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CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

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Abstract. In modern telephone exchanges, subscriber lines are usually connected to the socalled<br />

subscriber line modules. These modules serve both incoming <strong>and</strong> outgoing traffic. An<br />

important difference between these two types <strong>of</strong> calls lies in the fact that in the case <strong>of</strong> blocking<br />

due to all channels busy in the module, outgoing calls can be queued whereas incoming calls get<br />

busy signal <strong>and</strong> must be re-initiated in order to establish the required connection. The authors<br />

study the corresponding queueing model which takes into account subscriber retrials <strong>and</strong> investigate<br />

some <strong>of</strong> its properties such as existence <strong>of</strong> stationary regime, derive explicit formulas for<br />

the system characteristics, limit theorems for systems under high repetition intensity <strong>of</strong> blocked<br />

calls <strong>and</strong> limit theorems for systems under heavy traffic.<br />

Keywords: Telephone traffic, Queueing model, Retrial queue, Queueing theory, Congestion, Subscriber<br />

line modules, Telephone exchanges, Limit theorems<br />

34. Perry, M. <strong>and</strong> A. Nilsson. Performance modeling <strong>of</strong> automatic call distributors: assignable grade<br />

<strong>of</strong> service staffing. International Switching Symposium 1992. ‘Diversification <strong>and</strong> Integration <strong>of</strong><br />

Networks <strong>and</strong> Switching Technologies towards the 21st Century’ Proceedings. Inst. Electron.<br />

Inf. Commun. Eng, Tokyo, Japan, 1992, 294–298.<br />

Abstract. A new generation <strong>of</strong> automatic call distributors (ACDs) has been developed that can<br />

h<strong>and</strong>le multiple cell queues <strong>and</strong> associate priorities with these queues. The priority <strong>of</strong> a call is<br />

determined through an aging factor, which is a real number used to age the time a call has been<br />

queued. For example, if the oldest call in call queue 1 has waited 10 seconds, <strong>and</strong> the oldest<br />

call in call queue 2 has waited 7 seconds, <strong>and</strong> the aging factors for queue 1 <strong>and</strong> queue 2 are 3<br />

<strong>and</strong> 5, then the second call would have a higher priority, since the calculated priority factors<br />

are (10*3) <strong>and</strong> (7*5). The paper gives an analytical model for computing the expected waiting<br />

time for call types that have different aging factors. The results can be used to determine both<br />

the number <strong>of</strong> agents <strong>and</strong> the aging values needed to yield specified expected waiting times.<br />

Numerical examples focus on the ACD application operator services, in which both directory<br />

assistance <strong>and</strong> toll/assist calls are served from the same positions.<br />

Keywords: Performance modeling, Automatic call distributors, Operator services staffing, Heterogeneous<br />

positions, Telephony industry, Multi-purpose operator positions, Automatic call distributor,<br />

ACD, Toll <strong>and</strong> assist calls, Directory-assistance calls, Classical Erlang-type queueing<br />

models, Expected waiting-time, Average operator occupancy, Average occupancies, Simulation<br />

results<br />

35. Whitt, W. Underst<strong>and</strong>ing the efficiency <strong>of</strong> multi-server service systems, Management Science,<br />

38 (5), 1992, 708–723.<br />

Abstract. In the design <strong>and</strong> operation <strong>of</strong> service systems, it is important to determine an<br />

appropriate level <strong>of</strong> server utilization (the proportion <strong>of</strong> time each server should be working).<br />

In a multiserver queue with unlimited waiting space, the appropriate server utilization typically<br />

increases as the number <strong>of</strong> servers (<strong>and</strong> the arrival rate) increases. They explain this economy <strong>of</strong><br />

scale <strong>and</strong> give a rough quantitative characterization. The authors also show how increased variability<br />

in the arrival <strong>and</strong> service processes tends to reduce server utilization with a given grade<br />

<strong>of</strong> service. As part <strong>of</strong> this analysis, the author develops simple approximations for the mean<br />

steady-state waiting time <strong>and</strong> the fully steady-state waiting-time distribution. These approxi-<br />

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