基于异质性顾客心理期望等待时间的优先权服务定价研究

本文以非抢占式M/M/1排队系统为背景,以企业收益最大化为目标,基于顾客异质性(单位时间等待成本不同)将顾客分为两类,针对顾客的心理期望等待时间对服务提供商最优定价策略的影响进行研究。首先研究优先权顾客心理期望等待时间对企业收益的影响以及相应的优先权定价,然后研究优先权顾客和普通顾客同时存在心理期望等待时间对企业收益的影响和相应的优先权定价。研究表明:仅考虑优先权顾客的心理期望等待时间,企业应通过提高优先权定价来获得最优收益;当优先权顾客和普通顾客同时存在心理期望等待时间时,企业仍然采取提高优先权定价的策略,若普通顾客的价值大(获取服务的基本费用大),企业应对普通顾客提供一定的折扣来消除其心理期望等待时间增加企业收益;如果普通顾客的价值较小,企业应"有意"流失部分普通顾客,吸引更多顾客到优先权队列获取服务来获得更多收益。本文研究对于服务提供商在考虑顾客心理期望等待时间基础上设置最合理的队列机制有一定的指导意义和实际应用价值。     

基于顾客心理期望等待时间的服务策略研究

顾客在排队系统获取服务时，会存在心理上的期望等待时间，该期望会影响顾客在排队系统中的行为变化和流动，从而影响企业收益。本文以传统的M/M/1排队系统为背景，基于顾客存在期望等待时间的前提下，以企业收益最大化为优化目标进行研究。首先，对相应基础理论和模型假设进行介绍；其次，对顾客存在心理期望等待时间情形提出三种新的策略：重新定价、通过折扣对顾客期望值进行调整、提高服务率；然后，分别对上述三种策略进行优化分析，并同现有结果进行比较；研究表明：三种策略都比维持原有定价带来更大收益；当折扣力度较小时或顾客对费用感知强于时间感知时，折扣策略优于重新定价策略；当折扣力度较大或顾客对时间感知强于费用感知时，重新定价策略优于折扣策略；最后，通过对最优结果分析提出相应管理启示。本文的研究对于顾客存在心理期望等待时间的服务定价具有重要的指导意义和实际应用价值。     

基于“云排队”的经济效益分析

随着各种云智能排队预约系统的推出，顾客“排队难”的问题得到了极大的缓解。其简便的操作方式，受到了大量商家与顾客的好评。本文基于当下热门服务行业的运营模式，建立排队博弈模型，从理论上分析了“云排队”平台对顾客以及服务商的影响。本文主要得到了以下几个结果：(1) 给出了两类顾客的纳什均衡策略(包括进队策略与最优到达间隔)；(2) 发现当前的预约机制能够提高“线上”顾客的效用，并刺激更多的线上顾客购买该产品，但同时也降低了“线下”顾客的进队意愿；(3) 发现在云排队机制下服务商的收益将大大提高，同时该机制将刺激服务商收取更高的服务费用。     

A NOTE ON RENEGING DECISIONS

This note is an extension of the approach to the problem of reneging introduced in Parkan and Warren [1]. It is assumed customers consider joining an M/M/1 queuing system with a prior gamma distribution over the values of the mean service time. Thus, each customer has an initial estimate of his total waiting time in the system. The customers associate the same sunk value with the waiting time and obtain the same reward at service completion. Having joined a system each customer may consider reneging in view of his revision of his initial service time estimate based on service observations. The bounds on the stationary state probabilities for such a system are obtained and examples are provided to compare the cases with and without reneging.     

Capacity rationing in rental systems with two customer classes and batch arrivals

Service differentiation is an emerging method to improve profit and to better serve high-priority customers. Such an approach has recently been introduced by one of Europe's leading rail cargo companies. Under this approach, customers can choose between classic and premium services. Premium service is priced above classic service and premium customers receive a service guarantee which classic customers do not receive. The company has to decide under which conditions it should ration its fleet capacity to classic customers in order to increase service of premium customers. We model such a situation as a batch-arrival queuing loss system. We describe the model, solve it optimally, and derive quantities of interest such as service probabilities. We further analyze it by performing numerical experiments based on the data from the company that motivated our research. We show that the potential of capacity rationing can be substantial in situations like the one we analyzed. We also derive conditions under which rationing is especially beneficial, such as under high unit fleet holding costs or in the presence of batch arrivals compared to single arrivals.     

