Optimal service policies under learning effects Online publication date: Mon, 16-Jun-2008
by Geoffrey S. Ryder, Kevin G. Ross, John T. Musacchio
International Journal of Services and Operations Management (IJSOM), Vol. 4, No. 6, 2008
Abstract: For high-value workforces in service organisations such as call centres, scheduling rules rely increasingly on queueing system models to achieve optimal performance. Most of these models assume a homogeneous population of servers, or at least a static service capacity per service agent. In this work we examine the challenge posed by dynamically fluctuating service capacity, where servers may increase their own service efficiency through experience; they may also decrease it through absence. We analyse the special case of a single agent selecting between two different job classes, and examine which of five service allocation policies performs best in the presence of learning and forgetting effects. We find that a type of specialisation minimises the steady state queue size; cross-training boosts system capacity the most; and no simple policy matches a dynamic optimal cost policy under all conditions.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Services and Operations Management (IJSOM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com