Managing Response Time in a Call-Routing Problem with Service Failure

Véricourt, Francis de; Zhou, Yong‐Pin

doi:10.1287/opre.1050.0230

Cited by 66 publications

(60 citation statements)

References 28 publications

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“…Recent empirical research findings in primary care services confirm our conclusions. Chen et al (2009) and Mechanic et al (2001) examine primary care visit data in the United States between 1989 and2005, and show that primary care visit durations have increased (i.e., the average service rate is slower) with an accompanied increase in service value. It is optimal for firms providing primary care services to invest in highquality, slower service, and therefore, partial market coverage is likely to be observed.…”

Section: Service Speed and Market Coveragementioning

confidence: 99%

Quality–Speed Conundrum: Trade-offs in Customer-Intensive Services

2011

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I n many services, the quality or value provided by the service increases with the time the service provider spends with the customer. However, longer service times also result in longer waits for customers. We term such services, in which the interaction between quality and speed is critical, as customer-intensive services. In a queueing framework, we parameterize the degree of customer intensity of the service. The service speed chosen by the service provider affects the quality of the service through its customer intensity. Customers queue for the service based on service quality, delay costs, and price. We study how a service provider facing such customers makes the optimal "quality-speed trade-off." Our results demonstrate that the customer intensity of the service is a critical driver of equilibrium price, service speed, demand, congestion in queues, and service provider revenues. Customer intensity leads to outcomes very different from those of traditional models of service rate competition. For instance, as the number of competing servers increases, the price increases, and the servers become slower.

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Section: Service Speed and Market Coveragementioning

confidence: 99%

Quality–Speed Conundrum: Trade-offs in Customer-Intensive Services

2011

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“…In Ren and Zhou (2008), service quality depends on the staffing level; longer time with the customer often leads to better call resolution and thus to high service quality, an assumption similar to ours. De Véricourt and Zhou (2005) and Armony and Gurvich (2009) study the trade-offs between server speed and quality (that is associated with service failures in de Véricourt and Zhou (2005) and waiting times in Armony and Gurvich 2009) in making optimal call-routing decisions in a call center environment. There is no pricing decision in these works.…”

Section: Literature Reviewmentioning

confidence: 99%

Speed–Quality Trade-Offs in a Dynamic Model

Kostami

Rajagopalan

2014

M&SOM

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A n important trade-off organizations face in many environments is one between quality and speed. Working faster may result in greater output and less delay, but may result in lower quality and dissatisfied customers. In this work, we consider dynamic models in a monopoly setting to explore the optimal balance among the multiple dimensions of speed, price, and wait time. The impact of quality is captured via the market demand potential, which is a function of the speed (quality) in the previous period. We obtain several results and insights. First, in scenarios where speed may be difficult to change over time (e.g., some automated production lines) but price can be changed, we show that the optimal price charged is such that the demand rate remains constant over time, even though the price and market potential are changing. Furthermore, we identify conditions when the firm will work at a speed that is higher or lower than a benchmark speed and characterize the behavior of prices over time. Second, in scenarios where a firm may not be able to change prices but can adjust the speed each period, the firm starts at a speed that may be faster or slower than a benchmark speed but converges to it over time. In this constant price case, as the benchmark speed increases, the initial speed adopted by the firm is actually lower but increases more quickly thereafter. We also characterize the behavior of price and speed in settings where both can be changed over time. Interestingly, a firm typically starts at a slow speed and increases the speed, price, and demand over time. Although our main model assumes that the firm internalizes the congestion cost, several of our results extend to a scenario where the demand rate is impacted by the congestion level.

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“…We then have the following. Thus, since by (17),Ā N ⇒ e as N → , and since by (12) and (18) Now note that summing system Equations (7) and (8) one obtains that…”

Section: Fluid Limit Resultsmentioning

confidence: 93%

A Fair Policy for theG/GI/NQueue with Multiple Server Pools

Reed¹,

Shaki²

2015

Mathematics of OR

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We consider the G/GI/N queue with multiple server pools, each possessing a pool-specific service time distribution. The class of nonidling routing policies that we consider are referred to as u-greedy policies. These policies route incoming customers to the server pool with the longest weighted cumulative idle time to equitably spread incoming work amongst the server pools in the system. Our first set of results demonstrates that asymptotically in the Halfin-Whitt regime and under any u-greedy policy, the diffusion scaled cumulative idle time processes of each of the server pools are held in fixed proportion to one another. We next provide a heavy traffic limit theorem for the process keeping track of the total number of customers in the system. Our limit may be characterized as the solution to a stochastic convolution equation that is driven by a Gaussian process. To prove our main results, we introduce a new methodology for studying the G/GI/N queue in the Halfin-Whitt regime that has as its starting point a simple conservation of flow identity. Introduction. In the literature recently (Armony and Ward [3], Atar [4], Atar et al. [5], Gurvich andWhitt [18]), many papers have been published on the topic of providing fairness in many-server queueing systems with multiple server pools, with the concept of fairness being directed toward the customers as well as toward the servers. The majority of these papers focus on many-server queueing systems in the Halfin-Whitt [19] regime with exponentially distributed service times, possibly with service rates varying across the different server pools. The results of these papers make it overwhelmingly clear that under the assumption of exponentially distributed service times in each server pool, it is possible to achieve a high level of performance in a many-server queue while at the same time equitably sharing the incoming work across the different servers in the system.One of the primary motivating applications for the study of many-sever queues is the design and control of large-scale telephone call centers. However, it has been well documented in Brown et al.[8] that service times in telephone call centers are not exponentially distributed but rather appear to be lognormally distributed. This then brings us to the following important set of questions. Which policies already demonstrated to perform well for many-server queues under the assumption of exponentially distributed service times also perform well for general service time distributions? In other words, how robust are these policies with regard to changes in the service time distribution? Moreover, given that a policy performs well in the case of general service time distributions, how may we begin to evaluate its detailed performance measures?In the present paper, the above set of questions is addressed by considering a many-server queue with a single class of customers and multiple server pools. This type of system is commonly referred to in the literature as the inverted-V model. We assume that service times wi...

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Managing Response Time in a Call-Routing Problem with Service Failure

Cited by 66 publications

References 28 publications

Quality–Speed Conundrum: Trade-offs in Customer-Intensive Services

Quality–Speed Conundrum: Trade-offs in Customer-Intensive Services

Speed–Quality Trade-Offs in a Dynamic Model

A Fair Policy for theG/GI/NQueue with Multiple Server Pools

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