Management Policies to Improve the Effectiveness of Multi-Station Service Organizations*

A SLAM based simulation model of a multi-station, tandem queuing structure characteristic of a variety of service systems is employed to test various design options for the system. The model is based on an extensive study of the State of Florida driver licensing offices. The multiple objectives of low time in the system for customers and the efficient use of personnel resources are employed to measure the benefits of policy options. The use of simulation analysis permits the incorporation of complex system characteristics, therefore providing a realistic representation of the effects of possible management actions. Effective methods to control labor in such systems are suggested.     

An Analysis of the Production Line versus the Case Manager Approach to Information Intensive Services*

The production line approach has been beneficially used in manufacturing companies to yield consistent-quality standard products at relatively low cost. For this reason, service firms have also employed this approach, which embodies a division of labor, sequential flow processes, and standardization of services. Today's customers, however, are demanding customized services and shorter cycle times—outcomes that the production line approach finds difficult to achieve. An alternative process design proposed for information intensive services is the emerging “case manager” approach, in which all the steps required to deliver a service are performed by a single information technology-enabled individual. This paper uses queuing theory to compare the waiting time performance of the production line and the case manager approaches. This comparison shows that when the number of process steps under the production line approach is relatively large, the relative efficiency of the case manager is sufficiently high, system utilization rate is moderate, and the size of staff downsizing is small, the case manager approach is preferred. Critical issues for the design of enabling information technology are then discussed. The paper concludes with suggestions for future research on the case manager approach.     

Self‐Selecting Priority Queues with Burr Distributed Waiting Costs

Service providers, in the presence of congestion and heterogeneity of customer waiting costs, often introduce a fee‐based premier option using which the customers self‐segment themselves. Examples of this practice are found in health care, amusement parks, government (consular services), and transportation. Using a single‐server queuing system with customer waiting costs modeled as a Burr Distribution, we perform a detailed analysis to (i) determine the conditions (fees, cost structure, etc.) under which this strategy is profitable for the service provider, (ii) quantify the benefits accrued by the premier customers; and (iii) evaluate the resulting impact on the other customers. We show that such self‐selecting priority systems can be pareto‐improving in the sense that they are beneficial to everyone. These benefits are larger when the variance in the customer waiting costs is high and the system utilization is high. We use income data from the poorest and richest areas (identified by zipcode) in the United States along with the countrywide income distribution to illustrate our results. Numerical results indicate that planning for a 20–40% enrollment in the high‐priority option is robust in ensuring that all the stakeholders benefit from the proposed strategy.     

基于系统柔性的MTS-MTO混合生产决策

研究生产商采用MTS、MTO混合作业的方式为不同客户提供产品和服务的策略。计划利用一组可灵活控制的动态设备处理那些不同需求的MTS和MTO生产业务,为此,我们开发了一个多服务台的排队模型,利用拟生灭过程和相位型分布得到了MTS、MTO排队系统平衡条件和稳态概率矩阵几何解。通过求解分块矩阵方程组,给出了系统队列长度、平均等待队长、顾客服务水平等绩效测度指标。建立了系统运作成本最优化的数学模型,采用搜索算法,确定了关键参数的边界值,找到了混合系统运作的最优策略。数值模拟和系统绩效比较分析结果显示：（1）动态切换策略能够更快速的帮助MTS恢复目标库存量,控制系统缺货风险,降低库存持有成本;（2）找到了满足顾客服务水平的最少的设备配置数量和库存成本最低的生产切换时间,且动态系统的平均队列长度低于静态系统;（3）混合运作策略减少了约2/3的静态系统平均队列长度,企业在队列长度减小的窗口期内可以接受更多订单和缩短MTO订单交货时间。     

基于患者两阶段医疗服务过程的病床资源优化

随着医疗需求的日益增长，供需不平衡的矛盾日益显著，病床资源短缺已经成为制约医疗服务发展的重要因素之一。针对病床资源供需失衡的问题，本文构建患者两阶段医疗服务过程的病床资源优化模型。考虑患者的医疗服务是一个两阶段串联排队过程，第一阶段是强制治疗阶段，第二阶段是康复阶段。在患者排队系统中，服务台总数固定的前提下，本文利用增补变量方法给出了该排队系统的概率等式，通过求解偏微分方程得出系统的状态概率，并推导出医院拒绝患者的概率、平均康复时间等相关指标。考虑部分患者因病床资源不足而无法入院和康复阶段患者提前出院两种风险因素，本文基于排队指标构建多病种间的病床分配模型，再利用动态规划的求解思路得到病床分配的最优解。最后，本文采用2017年卫生统计数据和模拟数据相结合进行数值分析。研究结果表明与传统单阶段模型相比，两阶段模型对不同病种间病床数差别影响显著，并且患者入院率对病床分配影响较大。进一步，基于数值结果给出了医疗管理建议和研究展望。     

