This work introduce a model for the crew scheduling problem for train operations, based on a rotative schema, where weekly trips are fixed along the time. This generates a 0-1 medium/large size optimization problem. The special feature of this model is an infinite horizon schedule, due to the rotative schema, where every crew takes the place of the consecutive crew when a new week starts. The problem resolution is performed through three steps: first, finding a feasible solution of infinite length, where schedules repeat in a rotative way between crews; then, an adapted local search is used to improve the initial solution, in order to equilibrate the weekly working hours among crews; finally, drivers are assigned to the scheduled weeks, by solving a flow problem.

This paper provides an overview of the use of metaheuristics for solving multiobjective optimization problems. The metaheuristics discussed include multi-objective evolutionary algorithms (going from the early approches to the most recent research trends in that area), multi-objective particle swarm optimizers, multi-objective artificial immune systems, multi-objective ant colony systems and multi-objective scatter search. In the final part of the paper, we provide a review of sample applications of multi-objective metaheuristics, and a discussion of some of the topics in which more research is required.