Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

Table 1

Parameters	Description
	Set of train services, indexed by
	Set of stations, indexed by
	Stations next to station
,	Origin station and destination station of train service , respectively
	Set of stations on which train service runs,
	Scheduled arrival time of train service at station
	Scheduled departure time of train service from station
,	Penalty per time unit for delays and the number of train services delayed, respectively
	Free-flow time of train service at section ,
	Minimum dwell time of train service at station
	The headway of departure from station for train services
	The headway of arrival at station for train services
	Primary delays of train at station
	Maximum delays of each train service
,	Additional time caused by starting and stopping at station, respectively
	Set of available tracks at station
,	Set of available siding and main tracks at station , respectively
	Minimum interval time of the same track occupied by adjacent train services
Decision variables	Description
	Actual arrival time of train service at station
	Actual departure time of train service from station
	Binary variable that represents whether train service precedes train service arrival at station (1 indicates yes)
	Binary variable that represents whether train service precedes train service departure from station (1 indicates yes)
	Binary variable that represents whether train service stops at station (1 indicates yes)
	Binary variable that represents whether train service occupies track of station (1 indicates yes)