A. Unzueta, I. Eguia Ribero, M. A. Garín
Stochastic optimization problems of practical applications lead, in general, to some large scale models. The size of those models is linked to the number of scenarios that defines the scenario tree, which can be so large that decomposition strategies are required for problem solving in reasonable computing time. Methodologies such as Branch-and-Fix Coordination or Lagrangean Relaxation make use of these decomposition approaches, where independent scenario clusters are given. In this work, we present a technique to generate nested cluster submodel structures from the decomposition of a general two-stage stochastic mixed integer optimization model. These scenario cluster submodels can be embedded in different algorithmic schemes in order to make the chosen solution procedure more efficient. We will consider as a case study, a two-stage stochastic mixed 0-1 model that aims to make decisions that help to mitigate the complications of earthquake hazards, particularly in the area of Japan. Using it as a test bed, and with different algorithmic schemes, we provide some computational experience showing the effects of such a decomposition.
Keywords: Two stage mixed 0-1 optimization, Scenario Cluster Partitioning, Nested Decomposition
Scheduled
TC3 Natural Disaster Management
June 10, 2021 12:30 PM
3 - TC Koopmans
