Multiobjective metaheuristics for the bus-driver scheduling problem

We present new multiobjective metaheuristics for solving real-world crew scheduling problems in public bus transport companies. Since the crews of these companies are drivers, we will designate the problem as bus-driver scheduling. Crew scheduling problems are well known, and several mathematical programming-based techniques have been proposed to solve them, in particular, using the single-objective set-covering formulation. However, in practice, there exists the need to consider multiple objectives, some of them in conflict with each other; for example, the cost and service quality, implying also that alternative solution methods have to be developed. We propose multiobjective metaheuristics based on the tabu search and genetic algorithms. These metaheuristics also present some innovation features related with the structure of the crew scheduling problem that guide the search efficiently and enable them to find good solutions. Some of these new features can also be applied to the development of heuristics to other combinatorial optimization problems. A summary of computational results with real-data problems is presented. The methods have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.