• Corpus ID: 221516154

An online learning approach to dynamic pricing and capacity sizing in service systems

@article{Chen2020AnOL,
  title={An online learning approach to dynamic pricing and capacity sizing in service systems},
  author={Xinyun Chen and Yunan Liu and Guiyu Hong},
  journal={arXiv: Probability},
  year={2020}
}
We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queue, where the service provider's objective is to obtain the optimal service fee $p$ and service capacity $\mu$ so as to maximize cumulative expected profit (the service revenue minus the staffing cost and delay penalty). Due to the complex nature of the queueing dynamics, such a problem has no analytic solution so that previous research often resorts to heavy-traffic analysis in that both the arrival rate and service rate… 
View PDF on arXiv
3 Citations
Background Citations
2
Methods Citations
1

3 Citations

Citation Type

Citation Type

Adaptive service rate control of M/M/1 queue with breakdowns
We study service rate control problems for the M/M/1 queue with breakdowns in which the breakdown rate is assumed to be a function of the service rate. Assuming that the queue has infinite capacity,…
Selecting the Best Optimizing System
TLDR
This work designs easy-to-implement algorithms that adaptively chooses a system and a choice of decision to evaluate the noisy system performance, sequentially eliminates inferior systems, and eventually recommends a system as the best after spending a user-specified budget.
Stochastic approximation of symmetric Nash equilibria in queueing games
: We suggest a novel stochastic approximation algorithm to compute a Symmetric Nash Equilibrium strategy in a general queueing game with a finite action space. The algorithm involves a single…

References

SHOWING 1-10 OF 66 REFERENCES
Online Learning and Pricing for Service Systems with Reusable Resources
TLDR
Two new multi-armed bandit (MAB) based learning algorithms are proposed, termed Batch Upper Confidence Bound (BUCB) algorithm and Batch Thompson Sampling (BTS) algorithm, for finding near-optimal pricing policies.
Optimal pricing and capacity sizing for the GI/GI/1 queue
Pricing and Capacity Sizing for Systems with Shared Resources: Approximate Solutions and Scaling Relations
TLDR
Analysis of pricing and capacity sizing decisions in a single-class Markovian model motivated by communication and information services finds that congestion costs are "small," the optimal price admits a two-part decomposition, and the joint capacity sizing and pricing problem decouples and admits simple analytical solutions that are asymptotically optimal.
Pricing and Capacity Sizing of a Service Facility: Customer Abandonment Effects
TLDR
This paper studies the effect of customer abandonment in the economic optimization of a service facility and derives the following economical insight: when the capacity cost is sufficiently high, it can be advantageous for the system manager to “underinvest” in capacity and take advantage of the abandonments to trim congestion.
Dynamic Control of an M/M/1 Service System with Adjustable Arrival and Service Rates
TLDR
A service facility in which the system manager dynamically controls the arrival and service rates to maximize the long-run average value generated is studied, finding that the optimal arrival rate is decreasing and the optimal service rate is increasing in the number of customers in the system.
The Value of Dynamic Pricing in Large Queueing Systems
TLDR
It is shown that a simple policy of using only two prices can achieve most of the benefits of dynamic pricing, and dynamic pricing performs significantly better than static pricing at mitigating the effect of uncertainty.
Exploiting Market Size in Service Systems
TLDR
It is shown that employing the last-come, first-served rule in the concave case results in utilization and profit similar to the linear case, regardless of the actual form of the delay costs.
An Adaptive Algorithm for Finding the Optimal Base-Stock Policy in Lost Sales Inventory Systems with Censored Demand
TLDR
A nonparametric adaptive algorithm is developed that generates a sequence of order-up-to levels whose running average of the inventory holding and lost sales penalty cost converges to the cost of the optimal base-stock policy, and the cubic-root convergence rate of the algorithm is established.
Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems
TLDR
The results suggest that firms would be better off to perform dynamic learning and action concurrently rather than sequentially, and that the values of information on both the parametric form of the demand function as well as each customer's exact reservation price are less important than prior literature suggests.
Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms
TLDR
A single-product revenue management problem where the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues, and proposed algorithms develop policies that learn the demand function “on the fly,” and optimize prices based on that.
...
1
2
3
4
5
...