The Effect of Using the Tabu Search Algorithm on the Speed of Achieving the Optimal Train Speed Profile (in order to Reduce Energy Consumption)

Rail transit plays an increasingly important role in the public transportation system, and effectively reducing its huge energy consumption is of great practical significance. Wider use of public transport, particularly rail and metro, is one way to save energy. A growing trend of applying the rail network and metro by governments on one hand and the considerable energy consumption of a train during one year, on the other hand, demonstrate the necessity of considering the consumed energy by train. Railway transportation consumes amounts of energy. Direct energy consumption to complete the transport tasks is the main part of energy consumption of rail transportation, especially the traction system, which leads to the railway transportation costly. Optimization of the train speed curve plays an important role in minimizing train energy consumption. In this paper, first, there was a review on models of train energy optimization with different characteristics and corresponding other algorithms to find the optimum speed profile and accuracy of them, Second Tabu Search (TS) algorithm as a new approach for optimizing the train speed profile to save energy will be investigated. In this approach, after determining the appropriate points of acceleration, neural and braking, a speed profile in which train could cover its route with minimum energy consumption will be achieved. We call these points "the variables for changing the training strategy. The algorithm was implemented in alternative routes. In this study, the simulations of the proposed method are implemented in MATLAB software and are finally compared with the Genetic Algorithm method.