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Cited by 60Supply Chain and Inventory ManagementConsumer Market Behavior and PricingAdvanced Queuing Theory Analysis

Optimal Multiperiod Pricing with Service Guarantees

Borgs, Christian, Candogan, Ozan, Chayes, Jennifer, Lobel, Ilan, Nazerzadeh, Hamid·2014·Management Science
DOI 10.1287/mnsc.2013.1839
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Abstract

We study the multiperiod pricing problem of a service firm with capacity levels that vary over time. Customers are heterogeneous in their arrival and departure periods as well as valuations, and are fully strategic with respect to their purchasing decisions. The firm's problem is to set a sequence of prices that maximizes its revenue while guaranteeing service to all paying customers. We provide a dynamic programming based algorithm that computes the optimal sequence of prices for this problem in polynomial time. We show that due to the presence of strategic customers, available service capacity at a time period may bind the price offered at another time period. This phenomenon leads the firm to utilize the same price in multiple periods, in effect limiting the number of different prices that the service firm utilizes in optimal price policies. Also, when customers become more strategic (patient for service), the firm offers higher prices. This may lead to the underutilization of capacity, lower revenues, and reduced customer welfare. We observe that the firm can combat this problem if it has an ability, beyond posted prices, to direct customers to different service periods. This paper was accepted by Dimitris Bertsimas, optimization.

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  • Previous work has used RL and dynamic programming to set real-time electricity prices and more broadly for power market design.

    “Previous work has used RL and dynamic programming to set real-time electricity prices [30,90] and more broadly for power market design [30,881].”

    From Tackling climate change with machine learning (2023) · cites this paper as [90]

  • ML can help both to design real-time prices and to respond to these prices.

    “In this case, ML can help both to design real-time prices and to respond to these prices.”

    From Tackling climate change with machine learning (2023) · cites this paper as [90]

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Related

Authors
Borgs, C., Candogan, O., Chayes, J., Lobel, I., & Nazerzadeh, H.
Affiliations
  • Microsoft (United States) (US)
  • Duke University (US)
  • New York University (US)
  • University of Southern California (US)
ORCID
Venue
Management Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Published
2014-07
Volume
60
Issue
7
Pages
1792-1811
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
DOI
10.1287/mnsc.2013.1839
OpenAlex
W3022973009
Cited by
60
References
35
Citations by year
2026: 2 · 2023: 3 · 2022: 7 · 2021: 2 · 2020: 7 · 2019: 8 · 2018: 6 · 2017: 7 · 2016: 12 · 2015: 5 · 2014: 1
FWCI
18.49
Type
journal article
Language
en
Open access
closed
Locations
Management Science (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Supply Chain and Inventory Management
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Advanced Queuing Theory Analysis
Keywords
service guarantees, time-varying capacity, strategic customers, revenue maximization, dynamic programming algorithm, consumer welfare, customer patience

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