Optimal service policies under learning effects

Geoffrey S. Ryder, Kevin G. Ross and John T. Musacchio

International Journal of Services and Operations Management, 2008, vol. 4, issue 6, 631-651

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.

Keywords: operations models; services; service science; service engineering; optimal allocation policies; capacity management; queueing theory; learning; forgetting; Markov decision process; MDP; dynamic programming; call centres; scheduling; service efficiency; cross-training. (search for similar items in EconPapers)
Date: 2008
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