A fuzzy railroad blocking model with genetic algorithm solution approach for Iranian railways

In the railway, the fright car classification takes place in the terminals. This classification always imposes a remarkable delay to the movement of the cars from origin to destination. To reduce car handling, it is necessary to group various shipments together with respect to their destination in the railroad blocking plan. In this paper, for the first time, a railroad blocking model with fuzzy travel costs is proposed. In the model, the preferred fuzzy paths are determined by a fuzzy shortest path method. Then, the fuzzy model is transformed into a classic railroad blocking model. The real-life blocking problems are very large with many variables and constraints, and modeling and solving them using commercially available software is very time consuming. Therefore, a solution method based on genetic algorithm is developed. To evaluate the performance of the solution method, several simulated problems are tested and the solutions of genetic algorithm are compared with those of the CPLEX software. The results reveal the algorithm has promising accuracy and computing speed for solving the railroad blocking problem. As a case study, the proposed model for creating the Iranian railway blocking plan is utilized. Iran railways can significantly diminish the some costs and save the time in delivering the loads.