title: Hybrid evolutionary optimization of two-stage stochastic integer programming problems: an empirical investigation.
abstract: In this contribution, we consider decision problems on a moving horizon with significant uncertainties in parameters. The information and decision structure on moving horizons enables recourse actions which correct the here-and-now decisions whenever the horizon is moved a step forward. This situation is reflected by a mixed-integer recourse model with a finite number of uncertainty scenarios in the form of a two-stage stochastic integer program. A stage decomposition-based hybrid evolutionary algorithm for two-stage stochastic integer programs is proposed that employs an evolutionary algorithm to determine the here-and-now decisions and a standard mathematical programming method to optimize the recourse decisions. An empirical investigation of the scale-up behavior of the algorithms with respect to the number of scenarios exhibits that the new hybrid algorithm generates good feasible solutions more quickly than a state of the art exact algorithm for problem instances with a high number of scenarios.
authors.affiliation: Process Dynamics and Operations Group, Department of Biochemical and Chemical Engineering, Technische Universität Dortmund, Dortmund, 44227, Germany. thomas.tometzki@bci.tu-dortmund.de
authors.forename: Thomas
authors.initials: T
authors.lastname: Tometzki
authors.forename: Sebastian
authors.initials: S
authors.lastname: Engell
mesh_terms: D000465:Algorithms; D003205:Computing Methodologies; D003657:Decision Making; D013269:Stochastic Processes