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Motivated by a service platform, we study a two-sided network where heterogeneous demand (customers) and heterogeneous supply (workers) arrive randomly over time to get matched. Customers and workers arrive with a randomly sampled patience time (also known as reneging time in the literature) and are lost if forced to wait longer than that time to be matched. The system dynamics depend on the matching policy, which determines when to match a particular customer class with a particular worker class. Matches between classes use the head-of-line customer and worker from each class. Since customer and worker arrival processes can be very general counting processes, and the reneging times can be sampled from any finite mean distribution that is absolutely continuous, the state descriptor must track the age-in-system for every customer and worker waiting in order to be Markovian, as well as the time elapsed since the last arrival for every class. We develop a measure-valued fluid model that approximates the evolution of the discrete-event stochastic matching model and prove its solution is unique under a fixed matching policy. For a sequence of matching models, we establish a tightness result for the associated sequence of fluid-scaled state descriptors and show that any distributional limit point is a fluid model solution almost surely. When arrival rates are constant, we characterize the invariant states of the fluid model solution and show convergence to these invariant states as time becomes large. Finally, again when arrival rates are constant, we establish another tightness result for the sequence of fluid-scaled state descriptors distributed according to a stationary distribution and show that any subsequence converges to an invariant state. As a consequence, the fluid and time limits can be interchanged, which justifies regarding invariant states as first order approximations to stationary distributions.
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Large Deviations for the Single-Server Queue and the Reneging Paradox
For the M/M/1+M model at the law-of-large-numbers scale, the long-run reneging count per unit time does not depend on the individual (i.e., per customer) reneging rate. This paradoxical statement has a simple proof. Less obvious is a large deviations analogue of this fact, stated as follows: the decay rate of the probability that the long-run reneging count per unit time is atypically large or atypically small does not depend on the individual reneging rate. In this paper, the sample path large deviations principle for the model is proved and the rate function is computed. Next, large time asymptotics for the reneging rate are studied for the case when the arrival rate exceeds the service rate. The key ingredient is a calculus of variations analysis of the variational problem associated with atypical reneging. A characterization of the aforementioned decay rate, given explicitly in terms of the arrival and service rate parameters of the model, is provided yielding a precise mathematical description of this paradoxical behavior.
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- PAR ID:
- 10341417
- Date Published:
- Journal Name:
- Mathematics of Operations Research
- Volume:
- 47
- Issue:
- 1
- ISSN:
- 0364-765X
- Page Range / eLocation ID:
- 232 to 258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/moor.2021.1127
