Metaheuristics in Stochastic Combinatorial Optimization: a Survey

Metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and Stochastic Partitioning Methods are introduced, and their recent applications to a wide class of combinatorial optimization problems under uncertainty are reviewed. The flexibility of metaheuristics in being adapted to different modeling approaches and problem formulations emerges clearly. This paper provides also a description and classification of the modeling approaches of optimization under uncertainty. Moreover, a formal description of the main formulations corresponding to more classical domains in the literature is provided. In this survey, the reader familiar to metaheuristics finds also pointers to classical algorithmic approaches to optimization under uncertainty, while the reader new to metaheuristics should find a good tutorial in those metaheuristics that are being applied to optimization under un-