Designing hybrid integrative evolutionary approaches to the car sequencing problem

In this paper, we present three new integrative approaches for solving the classical car sequencing problem. These approaches are essentially based on a genetic algorithm which incorporates two crossover operators using an integer linear programming model. The two proposed hybrid crossover are combined efficiently in a genetic algorithm and we show that the hybrid approach outperforms a genetic algorithm with local search on the CSPLib benchmarks. Although that the computations time are long when integrative hybridization is used, this study well illustrates the interest of designing hybrid approaches exploiting the strengths of different methods.