This paper considers online stochastic optimization problems where uncertainties are characterized by a distribution that can be sampled and where time constraints severely limit the number of offline optimizations which can be performed at decision time and/or in between decisions. It reviews our recent progress in this area, proposes some new algorithms, and reports some new experimental results on two problems of fundamentally different nature: packet scheduling and multiple vehicle routing (MVR). In particular, the paper generalizes our earlier generic online algorithm with precomputation, least-commitment, service guarantees, and multiple decisions, all which are present in the MVR applications. Robustness results are also presented for multiple vehicle routing. © Springer-Velrag Berlin Hiedelberg 2004.
CITATION STYLE
Bent, R., & Van Hentenryck, P. (2004). Online stochastic and robust optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3321, 286–300. https://doi.org/10.1007/978-3-540-30502-6_21
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