An Optimal Multi-objective Train Speed Profile for Mass Transit Systems Using a Genetic Algorithm-Based Technique

This paper presents movement planning of a mass transit system between two stations with the use of Genetic Algorithm (GA) technique to minimize total energy consumption and total energy loss during the journey with appropriate weighting factors. The train movement is based on a sequence of four modes of operation, i.e., accelerating, constant speed or cruising, coasting, and braking modes. The train speed profile is genetically optimized by controlling the acceleration, the deceleration, and the location of coasting point. In this study, the investigation was carried out with a mass transit section between two station platforms with the service distance of 2 km, the variation of track gradient, and the maximum speed of 80 km/h. The results demonstrated that when compared with the use of GA-based single-objective functions, solving such a problem by using a GA-based multi-objective function can reduce the overall energy consumption (0.14% max) and total energy loss (3.53% max) while still being able to maintain the desired operation speed performance.