Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction

Nursing managers are faced with rising turnover and shortages of qualified nursing staff. At the same time they are under increased pressure to simultaneously increase patient care and satisfaction while reducing costs. In this study, we examine the impact of centralizing scheduling decisions across departments in a hospital. By pooling nurses from multiple units and scheduling them in one model, improved costs and reduced overtime result. Reduced overtime improves schedules for nurses. Improved satisfaction levels can positively impact turnover rates among nurses. Our results show that by using a centralized model, nursing managers in hospitals can improve the desirability of nurse schedules by approximately 34% and reduce overtime by approximately 80% while simultaneously reducing costs by just under 11%.     

Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction

Nursing managers are faced with rising turnover and shortages of qualified nursing staff. At the same time they are under increased pressure to simultaneously increase patient care and satisfaction while reducing costs. In this study, we examine the impact of centralizing scheduling decisions across departments in a hospital. By pooling nurses from multiple units and scheduling them in one model, improved costs and reduced overtime result. Reduced overtime improves schedules for nurses. Improved satisfaction levels can positively impact turnover rates among nurses. Our results show that by using a centralized model, nursing managers in hospitals can improve the desirability of nurse schedules by approximately 34% and reduce overtime by approximately 80% while simultaneously reducing costs by just under 11%.     

Risk Preferences,Probability Weighting,and Strategy Tradeoffs in Wildfire Management

Wildfires present a complex applied risk management environment, but relatively little attention has been paid to behavioral and cognitive responses to risk among public agency wildfire managers. This study investigates responses to risk, including probability weighting and risk aversion, in a wildfire management context using a survey‐based experiment administered to federal wildfire managers. Respondents were presented with a multiattribute lottery‐choice experiment where each lottery is defined by three outcome attributes: expenditures for fire suppression, damage to private property, and exposure of firefighters to the risk of aviation‐related fatalities. Respondents choose one of two strategies, each of which includes “good” (low cost/low damage) and “bad” (high cost/high damage) outcomes that occur with varying probabilities. The choice task also incorporates an information framing experiment to test whether information about fatality risk to firefighters alters managers' responses to risk. Results suggest that managers exhibit risk aversion and nonlinear probability weighting, which can result in choices that do not minimize expected expenditures, property damage, or firefighter exposure. Information framing tends to result in choices that reduce the risk of aviation fatalities, but exacerbates nonlinear probability weighting.     

Bi‐Criteria Scheduling of Surgical Services for an Outpatient Procedure Center

Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time‐setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi‐criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi‐criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade‐off between patient and health care provider‐based criteria. Finally, we summarize several important managerial insights based on our findings.     

经营者组合激励中非物质激励的价值分析

本文在经营者组合激励的基础上,以经营者为风险厌恶者和经营者物质激励的组成为外生变量,分析经营者非物质激励的价值与经营者物质激励的各组成部分、经营者的风险厌恶度的关系,具有较大的理论价值和应用价值。     

Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance

The problem of no‐shows (patients who do not arrive for scheduled appointments) is particularly significant for health care clinics, with reported no‐show rates varying widely from 3% to 80%. No‐shows reduce revenues and provider productivity, increase costs, and limit patient access by reducing effective clinic capacity. In this article, we construct a flexible appointment scheduling model to mitigate the detrimental effects of patient no‐shows, and develop a fast and effective solution procedure that constructs near‐optimal overbooked appointment schedules that balance the benefits of serving additional patients with the potential costs of patient waiting and clinic overtime. Computational results demonstrate the efficacy of our model and solution procedure, and connect our work to prior research in health care appointment scheduling.     

A Model of Consumer Inertia with Applications to Dynamic Pricing

This paper introduces a decision model of consumer inertia. Consumers exhibit inertia when they have an inherent bias to delay purchases. Inertia may induce consumers to wait even when it is optimal to buy immediately. We embed our decision model within a dynamic pricing context. There is a firm that sells a fixed capacity over two time periods to an uncertain number of both rational and inertial consumers. We find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product (in period two). We show that our model of inertia is consistent with well‐established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences. We offer practical recommendations for firms to influence the level of consumer inertia. These include offering returns policies (to mitigate potential consumer losses), providing decision aids (to avoid perception errors), and offering flexible payment options (to lower transaction costs).     

