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Online stochastic reservation systems

Author

Listed:
  • Pascal Hentenryck
  • Russell Bent
  • Luc Mercier
  • Yannis Vergados

Abstract

This paper considers online stochastic reservation problems, where requests come online and must be dynamically allocated to limited resources in order to maximize profit. Multi-knapsack problems with or without overbooking are examples of such online stochastic reservations. The paper studies how to adapt the online stochastic framework and the consensus and regret algorithms proposed earlier to online stochastic reservation systems. On the theoretical side, it presents a constant sub-optimality approximation of multi-knapsack problems, leading to a regret algorithm that evaluates each scenario with a single mathematical programming optimization followed by a small number of dynamic programs for one-dimensional knapsacks. It also proposes several integer programming models for handling cancellations and proves their equivalence. On the experimental side, the paper demonstrates the effectiveness of the regret algorithm on multi-knapsack problems (with and without overbooking) based on the benchmarks proposed earlier. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • Pascal Hentenryck & Russell Bent & Luc Mercier & Yannis Vergados, 2009. "Online stochastic reservation systems," Annals of Operations Research, Springer, vol. 171(1), pages 101-126, October.
    • Handle: RePEc:spr:annopr:v:171:y:2009:i:1:p:101-126:10.1007/s10479-008-0402-6
    DOI: 10.1007/s10479-008-0402-6
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    References listed on IDEAS

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    1. Russell Bent & Pascal Van Hentenryck, 2004. "A Two-Stage Hybrid Local Search for the Vehicle Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 38(4), pages 515-530, November.
    2. Russell W. Bent & Pascal Van Hentenryck, 2004. "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers," Operations Research, INFORMS, vol. 52(6), pages 977-987, December.
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    Cited by:

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    2. Huanan Zhang & Cong Shi & Chao Qin & Cheng Hua, 2016. "Stochastic regret minimization for revenue management problems with nonstationary demands," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 433-448, September.
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    4. Oleg Shcherbina & Elena Shembeleva, 2014. "Modeling recreational systems using optimization techniques and information technologies," Annals of Operations Research, Springer, vol. 221(1), pages 309-329, October.
    5. Christopher Dance & Alexei Gaivoronski, 2012. "Stochastic optimization for real time service capacity allocation under random service demand," Annals of Operations Research, Springer, vol. 193(1), pages 221-253, March.

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