A new approach for improving the performance of freight train timetabling of a single-track railway system

Ke, Bwo-Ren; Lin, Chih-tsang; Chien, H.H.; Chiu, Hsien-Shih; Chen, N.

doi:10.1080/03081060.2014.959357

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…Based on the temporal fuzzy reasoning method, a new conflict prediction method is proposed, and the results under two scenarios of HSR in China prove that conflict prediction after the fuzzy processing of the time intervals of a train timetable is reliable and practical. Conflicts between successive stations and within stations are identified and solved with the fuzzy logic system, where expertise is used to establish fuzzy rules and adjust train dwell times [143]. The fuzzy rules from this knowledge after fuzzification could express their actual meaning effectively.…”

Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

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Section: B: Gmmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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“…Regarding safe interactions between trains as constraints, the objective was to minimize the arrival time of the last train at its destination. Finally, Ke et al [23] addressed the problem of freight train timetabling on a single-track railway system to minimize the train waiting times. They presented a new method that utilized both fixed-block signaling systems and fuzzy logic systems to address the problem of freight train timetabling.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Model for Scheduling Freight Trains on a Single Rail Track

Alaghband

Moghaddam

2020

Scientia Iranica

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In many countries, a rail network consists of single lines with sidings where interactions between trains occur (meet, pass). In this paper, we study two issues of these networks: first, the scheduling of freight trains in a single line corridor while ensuring safe interactions and second, the allocation of freight to freight trains regarding the release dates of freight, weight of freight, and weight capacity of the trains. Both of these issues must be addressed when examining real-world freight train scheduling problems. The objective functions of this study are the minimization of a train's travelling time, the allocation of freight to freight trains, and the reduction of tardiness of freight at destination. Both scheduling and allocation problems are presented using integer linear programming models. In addition, an integrated novel heuristic algorithm has been proposed to solve them. Computational results demonstrated through a generated dataset show both model validation and efficiency of the heuristic algorithm. The heuristic algorithm has been designed to incorporate the practical operational railway rules with modest modification. Although its outputs slightly differ from the exact solutions, it can solve both models simultaneously in large scale problems.

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