Socio-economic Planning Sciences最新文献

A major lesson healthcare managers learned from the COVID-19 outbreak is the need for more effective patient classification and medical resource allocation for future pandemics. In their view, hospitalization mortality could be greatly reduced if more effective systems for patient classification were in place before the outbreak to evaluate and assign treatment facilities. This study presents a scalable patient clustering approach using a Self-Organizing Map (SOM) of the Artificial Neural Network (ANN) to cluster patients for appropriate treatment allocation. The patients’ membership is forecasted using Data Envelopment Analysis–Discriminant Analysis (DEA-DA). The objectives of this research are to develop a flexible framework that healthcare systems can adopt to cluster patients based on specific testing criteria from medical records and to assign them to suitable medical centers with appropriate treatment resources. This method aims to enhance healthcare system efficiency by ensuring patients with severe illnesses receive care at well-equipped centers, while those with milder symptoms are directed to other suitable facilities. The approach is scalable and adaptable to any type of widespread illness and aims to increase recovery rates and decrease mortality rates, as confirmed by the case study results.

Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a ”depot-customer-depot” paradigm. Thus, optimally locating multiple depots is critical to achieving operational efficiency and flexibility. Traditional location models, which rely on circular coverage, fail to capture the actual reachable area for UAV round-trips between multiple depots within a given endurance range. This drawback restricts deployment flexibility or results in excessive redundancy, even making it impractical. To address this limitation, we introduce an ellipse-based locating method for flexible UAV deployment, inspired by UAV reachability and process flexibility in manufacturing. This approach attempts to optimize the redundancy of multi-depot coverage for demand points to achieve a better balance between deployment flexibility and resource requirements. To tackle the model’s computational challenge, we present an improved Benders decomposition algorithm that speeds up the solution process by analytically addressing subproblems and implementing dominance rules to manage the master problem’s size. Simulations show that the proposed model greatly improves the ability to handle uncertainties by incorporating slight redundancy in emergency resources, and the fulfillment rate of demand fluctuations is increased by 5%–20%, which shows the superiority of enhancing the mobility and flexibility of UAV deployment.

The logistics industry is at the heart of socio-economic development. Disruption in the logistics and supply chain industry grounded many sectors to a halt at the height of COVID-19 with economic and environmental implications. This study examines the efficiency and interconnections among innovation, energy, infrastructure, and logistics performance as the logistics industry strives to attain a green supply chain while maintaining economic growth and environmental sustainability. To capture these relationships, a network data envelopment analysis model is designed for efficiency analysis. 36 % of the evaluated countries had below-average efficiency. Results also indicate innovation and energy sustainability as drivers of efficiency and sustainability in the logistics industry. This implies the need for innovation in logistics energy infrastructure by decarbonizing the logistics industry. Regional and national partnerships in the technological development of green energy to power the logistics industry are recommended.

The flux of economic policy uncertainty casts a long shadow over the ecological footprint, amplifying the risk and the urgency for strategic planning that harmonizes economic stability with environmental stewardship. This complex interplay necessitates a reassessment of how economic strategies can be sculpted to mitigate environmental risks and foster sustainability. This research evaluates the asymmetric impact of economic policy uncertainty on the ecological footprints in the top 10 polluted nations (China, USA, India, Russia, Japan, Brazil, Germany, Indonesia, France, and Mexico) from 2001 to 2022. Prior studies relied on panel data methods without accounting for the unique features of individual economies. Conversely, the present investigation implements the Quantile-on-Quantile methodology to delve into the interconnection between the variables within each country. This sophisticated methodology enriches the exactness of our evaluation, providing a thorough worldwide standpoint and uncovering precise insights tailored to the distinctive attributes of each particular country. The outcomes disclose that economic policy uncertainties reduce ecological footprints across diverse quantiles in the selected economies. Additionally, the findings underscore multiple behaviors in these correlations across several quantiles. These results highlight the need for policymakers to take extensive measures to address the deviating impacts of economic policy uncertainty on ecological sustainability.

