Metaheuristics for the bus-driver scheduling problem

Abstract

We present new metaheuristics for solving real crew scheduling problems in a public transportation bus company. Since the crews of these companies are drivers, we will designate the problem by the bus-driver scheduling problem. Crew scheduling problems are well known and several mathematical programming based techniques have been proposed to solve them, in particular using the set-covering formulation. However, in practice, there exists the need for improvement in terms of computational efficiency and capacity of solving large-scale instances. Moreover, the real bus-driver scheduling problems that we consider can present variant aspects of the set covering, as for example a different objective function, implying that alternative solutions methods have to be developed. We propose metaheuristics based on the following approaches: GRASP (greedy randomized adaptive search procedure), tabu search and genetic algorithms. These metaheuristics also present some innovation features based on and genetic algorithms. These metaheuristics also present some innovation features based on the structure of the crew scheduling problem, that guide the search efficiently and able them to find good solutions. Some of these new features can also be applied in the development of heuristics to other combinatorial optimization problems. A summary of computational results with real-data problems is presented.