SIMARAIL: Simulation based optimization software for scheduling railway network

Sajedinejad, Arman; Mardani, Soheil; Hasannayebi, Erfan; K., S. Ahmad Reza Mir Mohammadi; Kabirian, Alireza

doi:10.1109/wsc.2011.6148066

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…The purpose of this section is to show which problems have previously been studied in Discrete event simulation is a commonly used tool in railroad logistics. One of the most common uses is train scheduling Sajedinejad et al 2011). …”

Section: Locomotive Research Applicationsmentioning

confidence: 99%

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Verschelden¹,

Stamm²,

Easton³

2017

2017 Winter Simulation Conference (WSC)

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Locomotives in the U.S. use over 3 billion gallons of fuel each year and faster refueling can increase rail network capacity without the infrastructure cost associated with new terminals or tracks. This thesis introduces the locomotive refueling system configuration problem (LRSCP), which seeks to improve efficiency in refueling yards through new technologies or policies. This research also creates two new methods to solve LRSCP. Based on experimental results, solving either TRIP or SLIP and incorporating the optimal refueling policy improves railyard operations by 10 to 30%. This impact is statistically significant and increases the capacity of a railyard. Additionally, it impacts other important parameters such as time spent in the yard and the maximum queue for the railyard. Furthermore, there is a significant decrease in wasted time and an improvement to railyard efficiency.Implementing either method should increase a railyard's capacity and significantly increase revenue opportunities.iv

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Section: Locomotive Research Applicationsmentioning

confidence: 99%

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Verschelden¹,

Stamm²,

Easton³

2017

2017 Winter Simulation Conference (WSC)

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“…As train timetabling problem is known to be NP-hard [5,7,15,33], a meta-heuristic algorithms have been applied to solve it. It has been shown that GA has high potential in finding the global optimum in a large, poorly defined search space even in the presence of difficulties such as high dimensionality, multi-modality, discontinuity, and noise [15].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…It has been shown that GA has high potential in finding the global optimum in a large, poorly defined search space even in the presence of difficulties such as high dimensionality, multi-modality, discontinuity, and noise [15]. GA has been successfully applied to combinatorial problems and is able to handle huge search spaces as those arising in scheduling problems [33].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The simulation methods can be categorized as synchronous simulation and asynchronous simulation [15]. Synchronous simulation models processed the movements of all trains at the same time, but asynchronous modeled rank trains according to their priorities and inserted trains into simulation procedure sequentially based on the ranks.…”

Section: Simulation Modelmentioning

confidence: 99%

“…Several attempts have been made to use complex search procedures including look-ahead search [9], backtracking search and meta-heuristics algorithms [10,11], mixed-integer linear programming, branch and bound, tabu search (TS) [12], an enhanced local search heuristic (LSH), GA, TS, and two hybrid algorithms [13] and GA [14][15][16][17]. However, these studies have not optimized train traffic flow considering stops for track maintenance and praying.…”

Section: Introductionmentioning

confidence: 99%

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A simulation-based optimization approach for passenger train timetabling with periodic track maintenance and stops for praying

Bahramian

Bagheri

2015

J. Mod. Transport.

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This paper presents two optimization methods for solving the passenger train timetabling problem to minimize the total delay time in the single track railway networks. The goal of the train timetable problem is to determine departure and arrival times to or from each station in order to prevent collisions between trains and effective utilization of resources. The two proposed methods are based on integration of a simulation and an optimization method to simulate train traffic flow and generate near optimal train timetable under realistic constraints including stops for track maintenance and praying. The first proposed method integrates a cellular automata (CA) simulation model with genetic algorithm optimization method. In the second proposed approach, a CA simulation model combines with dynamically dimensioned search optimization method. The proposed models are applied to hypothetical case study to demonstrate the merit of them. The Islamic Republic of Iran Railways (IRIR) data and regulations have been used to optimize train timetable. The results show the first method is more efficient than the second method to obtain near optimal train timetabling.

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

Shakibayifar

Hassannayebi

Jafary

et al. 2017

Appl Stoch Models Bus & Ind

Self Cite

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Urban rail planning is extremely complex, mainly because it is a decision problem under different uncertainties. In practice, travel demand is generally uncertain, and therefore, the timetabling decisions must be based on accurate estimation. This research addresses the optimization of train timetable at public transit terminals of an urban rail in a stochastic setting. To cope with stochastic fluctuation of arrival rates, a two‐stage stochastic programming model is developed. The objective is to construct a daily train schedule that minimizes the expected waiting time of passengers. Due to the high computational cost of evaluating the expected value objective, the sample average approximation method is applied. The method provided statistical estimations of the optimality gap as well as lower and upper bounds and the associated confidence intervals. Numerical experiments are performed to evaluate the performance of the proposed model and the solution method.

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SIMARAIL: Simulation based optimization software for scheduling railway network

Cited by 21 publications

References 12 publications

Integrated optimization and simulation models for the locomotive refueling system configuration problem

Integrated optimization and simulation models for the locomotive refueling system configuration problem

A simulation-based optimization approach for passenger train timetabling with periodic track maintenance and stops for praying

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

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