Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
TD7 - SM9: Queuing models in services 2
Time:
Tuesday, 28/June/2022:
TD 16:00-17:30

Session Chair: Ricky Roet-Green
Location: Forum 11


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Presentations

Advance selling and upgrading in priority queues

Yaolei Wang1, Ping Cao1, Jingui Xie2, Dongyuan Zhan3

1UCL, United Kingdom; 2Technical University of Munich; 3University College London

We study advance selling and upgrading in a priority queue setting that emerges in the amusement park industry. Waiting sensitive customers who purchase cheaper advance tickets may suffer from demand uncertainty when consuming the service. Customers can purchase regular tickets in advance and upgrade to fast-track tickets on-site. We find if there are some offline customers who cannot purchase in advance, then allowing upgrading generates more revenue for the service provider.



Foresee the next line: on information disclosure in tandem queues

Jingwei Ji1, Ricky Roet-Green2, Ran Snitkovsky3

1University of Southern California; 2University of Rochester; 3Columbia University

We consider a system where customers have to go through two service stages. We study the fully-observable model, in which queue-length information of both queues is available at arrival: customers observe the state of the entire system and decide whether to join or not. To learn the value of information we compare the fully observable system with the partially observable system in which, instead of observing the full system state, customers observe queue length only at arrival to each queue.



Dynamic control of service systems with returns

Timothy C. Y. Chan, Simon Y. Huang, Vahid Sarhangian

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON Canada

We study a queueing system with returns where at each service completion epoch, the decision maker can choose to reduce the probability of return for the departing customer at a cost that is convex increasing in the amount of reduction in the return probability. We characterize the structure of optimal long-run average and bias-optimal transient control policies for associated fluid control problems. Our results provide insights on the design of post-service intervention programs.