Blind Queues: The Impact of Consumer Beliefs on Revenues and Congestion

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (e.g., customers at checkout counters may not know the true service rate before joining). In such “blind queues,” customers make their joining/balking decisions based on limited information about the service provider’s operational parameters (from past service experiences, reviews, etc.) and queue lengths. We analyze a firm serving customers making decisions under arbitrary beliefs about the service parameters in an observable queue for a service with a known price. By proposing an ordering for the balking threshold distributions in the customer population, we are able to compare the effects of customer beliefs on the queue. We show that, although revealing the service information to customers improves revenues under certain conditions, it may destroy consumer welfare or social welfare. Given a market size, consumer welfare can be significantly reduced when a fast server announces its true service parameter. When revenue is higher under some beliefs, one would expect the congestion to also be higher because more customers join, but we show that congestion may not necessarily increase.

[1]  Refael Hassin,et al.  To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems , 2002 .

[2]  Xuanming Su,et al.  Bounded Rationality in Newsvendor Models , 2007, Manuf. Serv. Oper. Manag..

[3]  Gad Allon,et al.  Bounded Rationality in Service Systems , 2013, Manuf. Serv. Oper. Manag..

[4]  Wei Sun,et al.  Equilibrium and optimal strategies to join a queue with partial information on service times , 2011, Eur. J. Oper. Res..

[5]  Vishal Gaur,et al.  Asymmetric Consumer Learning and Inventory Competition , 2007, Manag. Sci..

[6]  Pengfei Guo,et al.  The effects of the availability of waiting-time information on a balking queue , 2009, Eur. J. Oper. Res..

[7]  Refael Hassin Information And Uncertainty In A Queuing System , 2007 .

[8]  Antonis Economou,et al.  Optimal balking strategies and pricing for the single server Markovian queue with compartmented waiting space , 2008, Queueing Syst. Theory Appl..

[9]  Tingliang Huang,et al.  Service Systems with Experience‐Based Anecdotal Reasoning Customers , 2015 .

[10]  Sebastian Fischer Comparison Methods For Stochastic Models And Risks , 2016 .

[11]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[12]  Refael Hassin Consumer Information in Markets with Random Product Quality: The Case of Queues and Balking , 1986 .

[13]  D. K. Hildebrand,et al.  Congestion Tolls for Poisson Queuing Processes , 1975 .

[14]  R. Duncan Luce,et al.  Individual Choice Behavior , 1959 .

[15]  R. Randhawa,et al.  Pricing in Queues without Demand Information , 2012 .

[16]  Laurens G. Debo,et al.  Equilibrium in Queues Under Unknown Service Times and Service Value , 2014, Oper. Res..

[17]  Yina Lu,et al.  Measuring the Effect of Queues on Customer Purchases , 2012, Manag. Sci..

[18]  B. G. Bhaskaran,et al.  Almost sure comparison of birth and death processes with application to M/M/s queueing systems , 1986, Queueing Syst. Theory Appl..

[19]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[20]  Zeynep Akşin,et al.  The Modern Call Center: A Multi‐Disciplinary Perspective on Operations Management Research , 2007 .

[21]  Pengfei Guo,et al.  Analysis and Comparison of Queues with Different Levels of Delay Information , 2007, Manag. Sci..

[22]  Constantinos Maglaras,et al.  On Customer Contact Centers with a Call-Back Option: Customer Decisions, Routing Rules, and System Design , 2004, Oper. Res..

[23]  A. Berger,et al.  Comparisons of multi-server queues with finite waiting rooms , 1992 .

[24]  Yves Dallery,et al.  Call Centers with Delay Information: Models and Insights , 2011, Manuf. Serv. Oper. Manag..

[25]  S. Chapman Advertising as information , 1999 .

[26]  P. Naor The Regulation of Queue Size by Levying Tolls , 1969 .

[27]  Constantinos Maglaras,et al.  Decision , Risk and Operations Working Papers Series Contact Centers with a CallBack Option and Real-Time Delay Information , 2004 .

[28]  Omar Besbes,et al.  Revenue Optimization for a Make-to-Order Queue in an Uncertain Market Environment , 2009, Oper. Res..

[29]  W. Whitt,et al.  Improving Service by Informing Customers About Anticipated Delays , 1999 .

[30]  Long Gao,et al.  Service Performance Analysis and Improvement for a Ticket Queue with Balking Customers , 2007, Manag. Sci..

[31]  Ronald W. Wolff,et al.  Poisson Arrivals See Time Averages , 1982, Oper. Res..

[32]  J. Quirk,et al.  Admissibility and Measurable Utility Functions , 1962 .

[33]  C. Larsen Investigating sensitivity and the impact of information on pricing decisions in an M/M/1/ ∞ queueing model , 1998 .