating the available water for various demands using the WEAP model. This study aims at suggesting some Best Management Practices (BMPs) for those water stressed areas so as to minimize the water scarcity problems. To contribute towards this goal, Water Evaluation and Planning (WEAP) model is used, which is a decision support system (DSS) to assess water availability and investigate the impacts of different water allocation scenarios (water demand management strategies) aimed at meeting various sectorial water demands. The results from the study revealed that the implementation of BMP’s like improving the irrigation efficiency, changing of planting date, changing of crops etc. could bring in significant reduction in water scarcity problems in the identified hotspots. Moreover, in certain sub basins it was found that it would be possible to completely eliminate water scarcity by implementing combinations of BMPs. Thus, such studies done in an extensive manner will help in finding a balance between what is needed and what is available within the environment which in turn serves as an important step in achieving water resource sustainability.

The City as a License. Implications of Blockchain and Distributed Ledgers for Urban Governance

Distributed ledger technologies (DLTs) such as blockchain have in recent years been presented as a new general-purpose technology that could underlie many aspects of social and economic life, including civics and urban governance. In an urban context, over the past few years, a number of actors have started to explore the application of distributed ledgers in amongst others smart city services as well as in blockchain for good and urban commons-projects. DLTs could become the administrative backbones of such projects, as the technology can be set-up as an administration, management and allocation tool for urban resources. With the addition of smart contracts, DLTs can further automate the processing of data and execution of decisions in urban resource management through algorithmic governance. This means that the technological set-up and design of such DLT based systems could have large implications for the ways urban resources are governed. Positive contributions are expected to be made toward (local) democracy, transparent governance, decentralization, and citizen empowerment. We argue that to fully scrutinize the implications for urban governance, a critical analysis of distributed ledger technologies is necessary. In this contribution, we explore the lens of “the city as a license” for such a critical analysis. Through this lens, the city is framed as a “rights-management-system,” operated through DLT technology. Building upon Lefebvrian a right to the city-discourses, such an approach allows to ask important questions about the implications of DLTs for the democratic governance of cities in an open, inclusive urban culture. Through a technological exploration combined with a speculative approach, and guided by our interest in the rights management and agency that blockchains have been claimed to provide to their users, we trace six important issues: quantification; blockchain as a normative apparatus; the complicated relationship between transparency and accountability; the centralizing forces that act on blockchains; the degrees to which algorithmic rules can embed democratic law-making and enforcing; and finally, the limits of blockchain's trustlessness.

AVAILABILITY HEURISTICS AND INSIGHTS FOR CORRUPTION

The most influencing insight which economic analysis of law provides is that human actors respond to incentives. This insight has two implications; first, law can be employed as an apparatus to bring in socially desirable behaviour as well as to discourage socially undesirable behaviour. In other words, law can be used as tool by policy makers to subsidize socially desirable behaviour and tax undesirable behaviour1 . Secondly, law can be used from the perspective of efficiency and distributive concerns i.e. it can be used as a device to encourage or discourage the production of social resources as well as a tool for efficient allocation of such social resources. To exhibit in a coherent manner the repercussions of the incentives on people in a legal system the analysts borrowed from economics the ‘rational choice theory’ which focuses on assumptions relating to how human actors respond to incentives. These assumptions were then applied to the domain of law to understand how law as a tool can be used in incentivising and de-incentivising certain behaviour. Thus, these incentive effects of legal rules were studied and analyzed in designing an efficient legal policy to attain desired social behaviour. The underlying conception of the rational choice theory as pointed out by Richard Posner is that “man is a rational maximizer of his ends”2 the adherents of ‘utility maximization version’ of rational choice theory take the above conception a bit further as according to them:

Urban rent control: a decision support tool for the optimal resources allocation between urban forest projects

Green areas in urban agglomerations are strategic resource for the sustainable city development. The implementation of Urban Forestry Projects (UFP) allows on the one hand to raise the environmental quality level, improving the microclimate and preserving biodiversity, on the other hand to promote urban regeneration and promote socio-economic development by creating eco-systemic s er vices for the population. The result is a more rational land use and an increase in real estate values. Although the EU Directives show the need to promote the sustainable territory growth through the recover y and redevelopment of the built environment, the implementation of investments based on eco-system logic is rarely counted as a priority action for the city, often preferring a different allocation of available resources. The present work aims first to define an indicators set useful to express the value components – financial, social, cultural and ecological- environmental – for the UFP. These indicator s are the reference terms for the characterization of an innovative protocol of multicriteria analysis for the public operator who wants to establish the optimal distribution of funds between UFP units in limited areas of the urban fabric. The protocol uses the algorithms of mathematical programming and is tested on a case study about urban areas to be redeveloped.