Modeling and Managing the Percentage of Satisfied Customers in Hidden and Revealed Waiting Line Systems

We perform an analysis of various queueing systems with an emphasis on estimating a single performance metric. This metric is defined to be the percentage of customers whose actual waiting time was less than their individual waiting time threshold. We label this metric the Percentage of Satisfied Customers (PSC.) This threshold is a reflection of the customers' expectation of a reasonable waiting time in the system given its current state. Cases in which no system state information is available to the customer are referred to as “hidden queues.” For such systems, the waiting time threshold is independent of the length of the waiting line, and it is randomly drawn from a distribution of threshold values for the customer population. The literature generally assumes that such thresholds are exponentially distributed. For these cases, we derive closed form expressions for our performance metric for a variety of possible service time distributions. We also relax this assumption for cases where service times are exponential and derive closed form results for a large class of threshold distributions. We analyze such queues for both single and multi‐server systems. We refer to cases in which customers may observe the length of the line as “revealed” queues.“ We perform a parallel analysis for both single and multi‐server revealed queues. The chief distinction is that for these cases, customers may develop threshold values that are dependent upon the number of customers in the system upon their arrival. The new perspective this paper brings to the modeling of the performance of waiting line systems allows us to rethink and suggest ways to enhance the effectiveness of various managerial options for improving the service quality and customer satisfaction of waiting line systems. We conclude with many useful insights on ways to improve customer satisfaction in waiting line situations that follow directly from our analysis.     

The Critical Few Minutes in Scheduling Time‐Varying Queuing Systems

For nonstationary queuing systems where demand varies over time, an important practical issue is scheduling the number of servers to be available at various times of the day. Widely used scheduling procedures typically involve adding servers at natural time points (e.g., on the hour or at half past the hour) during peak demand periods. Scheduling is often complicated by restrictions on the minimum amount of time (human) servers must work, the earliest (or latest) time a server is available, and limits on the maximum number of servers that can be used at any one time. This paper was motivated by experience with actual queuing systems that embodied such complications. For these systems common scheduling methods that used “natural” starting times for servers resulted in needlessly long customer waits. This research demonstrates that changing the starting times of servers by only a few minutes can have dramatic impacts on customer waiting times for extended periods. In addition, the results highlight the importance of server punctuality.     

Pricing in a Transportation Station with Strategic Customers

We consider a transportation station, where customers arrive according to a Poisson process, observe the delay information and the fee imposed by the administrator and decide whether to use the facility or not. A transportation facility visits the station according to a renewal process and serves all present customers at each visit. We assume that every customer maximizes her individual expected utility and the administrator is a profit maximizer. We model this situation as a two‐stage game among the customers and the administrator, where customer strategies depend on the level of delay information provided by the administrator. We consider three cases distinguished by the level of delay information: observable (the exact waiting time is announced), unobservable (no information is provided) and partially observable (the number of waiting customers is announced). In each case, we explore how the customer reward for service, the unit waiting cost, and the intervisit time distribution parameters affect the customer behavior and the fee imposed by the administrator. We then compare the three cases and show that the customers almost always prefer to know their exact waiting times whereas the administrator prefers to provide either no information or the exact waiting time depending on system parameters.     

Equilibrium Joining Strategies and Optimal Control of a Make‐to‐Stock Queue

We consider a make‐to‐stock, finite‐capacity production system with setup cost and delay‐sensitive customers. To balance the setup and inventory related costs, the production manager adopts a two‐critical‐number control policy, where the production starts when the number of waiting customers reaches a certain level and shuts down when a certain quantity of inventory has accumulated. Once the production is set up, the unit production time follows an exponential distribution. Potential customers arrive according to a Poisson process. Customers are strategic, i.e., they make decisions on whether to stay for the product or to leave without purchase based on their utility values, which depend on the production manager's control decisions. We formulate the problem as a Stackelberg game between the production manager and the customers, where the former is the game leader. We first derive the equilibrium customer purchasing strategy and system performance. We then formulate the expected cost rate function for the production system and present a search algorithm for obtaining the optimal values of the two control variables. We further analyze the characteristics of the optimal solution numerically and compare them with the situation where the customers are non‐strategic.     