可加效用下基于消费者惰性的易逝品动态定价

本文在考虑销售商的风险规避态度和消费者惰性的情况下，研究了易逝品的动态定价策略。首先给出风险规避的销售商实施动态定价的马尔可夫决策过程，并运用MNL随机效用模型来刻画惰性消费者的购买决策，在此基础上，以风险规避销售商的期望效用最大化为目标，利用动态规划方法建立了可加效用下的易逝品动态定价模型，并探讨了消费者的惰性行为和销售商的风险规避态度对最优价格的影响。结论表明：在销售商的风险态度为风险规避的情况下，考虑消费者惰性行为的最优价格随库存水平的增加而降低，随剩余销售时间的增加而提高，消费者的惰性行为和销售商的风险规避态度对最优价格都存在负向影响，即最优价格随惰性深度的增加而降低，随惰性宽度的增加而降低，同时，销售商的风险规避态度越强烈，其制定的产品价格越低。     

考虑作业空间干涉的工程项目时间-成本均衡优化研究

工程现场的空间资源是制约工程活动开展的重要影响因素。本文研究考虑空间干涉的工程调度优化问题,定义作业空间干涉的度量方式和作业效率函数,建立工程调度的工期-成本双目标优化模型。针对问题特征设计相对延迟编码方式和解码机制,采用NSGA-II算法求解模型。以某工程案例为研究对象,通过与传统方法对比实验验证了模型和算法的有效性,接着分析了算法的最大延迟时间参数对算法性能的影响。实验结果表明,本文提出的模型和算法能有效提升工程进度和成本目标。     

市场层面上的赌资效应研究

赌资效应(House Money Effect)是指前期收益会使投资者变得更加风险寻求。与以往基于心理学实验或投资者个人交易账户数据所进行的实证研究不同,本文以股票市场整体行为为研究对象,采用世界上具有代表性的十四支股票综合指数为样本,构建TVRA-GARCH-M模型来研究在市场层面上前期损益对当期风险态度的影响。实证研究发现,在股票市场整体行为上,前期收益会降低当期的风险规避程度;同时,前期损失会提高当期的风险规避程度。     

Appointment Scheduling with No‐Shows and Overbooking

We study an overbooking model for scheduling arrivals at a medical facility under no‐show behavior, with patients having different no‐show probabilities and different weights. The scheduler has to assign the patients to time slots in such a way that she minimizes the expected weighted sum of the patients' waiting times and the doctor's idle time and overtime. We first consider the static problem, where the set of patients to be scheduled and their characteristics are known in advance. We partially characterize the optimal schedule and introduce a new sequencing rule that schedules patients according to a single index that is a function of their characteristics. Then we apply our theoretical results and conclusions from numerical experiments to sequential scheduling procedures. We propose a heuristic solution to the sequential scheduling problem, where requests for appointments come in gradually over time and the scheduler has to assign each patient to one of the remaining slots that are available in the schedule for a given day. We find that the no‐show rate and patients' heterogeneity have a significant impact on the optimal schedule and should be taken under consideration.     

Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.     

Equity, Efficiency, Uncertainty, and the Mitigation of Global Climate Change

Policies to mitigate potential damages from global climate change impose costs on the current generation to provide benefits to future generations. This article examines how comparisons among three stylized policies-business-as-usual, mitigation of climate change, and compensation for climate damages-depend on social preferences with respect to risk and intertemporal equity. Also examined is the opportunity-cost criterion, which asserts that mitigation should not be chosen if its net present value is smaller than that of business-as-usual. Analysis reveals that the discount factor used to evaluate whether mitigation satisfies this criterion depends on preferences regarding risk and intertemporal inequality of consumption, and on the risk of the compensation policy. Risk aversion favors mitigation over business-as-usual. If society is neutral to inequality, risk aversion disfavors compensation, but if society is inequality averse, the effect of risk aversion on preferences between compensation and business-as-usual is ambiguous. Inequality aversion tends to favor business-as-usual over both alternative policies provided that, roughly speaking, the anticipated future improvements in welfare exceed the anticipated climate damages.     