PIN121 Longitudinal Trends and Burden of Tropical Infectious Diseases in Colombia: A Real-World DATA Analysis

Tropical diseases subsume all diseases occurring solely or mainly in tropical regions. Those diseases are often neglected and pose a significant burden among the healthcare systems of these regions. Therefore, our study aims to assess the epidemiological trends and exploratory disease burden indicators of four infectious diseases: dengue, tuberculosis, zika and chikungunya in Colombia. Data on cases, hospitalizations and mortality of tropical diseases across the 2014–2018 period was extracted from the SIVIGILA (Epidemiological Surveillance System) database, which contains the registry of diseases subjected to mandatory notification in Colombia. Yearly incidence rate, hospitalization rate and case fatality rate were estimated and compared during the analyzed period. The average yearly incidence rates for dengue, tuberculosis, zika and chikungunya were 72.12, 29.30, 40.30 and 33.31 cases per 100,000 people; the average yearly hospitalization rates were 48.19%, 51.48%, 9.79%, 9.79% and the average yearly case fatality rates were 2.97, 80.47, 2.84 and 1.05 deaths per 1000 cases, respectively. Overall, viral diseases (dengue, zika and chikungunya) experienced a steep rise on weekly incidence rates in 2015 – 2016, which coincides with their outbreak dates in Latin America. Tuberculosis incidence rates maintained constant throughout the analyzed period; zika and chikungunya rates are decreasing over time and dengue rates are experiencing a steady increase recently. Furthermore, there are subgroups among diseases with significantly different impacts on burden indicators. For example, the average yearly hospitalization rate of severe dengue is 94.1% whereas that of mild to moderate dengue is 48.1%. Similarly, the average yearly case fatality rate of meningitis tuberculosis is significantly greater than the ones of pulmonary and extrapulmonary tuberculosis: 267.24, 71.00 and 73.87 deaths per 1000 cases, respectively. Real-World Data (RWD) can be a powerful tool for early outbreak detection and healthcare resource allocation for tropical diseases in Latin America.

Crop pattern planning and irrigation water allocation compatible with climate change using a coupled network flow programming-heuristic optimization model

ABSTRACT Sustainable agricultural production has encountered difficulties such as water scarcity, improper use of available water resources and climate change in arid countries like Iran. Simulation-optimization approaches are helpful tools for crop pattern planning and irrigation water allocation to ensure maximum net benefit is gained from the system. In this paper, optimum cultivation area and allocation of irrigation water in conditions compatible with climate change are obtained for the Borkhar plain in Iran. To achieve this, the network flow programming-based MODSIM, as a water allocation simulation model, is coupled with the grey wolf optimization (GWO) algorithm to obtain the optimum irrigation amounts and cultivation areas in the plain under two conditions: status quo, and with climate change-affected streamflows. The Hadley Centre coupled Model version 3 (HadCM3) and the second-generation Canadian Earth System Model (CanESM2) are used to generate the climatic parameters in the study area. The Identification of unit Hydrographs and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) rainfall–runoff model is applied to calculate the coefficients of variation for the Zayandehroud River streamflows, as the surface water resource for irrigation of the plain. Results indicate that the agricultural net benefit gained from the plain will decrease by 1.5% in the A2 emissions scenario, and by 3.5%, 8% and 17.5% in the three representative concentration pathway (RCP) scenarios in the optimum states obtained by the GWO-MODSIM model. Moreover, the cultivation areas are decreased in the climate change scenarios. Therefore, appropriate management policies should be adopted for adaptation to the likely future situation.

Simulation models to support the preliminary electoral results program for the Mexican Electoral Institute

On July 1st, 2018, federal elections for president, senators and deputies took place in Mexico. In most states, elections for state governors and representatives took also place in the same polling booths. The Technical Unit for Information Services (UNICOM) of the National Electoral Institute (INE) of Mexico has the responsibility for planning and implementation of the Preliminary Electoral Results Program (PREP) for federal elections. For the 2018 elections UNICOM developed forecasting models for the performance of PREP based on simulation models that were developed using a special purpose simulation software and C++ subroutines for fast simulation of queues. These simulation models were a valuable tool for planning, scheduling and allocation of the main resources that participated in the operational process of the PREP. In this article we report the main features, applications and results obtained by using these simulation models.