Research Article

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Table 1

The characteristics of the related studies on transit network design problem.

PaperObjectivesDemandMethodology

G. Laporte et al. (2004)Trip coveringMode choice dependedConventional (CPLEX)
W. Fan et al. (2008)The sum of operator cost, user cost, and unsatisfied demand costsMode choice dependedHeuristic (systematic tabu search)
Á. Marín et al. (2009)Travel cost, attractivenessMode choice dependedConventional (piecewise linearization)
B. García-Archilla et al. (2011)Trip coverageMode choice dependedHeuristic (GRASP algorithms)
G. Gutiérrez-Jarpa et al. (2013)Travel cost, traffic captureAttraction depended between OD pairsConventional (branch-and-cut method with CPLEX)
J. S. C. Chew et al. (2013)Travel cost, operation costFixedHeuristic/matheuristic (genetic algorithm)
M. Nikolić et al. (2013)The number of satisfied passengers, transfers, and travel timeFixedMetaheuristic (bee colony optimization)
D. Canca et al. (2014)Net profit of the railway networkMode choice dependedHeuristic (combination of branch-and-bound and local nonlinear optimization)
G. Gutiérrez-Jarpa et al. (2017)Construction cost, time savings, patronageMode choice dependedConventional (-constraint method with CPLEX)
F. López-Ramos et al. (2017)Travel cost, costs related to construction and exploitationFixedHeuristic/matheuristic (corridor generation algorithm and line splitting algorithm)
D. Canca et al. (2017)Discounted profitMode choice dependedMatheuristic (adaptive large neighborhood search algorithm)
A. T. Buba et al. (2018)Passenger cost, unmet demandFixedMatheuristic (differential evolution)
X. Feng et al. (2019)Travel timeFixedHeuristic/matheuristic (genetic algorithm)
S. Bhushan Jha et al. (2019)Travel time, operation costFixedHeuristic/matheuristic (genetic algorithm, particle swarm optimization)