Locomotive optimization using artificial intelligence approach

The problem of assigning locomotives to trains consists of determining the number of locomotives of different types that provide sufficient power to pull trains on fixed schedules. The objective is to minimize the fixed and operational locomotive costs. The locomotive assignment problem is defined for cyclic and non cyclic problems. In this paper, an approach using a genetic algorithm and a neural network algorithm is presented to find a cyclic solution on a one-week horizon, while satisfying the power demands of all trains. This system was tested using realistic data from the Canadian National (C.N.) North America Company with about 1600 trains and 1300 locomotives.