2014
DOI: 10.1016/j.simpat.2014.09.004
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Urban rail transit planning using a two-stage simulation-based optimization approach

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Cited by 76 publications
(23 citation statements)
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“…This is especially useful in some practical situations where the explicit analytical formulae are too complex to be deduced. In urban rail transit station domain, researchers used simulation-based optimization technique to evaluate and design different service facilities (Jiang et al [18]; Jiang and Lin [27]; Hassannayebi et al [28]). …”
Section: Literature Reviewmentioning
confidence: 99%
“…This is especially useful in some practical situations where the explicit analytical formulae are too complex to be deduced. In urban rail transit station domain, researchers used simulation-based optimization technique to evaluate and design different service facilities (Jiang et al [18]; Jiang and Lin [27]; Hassannayebi et al [28]). …”
Section: Literature Reviewmentioning
confidence: 99%
“…The problem was formulated as a combinatorial optimization problem and solved using mathematical programming techniques and metaheuristics. Hassannayebi et al [22] proposed a simulation-based optimization approach for train timetabling in urban rail. The problem was solved using genetic algorithm and the outcomes verify the e ectiveness of the solution method.…”
Section: Literature Reviewmentioning
confidence: 99%
“…The praying service activation conditions can be stated by parameters ea j and ld j in the following equations: ld j L r: (j;1) + t p ; (22) ea j U r: (j;K j +1) t p : (23) Next, we describe the main components of our preprocessing in Algorithm 1.…”
Section: Variable Xing Strategiesmentioning
confidence: 99%
“…Simulation modeling approaches have been used extensively in transportation applications as a exible and powerful method to evaluate the robustness and reliability of the system (see [2,[6][7][8][9][10][11][12][13][14]). However, despite the fact that the train-rescheduling problem has been analyzed extensively, a limited research has been directed to the combination of simulation platforms with advanced search techniques to solve train-rescheduling problems under uncertainty.…”
Section: Introductionmentioning
confidence: 99%