Online Stochastic and Robust Optimization

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.

• Type: Conference
• Year: 2004
• Where: ASIAN
• Authors: Russell Bent, Pascal Van Hentenryck
• Keywords: ASIAN 2004, Earlier Generic Online, Multiple Vehicle, Stochastic Optimization Problems