To improve the efficiency and viability of healthcare organizations, it is crucial to enhance both the quality of patient care and the well-being of healthcare staff, particularly nurses. They play a vital role as a key human resource in healthcare, and their well-being and job satisfaction are significantly influenced by effective scheduling. Improved scheduling practices not only enhance the quality of care provided but also contribute to better job satisfaction and overall well-being of the nursing staff. Despite the importance of this task, nurse scheduling is often conducted manually by head nurses, which is both challenging and time-consuming. This study proposes a mathematical model that addresses the nurse scheduling problem, with a specific application in a French hospital department. The objectives of the model are to accommodate a wide range of constraints flexibly, ensure balanced workloads and shifts, and align with the preferences of the nursing staff. A significant contribution of this research is the theoretical investigation and comparison of various workload balancing criteria tailored to the specific context of the problem. The study analyzes different workload balancing criteria to formulate an efficient scheduling solution. The effectiveness of the proposed approach is demonstrated through large experiments and practice feedback, confirming its potential to enhance both healthcare efficiency and nurses’ well-being.

Performance measurement of decision-making units (DMUs) with network structure is one of the main challenges in data envelopment analysis (DEA) field. The main purpose of this paper is to propose a novel network data envelopment analysis (NDEA) approach based on matrix of efficiency, common set of weights (CSW), multi-objective programming (MOP), and goal programming (GP) technique for performance measurement of peer DMUs in two-stage network structure. The advantages of the proposed NDEA approach can be summarized as follows: comparing all DMUs and sub-DMUs on the same base, considering all internal structures and relations and capability to extending this for all network structures, linearity of the proposed models, unique efficiency decomposing without any need to consider multiplicative, additive or leader-follower relations between overall and stages efficiency. To illustrate the usefulness and applicability of the proposed approach we applied it to a real application of non-life insurance companies in Taiwan.

Credit scoring models for non-traditional lending channels, such as peer-to-peer (P2P) lending platforms, are usually estimated only on the sample of accepted applicants. This may lead to biased estimates of the risk drivers. This issue can be addressed using a reject inference technique that includes the characteristics of rejected applicants in the model. Due to the low numbers of accepted applicants and default records, credit scoring models usually face a class imbalance problem. However, previous literature on sample selection models for credit scoring does not address the class imbalance issue. To fill this gap, we extend the Generalised Extreme Value (GEV) regression model for binary data to the sample selection framework. We consider the quantile function of the GEV distribution as a link function in both the selection and outcome equations. We use the copula function to model the dependence structure between the two equations for its flexibility. This proposal is called the Sample Selection Generalised Extreme Value (SSGEV) model and it is implemented in the R package BivGEV. We apply this model to a comprehensive dataset provided by Lending Club, and we show that parameter estimates obtained only on accepted P2P applicants are biased and coherently with the literature. The SSGEV model achieves a higher predictive accuracy than those obtained using univariate approaches or a sample selection probit model. Our proposal also provides more conservative estimates of the Value-at-Risk and the Expected Shortfall.

We propose a composite indicator for the assessment of tourism sustainability in European Union (EU) countries relatively to years 2019 and 2021. Our proposal is a revisitation of the Travel & Tourism Development Index (TTDI), which is based on a uniform weighting scheme, by means of the Multi-Directional Benefit of the Doubt (MD-BoD) model, an innovative non-compensatory method able to derive a country-specific preference structure from the data. Thanks to the MD-BoD model, each pillar and elementary indicator receives a score that reflects the potential improvement of the country relatively to an empirical benchmark, therefore it is possible to objectively identify weak and strong dimensions of tourism sustainability before and after the Covid-19 pandemic. As such, our composite indicator represents a valuable support for the design of policies aimed at increasing the resilience of the European tourism ecosystem.

This study analyzes the impact of Environmental, Social, and Governance (ESG) risk metrics on the financial performance of ETFs in the US and Europe from January 2020 to December 2023, particularly during the COVID-19 pandemic. It explores the influence of ESG investment trends on ETF performance and investor preferences between equities and bonds in response to ESG risk profiles. Results show a positive correlation between higher ESG standards and financial performance, measured by the Sharpe ratio, with a marked preference for bonds amidst increased ESG risks. This highlights the critical role of ESG considerations in investment strategies and risk management, providing insights for sustainable finance.