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An anytime multistep anticipatory algorithm for online stochastic combinatorial optimization

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  • Luc Mercier
  • Pascal Hentenryck

Abstract

The one-step anticipatory algorithms (1s-AA) is an online algorithm making decisions under uncertainty by ignoring the non-anticipativity constraints in the future. It was shown to make near-optimal decisions on a variety of online stochastic combinatorial problems in dynamic fleet management and reservation systems. Here we consider applications in which 1s-AA is not as close to the optimum and propose Amsaa, an anytime multi-step anticipatory algorithm. Amsaa combines techniques from three different fields to make decisions online. It uses the sampling average approximation method from stochastic programming, search algorithms for Markov decision processes from artificial intelligence, and discrete optimization algorithms. Amsaa was evaluated on a stochastic project scheduling application from the pharmaceutical industry featuring endogenous observations of the uncertainty. The experimental results show that Amsaa significantly outperforms state-of-the-art algorithms on this application under various time constraints. Copyright Springer Science+Business Media, LLC 2011

Suggested Citation

  • Luc Mercier & Pascal Hentenryck, 2011. "An anytime multistep anticipatory algorithm for online stochastic combinatorial optimization," Annals of Operations Research, Springer, vol. 184(1), pages 233-271, April.
    • Handle: RePEc:spr:annopr:v:184:y:2011:i:1:p:233-271:10.1007/s10479-010-0798-7
    DOI: 10.1007/s10479-010-0798-7
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    References listed on IDEAS

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    1. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    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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    3. Giovanni Pantuso, 2021. "A node formulation for multistage stochastic programs with endogenous uncertainty," Computational Management Science, Springer, vol. 18(3), pages 325-354, July.
    4. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).

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