Plots of Conditional Wait-Time Intervals Provide Better Information to Queueing System Arrivals

The management of queues is a very important aspect of service operations management. One way to enhance the queueing experience is to provide customers with the most informative and certain estimate of anticipated wait time. This paper develops such a measure—referred to as the conditional wait-time interval (CWTI)—for queueing systems with exponentially distributed service times. It is especially applicable at the operational level of an organization since it utilizes the most current information on the status of the system (number of customers in the queue at arrival). CWTI is also based on two risk factors that consider the chance a customer's actual wait time is outside of the estimated range. This paper also provides a graphical means to display multiple CWTIs.     

Governance and strategy within the financial system: tradition and innovation

Italian banks have undergone an evolutionary process and development of corporate, retail and private banking within the Italian banking system as a response to market pressures exerted by business and private customers for a broadening and qualitative expansion of offerings and organization of available competencies. This not only refers to large enterprises, whose relations with the financial system are autonomous, on equal terms and for some time now have opened up internationally, but above all the large number of SMEs found in Italy’s economic system. Interpreting governance and strategy takes place in a broad perspective in which banks and the financial system have to deal with five significant factors today: regulations, customers, knowledge, capital and synergies. Interaction with these five factors is undoubtedly not only guided by a choice made by shareholders and managers but represents the set of decisions that mitigate ideological factors, choices concerning sustainability and social acceptance of these choices. This paper intends to explore this interaction, drawing on and utilizing the most significant studies in the Italian Banking & Finance sector, with a specific focus on relations between the financial system and businesses, that is to say, the corporate banking area.     

The Impact of Revenue‐Maximizing Priority Pricing on Customer Delay Costs

Speed is an increasingly important determinant of which suppliers will be given customers' business and is defined as the time between when an order is placed by the customer and when the product is delivered, or as the amount of time customers must wait before they receive their desired service. In either case, the speed a customer experiences can be enhanced by giving priority to that particular customer. Such a prioritization scheme will necessarily reduce the speed experienced by lower‐priority customers, but this can lead to a better outcome when different customers place different values on speed. We model a single resource (e.g., a manufacturer) that processes jobs from customers who have heterogeneous waiting costs. We analyze the price that maximizes priority revenue for the resource owner (i.e., supplier, manufacturer) under different assumptions regarding customer behavior. We discover that a revenue‐maximizing supplier facing self‐interested customers (i.e., those that independently minimize their own expected costs) charges a price that also minimizes the expected total delay costs across all customers and that this outcome does not result when customers coordinate to submit priority orders at a level that seeks to minimize their aggregate costs of priority fees and delays. Thus, the customers are better off collectively (as is the supplier) when the supplier and customers act independently in their own best interests. Finally, as the number of priority classes increases, both the priority revenues and the overall customer delay costs improve, but at a decreasing rate.     

管理学研究者客户迷失的判定、原因及出路

管理学研究者应认清自己的客户是谁,为客户创造价值。管理学研究者的客户有同行客户和实践客户2类,对管理学研究成果评价的优先次序依次是：实践客户、同行客户、专业机构。典型的客户迷失有抛弃客户型、客户倒置型、两头踏空型3种。管理学界3种迷失都相当严重,这加剧了管理学脱离实践和学术不端两大问题。解决客户迷失问题的出路是,认清研究成果的价值排序,改革学术评价体系,不再片面强调刊物等级指标,而是以“使用者、读者、引用者”为核心,按照研究者的自我定位分类进行评价。     

多项目环境下新产品研发项目资源分配问题研究

在对国内外相关文献进行评述的基础上,针对多项目环境下新产品研发项目的资源分配问题,将新产品研发多项目资源分配转换为一个多队列排队问题,建立了一个仿真模型,并以三个并行新产品研发项目为例,通过GPSS系统仿真得出了新产品研发项目的完工时间和资源分布结果并进行了分析。     

动态需求下车辆路径问题的周期性优化模型及求解

针对客户点不断更新的动态需求车辆路径问题,依据滚动时域对配送中心工作时间进行划分,提出基于延迟服务的周期性客户点实时重置策略,策略中延迟服务机制能结合车辆启动延迟系数对照当前时域的时间进行检验,满足所有客户点的服务需求,保证车辆满足中心时间窗约束。设计多阶段求解的混合变邻域人工蜂群算法对各时间片内子问题进行连续迭代优化,算法中子路径动态转变的设计能较好平衡原有客户点和新客户点对路径更新和车辆实时信息匹配的要求。算例验证及对比分析表明本文策略和算法在求解动态问题时的有效性和可行性。