Performance Modeling of Automatic Call Distributors: Operator Services Staffing with Heterogeneous Positions

Perry, Mark; Nilsson, Arne A.

doi:10.1016/b978-0-444-82031-0.50105-0

Cited by 15 publications

(20 citation statements)

References 4 publications

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“…(In these circumstances, agent selection arises infrequently enough to be handled arbitrarily.) We note that quadratic costs recover the aging factor of Kleinrock (1964), Kleinrock and Finkelstein (1967), and Perry and Nilsson (1992) that is introduced in the previous subsection. The assumption C i 0 = 0 rules out linear costs, but it is conjectured in Mandelbaum and Stolyar (2002) that these can be accommodated by carefully choosing aging factors that vary with system parameters.…”

Section: Gans Koole and Mandelbaummentioning

confidence: 99%

“…The paper also uses simulation to demonstrate how various routing policies can effect dramatic differences in system performance. Perry and Nilsson (1992) consider a V-design in which two classes of calls are served by a single pool of crossed-trained agents. The model represents a single group of operators that provides directory assistance, as well as serving toll/assist calls.…”

Section: Gans Koole and Mandelbaum Telephone Call Centersmentioning

confidence: 99%

“…Stanford and Grassmann (2000) and Shumsky (2000) both consider fixed, static priority policies: The former use matrix-geometric methods for performance analysis and staffing; and the latter an approximate analysis. Perry and Nilsson (1992) use a scheme first analyzed by Kleinrock (1964) and Kleinrock and Finkelstein (1967), that assigns to each waiting customer an aging factor that grows proportionally to its waiting time. The call selection problem is solved by serving the call with the greatest attained age.…”

Section: Gans Koole and Mandelbaum Telephone Call Centersmentioning

confidence: 99%

See 2 more Smart Citations

Telephone Call Centers: Tutorial, Review, and Research Prospects

Gans

Koole

Mandelbaum

2003

M&SOM

1,105

850

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T elephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value-and at the same time fundamentally limited-in their ability to characterize system performance.We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.

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Section: Gans Koole and Mandelbaummentioning

confidence: 99%

Section: Gans Koole and Mandelbaum Telephone Call Centersmentioning

confidence: 99%

Section: Gans Koole and Mandelbaum Telephone Call Centersmentioning

confidence: 99%

See 1 more Smart Citation

Telephone Call Centers: Tutorial, Review, and Research Prospects

Gans

Koole

Mandelbaum

2003

M&SOM

1,105

850

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“…Perry and Nilsson (1992) considered a system in which two classes of calls are served by a single pool of agents (V-design) and determined both the number of agents and the assignment policy needed to yield specified expected waiting times. Bhulai and Koole (2000) also modeled a variant of this system.…”

Section: Literature Reviewmentioning

confidence: 99%

Pooling strategies for call center agent cross-training

2009

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We consider call center environments where agents serve distinct customer types, and investigate the efficiency benefits achievable via cross-training. We do this by first considering specialized agents grouped into N departments according to the customer type they serve.Then we examine cross-training policies that pool a set of departments K (selecting k = |K| of N ) into a single larger department in which every agent serves all of the pooled call types. For the pooled department, we analyze both First-Come-First-Served (FCFS) and Non-Preemptive Priority (NPP) service disciplines. By comparing the resulting queueing models via standard queueing approximations and numerical analysis, we characterize the impact of system parameters, such as department sizes, arrival rates and service times, on the decision of what departments to pool.

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“…The state of research into solving skill-based routing problems is still in its infancy. A special case in which two classes of calls are served by a single pool of cross-trained agents is studied in Bhulai and Koole [3], Gans and Zhou [8], Perry and Nilsson [18], and by Gurvich, Armony, and Mandelbaum [9], who studied multiple classes of calls. Shumsky [21], Stanford and Grassmann [22], and Wallace and Whitt [24] considered fixed, static priority policies.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Routing Policies for Multiskill Call Centers

Bhulai¹

2008

Prob. Eng. Inf. Sci.

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We consider the problem of routing calls dynamically in a multiskill call center. Calls from different skill classes are offered to the call center according to a Poisson process. The agents in the center are grouped according to their heterogeneous skill sets that determine the classes of calls they can serve. Each agent group serves calls with independent exponentially distributed service times. We consider two scenarios. The first scenario deals with a call center with no buffers in the system, so that every arriving call either has to be routed immediately or has to be blocked and is lost. The objective in the system is to minimize the average number of blocked calls. The second scenario deals with call centers consisting of only agents that have one skill and fully cross-trained agents, where calls are pooled in common queues. The objective in this system is to minimize the average number of calls in the system. We obtain nearly optimal dynamic routing policies that are scalable with the problem instance and can be computed online. The algorithm is based on one-step policy improvement using the relative value functions of simpler queuing systems. Numerical experiments demonstrate the good performance of the routing policies. Finally, we discuss how the algorithm can be used to handle more general cases with the techniques described in this article.

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Performance Modeling of Automatic Call Distributors: Operator Services Staffing with Heterogeneous Positions

Cited by 15 publications

References 4 publications

Telephone Call Centers: Tutorial, Review, and Research Prospects

Telephone Call Centers: Tutorial, Review, and Research Prospects

Pooling strategies for call center agent cross-training

Dynamic Routing Policies for Multiskill Call Centers

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