Locomotive assignment problem with consideration of infrastructure and freight train constraints: Mathematical programming model and metaheuristic solution approaches

Abstract

Railway companies can improve their achievement through the efficient management of locomotives in the network, even in the case of severe limitations in number of available locomotives. To reach the mentioned goal, this study addresses a Locomotive Assignment Problem (LAP) for freight trains. It simultaneously considers timetable flexibility, locomotive connection and train path restrictions, locomotive light movements, and lost powers due to forming consists (combination of locomotives) and difference between required power of trains and output of consists. The aim is to minimize the total operational cost imposed by light movements, lost powers, and deviation from initial timetable. To solve the problem, a Mixed-Integer Quadratically-Constrained Programming (MIQCP) is proposed. Due to the NP-hard nature of the problem, two metaheuristic solution approaches, including Adaptive Genetic Algorithm (AGA) and Simulated Annealing (SA) are proposed. An experimental analysis based on small-scale and large-scale instances is designed to investigate the performance of the solution approaches. To implement a sensitivity analysis based on LAP indicators, the Iranian railway network is applied as a real-world case study. Based on the results, it is deduced that there is a mutual interaction between the LAP and the train formation problem. Applying longer and heavier trains would cause an increase in the light movements of the locomotives, but it decreases their lost powers. The managerial insights provided in this study can help experts make appropriate decisions for the railway systems.