Title
Refined Models for Efficiency-Driven Queues with Applications to Delay Announcements and Staffing
Abstract
AbstractData has revealed a noticeable impact of delay-time-related information on phone-customers; for example and somewhat surprisingly, delay announcements can abruptly increase the likelihood to abandon (hang up). Our starting point is that the latter phenomena can be used to support the control of queue lengths and delays. We do so by timing the announcements appropriately and determining the staffing levels accordingly. To this end, we model a service system as an overloaded GI/M/s + GI queue, in which we seek to minimize the number of servers, s, subject to quality-of-service constraints (e.g., fraction abandoning), while accounting for the instantaneous (hence discontinuous) impact of an announcement on the distribution (hazard rate) of customer patience. For tractability, our analysis is asymptotic as s increases indefinitely, and it is naturally efficiency-driven (namely the servers are highly busy, and hence essentially all customers are delayed in queue prior to service). This requires one to go beyond existing theory, which turns out to be too crude for our needs (e.g., it requires a continuous hazard rate of impatience and hence cannot be applied). We thus develop a refined process and steady-state models, and use them to solve our minimization problem and more. The value and accuracy of our models are demonstrated via extensive numerical experiments.The online appendix is available at https://doi.org/10.1287/opre.2017.1619.
Year
DOI
Venue
2017
10.1287/opre.2017.1619
Periodicals
Keywords
Field
DocType
customer abandonment,delay announcement,staffing,call centers,ED plus QED,refined approximation
Mathematical optimization,Staffing,Queue,Service system,Server,Hang,Mathematics,Operations management
Journal
Volume
Issue
ISSN
65
5
0030-364X
Citations 
PageRank 
References 
6
0.49
17
Authors
4
Name
Order
Citations
PageRank
Junfei Huang1202.80
Avishai Mandelbaum2106185.79
Zhang Hanqin322328.29
Jiheng Zhang4717.90