Determining optimal fuel delivery strategies under uncertainty

During the preparation before a hurricane makes landfall, affected individuals may be asked to evacuate. Large and small-scale evacuations can cause rapid increases in the demand for gasoline fuel. However, during a hurricane vessels carrying gas may be delayed and/or rerouted, adding to the difficulty of providing the necessary gas in affected areas. In this work, we determine alternate delivery locations and times for vessels carrying fuel that are scheduled to arrive and deliver fuel at ports impacted by an approaching hurricane. Motivated by Hurricane Irma in Florida, we develop a multi-period stochastic scheduling model that incorporates hurricane (weather) advisories, fuel delivery schedules, port storage capacities, and port docking capacities. Our model determines the best schedule based on two objectives: (1) minimize the total unmet demand at each port, and (2) minimize inequities in unmet demands among the ports. We also present a case study and a numerical experiment based on fuel delivery data from ports in Florida. Among our key findings is that port availability is the driving factor in determining feasible schedules for vessel gas deliveries. We also present a scheduling heuristic that dynamically adapts to weather advisories so as to minimize the impact of unmet demand in the affected areas.     

Public debt management and private financial development

In several developing countries, high and rising public debt is an important source of vulnerability. Strengthening debt management is a priority, but its effects on domestic economies have been hardly analyzed. This paper asks whether better public debt management could have spillover effects on the private sector, leading to more (and more stable) private capital flows and domestic credit. This is a relevant question in a context of financial deepening and increasing private capital inflows, which could be prone to episodes of bonanza, sudden stops and crises. Our results, based on a sample of developing countries, show positive spillover effects from better public debt management to private capital inflows and domestic financial deepening.     

Video-based pedestrian grouping model considering long-span space in a big hall

Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.     

Social capital as a facilitator of successful buyer-supplier performance management

Performance management in supply chains is an increasingly important, rapidly developing but challenging area. It may benefit from sufficient levels of social capital, such as frequent interactions between buyers and suppliers. However, social capital has not yet been studied as a facilitator of performance management. To bridge this knowledge gap, we examine buyer-supplier performance management and social relationship characteristics as perceived by suppliers. We analyze empirical data gathered through a survey of 482 suppliers by employing PLS-SEM and PLS-MGA. The findings elaborate on the role of cognitive, relational, and structural capital in performance management activities within a buyer-supplier relationship. Moreover, the results demonstrate how a supplier's positive perception, in the form of preferred customer status, can be facilitated through performance management activities. The findings reveal that there is a difference in the role of structural and relational capital between key and non-key supplier groups in performance management activities. We conclude that successful performance management in buyer-supplier relationships requires more than simply a system for sharing information. The mature use of performance measurement is also essential, with cognitive capital being especially beneficial in this endeavor.     

Environmental,Social and Governance investing: Does rating matter?

In the last decade, the demand for sustainable and social investments has improved. The mutual funds industry has responded to market needs by offering a number of investment products focused on Environmental, Social and Governance (ESG) companies. The aim of this article is to understand if an ESG score can actually be considered a valid criterion that portfolio managers could adopt, along with traditional risk–return optimisation, in selecting asset portfolios. The paper analyses the link between the performance and the ESG score of different sectoral portfolios (one for each sector of the Global Industry Classification Standard), entirely composed of ESG assets, in the search for a clear and strong positive correlation that could suggest an overall advantage to focus on an ex ante choice of assets with high ESG scores.     

Dynamism and realignment in the HR architecture: Online labor platform ecosystems and the key role of contractors

Given the widespread contribution of independent contractors to organizational innovation and competitive advantage, it is timely to reassess assumptions about the HRM practices appropriate to their management and the rationale for organizations to work with them. In the original and highly influential HR architecture model of Lepak and Snell (1999), contractor status is viewed as an outcome of the low value and/or low uniqueness of human capital resulting in the proposition to externalize and manage them using either none or minimal compliance-based HRM practices. Developments in digital technologies and algorithmic management epitomized by online labor platforms prompt us to reconsider these assumptions and to challenge the proposed links between value/uniqueness of human capital, employment mode and HRM practices that are assumed by the HR architecture model. Using insights from online labor platforms, we argue that the significant benefits to firms of working with contractors, coupled with the possibilities offered by algorithmic management to efficiently monitor and regulate their behavior, provide a compelling reason for organizations to choose external employment modes even when workers are key to value creation. We challenge the alignment and stability of the relationships proposed by the HR architecture model, and offer propositions to extend the model by reconsidering the rationale for, and nature of, HRM practices associated with contractors. This reassessment is both timely and relevant given the growing prominence of business models where externalizing workers is central alongside the development of new forms of algorithmic human resource management to control them.     

Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques

Emergency Departments (EDs) can better manage activities and resources and anticipate overcrowding through accurate estimations of waiting times. However, the complex nature of EDs imposes a challenge on waiting time prediction. In this paper, we test various machine learning techniques, using predictive analytics, applied to two large datasets from real EDs. We evaluate the predictive ability of Lasso, Random Forest, Support Vector Regression, Artificial Neural Network, and the Ensemble Method, using different error metrics and computational times. To improve the prediction accuracy, new queue-based variables, that capture the current state of the ED, are defined as additional predictors. The results show that the Ensemble Method is the most effective at predicting waiting times. In terms of both accuracy and computational efficiency, Random Forest is a reasonable trade-off. The results have significant practical implications for EDs and hospitals, suggesting that a real-time performance monitoring system that supports operational decision-making is possible.     

Information provision and consumer search behavior for products with asymmetric uncertainty

When selling multiple products with asymmetric uncertainty, should the seller disclose product information so that customers do not have to incur any cost to resolve their uncertainties; if so, which product should the seller choose? To address these questions, we consider a monopolist selling two substitutable products to a group of consumers. Each consumer has asymmetric uncertainty regarding the two products. A total of four different information provision structures are considered based on whether the seller discloses information about each product with the aim of determining which strategy provides the seller with the greatest revenue. We derive several interesting results. First, the optimal information provision strategy depends on the magnitude of uncertainty in relation to the product with lower uncertainty. Specifically, if the uncertainty regarding the product with lower uncertainty is sufficiently small, it is optimal for the seller to provide information about the product with higher uncertainty, otherwise, the seller should provide information about both products. Second, when only one product's information should be revealed, it is optimal for the seller to choose the product with higher uncertainty and charge a higher price. Third, withholding information on both products is never optimal for the seller. Finally, our main model is extended by examining the Mean-Preserving Spread setting, and the robustness of our main results is confirmed. Furthermore, we examine the situation in which a monopolist sells a single product with two main attributes. We find that each of the four information provision strategies can be optimal under various scenarios.     

构建基于和谐劳动关系的我国人力资源管理新体系

和谐劳动关系作为社会关系和企业内部最基础、最广泛、最重要的关系,对社会和企业的和谐发展起着至关重要的作用。本文从转型时期我国劳动关系的特殊性角度出发,探讨了我国人力资源管理学科发展的特殊历程,依据和谐社会的必然要求和企业管理的目的与宗旨,提出了构建基于和谐劳动关系的我国人力资源管理新体系的总体思路,以期对我国人力资源管理的巨大发展有促进作用。     

房产价格持续攀升的原因和价格监督机制的构建

本文对我国近十年来房产价格的总体水平呈现出持续攀升的原因和房产价格监督管理立法的现状进行了全面、深入地分析．指出了我国房产价格监督管理实践存在的问题,提出了通过构楚房产市场竞争机制和房产价格宏观调控机制来完善我国房产价格监督管理机制的构想。