An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems

Abstract

Both energy consumption and travel time are important indices to evaluate the efficiency of operations of metro rail systems. This paper proposes an optimisation method to schedule trains for reducing the energy consumption and travel time. Firstly, we formulate an integer programming model with timetable and speed control. Secondly, we design an optimal train control algorithm and an adaptive genetic algorithm to find a good solution. Finally, we conduct numerical examples based on the real-life operation data from the Beijing Yizhuang metro rail line of China. The results illustrate that the proposed approach can reduce energy consumption by 7.31% and reduce travel time by 3.26% in comparison with the current operation strategy.

A Survey on Energy-Efficient Train Operation for Urban Rail Transit

“…The results showed that the developed model can save 2.4% of energy for one trip in comparison with the energy-efficient driving method [25]. Yang et al [80] developed an integrated optimization method to reduce the total energy consumption and total travel time, they determined the timetable and speed profile by finding the optimal dwell time at stations and maximum train speed at sections. The method can reduce total energy consumption by 7.31% in comparison with the current operation strategy in Beijing Metro Yizhuang Line.…”

An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

“…It was firstly initiated by Holland (1975) by mimicking the natural evolution process. Due to its extensive generality, strong robustness, high efficiency and practical applicability, genetic algorithm has been widely adopted as a numerical method for solving many transportation research problems, including the build-operate-transfer network design problem (Chen et al, 2006;Chen and Subprasom, 2007), stochastic multiobjective network design problem (Chen et al, 2010), reliability-based land use and transportation optimization (Yim et al, 2011), arterial traffic signal offset optimization (Hu and Liu, 2013), electric vehicle charging station (Dong et al, 2014), traffic restriction network design problem (Shi et al, 2014), and metro optimization problem (Yang et al, 2012(Yang et al, , 2015Xu et al, 2014). In this paper, we apply genetic algorithm to solve the following optimization model without the equality constraint.…”

Stochastic optimization of an urban rail timetable under time‐dependent and uncertain demand

“…Train scheduling models can be classified into off‐line train timetabling, train rescheduling, train controlling, and real‐time planning models. In context of railway systems, the performance measures are also different such as passenger travel time, passenger waiting time, train delays, regularity, punctuality, energy saving, deviation, and operational cost . A number of models and solution methods have been suggested in the literature to minimize the waiting time of passengers in transit networks.…”