Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System

Table 1

Recent publications on energy-saving driving in comparison with this study.

Publication	Decision variable		Objects			The highest power of the velocity		Solution methods
	Timetable	Speed profiles	Energy consumption		The quality of service	<3	>=3
Li and Lo (2014)	√	√	√				√	GA
Fournier et al. (2012)	√		√	√			√	Linear programming algorithm
Nasri et al. (2010)	√		√				√	GA
Li and Yang (2013)	√		√				√	A binary-coded genetic algorithm
Bocharnikov et al. (2010)		√	√				√	GA
Rodrigo et al. (2013)		√	√				√	Semi-analytical solution
Tuyttens et al. (2013)		√	√				√	GA
Sun et al. (2019)	√		√	√			√	GA
Chevrier et al. (2013)	√		√				√	Multi-objective evolutionary algorithm
Wang and Goverde (2016)		√	√				√	Pseudospectral method
Luan et al. (2018)		√	√	√			√	PNLP approach and the PTSPO approach
Huang et al. (2019)		√	√				√	Machine learning algorithms
Yin et al. (2016)	√		√	√			√	Dynamic programming algorithm
Ye and Liu (2017)		√	√				√	Two novel methods
Yang et al. (2015)	√		√				√	GA
Yang et al. (2019)	√		√	√			√	Allocation algorithm
Hou et al. (2019)	√		√	√			√	CPLEX
This paper	√		√	√		√		NSGA-II