两种订货模式下订购和促销联合优化策略

本文基于CVaR决策准则,通过构建需求依赖于促销的一般需求模型,其中包括加法和乘法需求模型作为特例,考察了一次订货和允许紧急订货两种模式下风险厌恶零售商关于促销和库存的联合优化问题。讨论了紧急订购成本、风险厌恶以及市场需求变动对最优策略的影响,并对两种模式下零售商的最优策略和收益进行了比较分析,结果表明:在两种订货模式下,零售商的最优订购量和促销努力均随风险厌恶程度的增大而降低;紧急订货模式下的促销努力和实现的收益大于一次订货模式,且在加法需求模型下,紧急订货模式下的初次订购量小于一次订货模式的最优订购量。运用随机变量一阶和二阶交替随机占优的概念刻画了市场需求变动下零售商如何调整最优策略的充分条件。最后实施数值实验验证了理论分析结果。     

销售成本信息不对称下供应商风险规避的回购契约

在价格随机条件下,销售成本信息不对称且供应商规避风险时,本文探讨回购契约协调供应链的最优决策。在前提假设的基础上构建新的回购契约模型,求解并用算例进行仿真验证,考虑信息不对称与风险规避共同发生耦合作用后对供应链相关决策变量的影响。研究结果表明：在价格随机条件下,不管信息是否对称,只要供应商有风险规避意识,供应链相关决策变量均发生分岔突变;不管市场价格是否随机,也不管供应商是否风险规避,只要零售商隐瞒私人销售成本信息,就会给自己带来额外的收益,但会给供应商与整个供应链带来损害;供应链上的信息越不对称,在分岔突变区域,相关决策变量的振荡幅度越大。分岔突变现象是市场价格随机和供应商风险规避耦合作用后特有的现象;零售商能够利用信息不对称给自己带来额外的好处,但会损害供应商和供应链的利益;供应商防范零售商这种损人利己行为的最好对策,就是通过设计一种合作机制,以最低成本的方式来促使零售商将销售成本信息公开化;另外,供应商以平稳的心态(风险中性)应对外部风险,更有利于提高其自身决策的水平。     

Why do users trust algorithms? A review and conceptualization of initial trust and trust over time

Algorithms are increasingly playing a pivotal role in organizations' day-to-day operations; however, a general distrust of artificial intelligence-based algorithms and automated processes persists. This aversion to algorithms raises questions about the drivers that lead managers to trust or reject their use. This conceptual paper aims to provide an integrated review of how users experience the encounter with AI-based algorithms over time. This is important for two reasons: first, their functional activities change over the course of time through machine learning; and second, users' trust develops with their level of knowledge of a particular algorithm. Based on our review, we propose an integrative framework to explain how users’ perceptions of trust change over time. This framework extends current understandings of trust in AI-based algorithms in two areas: First, it distinguishes between the formation of initial trust and trust over time in AI-based algorithms, and specifies the determinants of trust in each phase. Second, it links the transition between initial trust in AI-based algorithms and trust over time to representations of the technology as either human-like or system-like. Finally, it considers the additional determinants that intervene during this transition phase.     

