Optimization of railway operations using neural networks

Railroad operations involve complex switching and classification decisions that must be made in short periods of time. Optimization with respect to these decisions can be quite difficult due to the discrete and non-linear characteristics of the problem. The train formation plan is one of the important elements of railroad system operations. While mathematical programming formulations and algorithms are available for solving the train formation problem, the CPU time required for their convergence is excessive. At the same time, shorter decision intervals are becoming necessary given the highly competitive operating climates of the railroad industry. The field of Artificial Intelligence (Al) offers promising alternatives to conventional optimization approaches. In this paper, neural networks (an empirically-based AI approach) are examined for obtaining good solutions in short time periods for the train formation problem (TFP). Following an overview, and formulation of railroad operations, a neural network formulation and solution to the problem are presented. First a training process for neural network development is conducted followed by a testing process that indicates that the neural network model will probably be both sufficiently fast, and accurate, in producing train formation plans. Copyright (C) 1996 Elsevier Science Ltd