Predicting Cancer‐Prevention Behavior: Disentangling the Effects of Risk Aversion and Risk Perceptions

Experimental and survey research spanning the last two decades concludes that people who are more risk tolerant are more likely to engage in risky health activities such as smoking and heavy alcohol consumption, and are more likely to be obese. Subjective perceptions of the risk associated with different activities have also been found to be associated with health behaviors. While there are numerous studies that link risk perceptions with risky behavior, it is notable that none of these controls for risk aversion. Similarly, studies that control for risk aversion fail to control for risk misperceptions. We use a survey of 474 men and women to investigate the influence of risk aversion, risk misperceptions, and cognitive ability on the choice to engage in behaviors that either increase or mitigate cancer risk. We measure optimism in two dimensions: baseline optimists are those who inaccurately believe their cancer risk to be below its expert‐assessed level, while control optimists are those who believe they can reduce their risk of cancer (by changing their lifestyle choices) to a greater extent than is actually the case. Our results indicate that baseline optimism is significantly and negatively correlated with subjects′ tendencies to engage in cancer‐risk‐reducing behaviors, and positively correlated with risky behaviors. Subjects’ control misperceptions also appear to play a role in their tendency to engage in risky and prevention behaviors. When controlling for both of these types of risk misperception, risk aversion plays a much smaller role in determining health behaviors than found in past studies.     

时变风险厌恶下的期权定价——基于上证50ETF期权的实证研究

传统的随机波动率（SV）期权定价是在投资者具有常数风险偏好假设下进行的.但近年来越来越多的研究表明,市场参与者具有时变风险厌恶特征.基于此,本文对时变风险厌恶条件下的期权定价问题进行深入研究.首先,对传统的（非仿射）常数风险厌恶SV（CRA-SV）期权定价模型进行扩展,构建时变风险厌恶SV（TVRA-SV）期权定价模型对期权进行定价,并分析时变风险厌恶对期权价格的影响;其次,采用标的资产与期权数据信息,建立基于连续粒子滤波的极大似然估计方法,对定价模型的客观与风险中性参数进行联合估计;最后,采用我国期权市场上的上证50ETF期权数据,对构建的定价模型进行实证检验.结果表明：TVRA-SV期权定价模型相比传统的CRA-SV期权定价模型具有更好的数据拟合效果,能够更充分地刻画标的上证50ETF收益率在客观与风险中性测度下的波动性;TVRA-SV期权定价模型相比传统的Black-Scholes（B-S）期权定价模型和CRA-SV期权定价模型都具有明显更高的定价精确性。     

Consistency of Higher Order Risk Preferences

Risk aversion (a second‐order risk preference) is a time‐proven concept in economic models of choice under risk. More recently, the higher order risk preferences of prudence (third‐order) and temperance (fourth‐order) also have been shown to be quite important. While a majority of the population seems to exhibit both risk aversion and these higher order risk preferences, a significant minority does not. We show how both risk‐averse and risk‐loving behaviors might be generated by a simple type of basic lottery preference for either (1) combining “good” outcomes with “bad” ones, or (2) combining “good with good” and “bad with bad,” respectively. We further show that this dichotomy is fairly robust at explaining higher order risk attitudes in the laboratory. In addition to our own experimental evidence, we take a second look at the extant laboratory experiments that measure higher order risk preferences and we find a fair amount of support for this dichotomy. Our own experiment also is the first to look beyond fourth‐order risk preferences, and we examine risk attitudes at even higher orders.     

How Software Project Risk Affects Project Performance: An Investigation of the Dimensions of Risk and an Exploratory Model*

To reduce the high failure rate of software projects, managers need better tools to assess and manage software project risk. In order to create such tools, however, information systems researchers must first develop a better understanding of the dimensions of software project risk and how they can affect project performance. Progress in this area has been hindered by: (1) a lack of validated instruments for measuring software project risk that tap into the dimensions of risk that are seen as important by software project managers, and (2) a lack of theory to explain the linkages between various dimensions of software project risk and project performance. In this study, six dimensions of software project risk were identified and reliable and valid measures were developed for each. Guided by sociotechnical systems theory, an exploratory model was developed and tested. The results show that social subsystem risk influences technical subsystem risk, which, in turn, influences the level of project management risk, and ultimately, project performance. The implications of these findings for research and practice are discussed.