A multi-objective and dictionary-based checking for efficient rescheduling trains

Kumar, Neeraj; Mishra, Abhishek

doi:10.1016/j.aej.2021.01.027

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…Specifically, the identified industry 4.0 technologies used in this domain are AI, IoT, modelling and simulation, Big Data, SDSS, VR and Cloud Computing. Within these technologies we found the following algorithms for rail transport optimization: GA [ 104 ], Gaussian Kernel Ant Colony Optimization (GKACO) algorithm [ 103 ], MILPs [ 93 , 97 , 102 ], PCA algorithm [ 100 ], ML [ 86 , 96 ] and DL [ 87 , 89 ] techniques. For obtaining railway insights, we identified the following tools based on big data and cloud computing: Apache Hadoop, Tashi Cloud Middleware [ 111 ], Apache Spark and Google Cloud Infrastructure [ 79 ].…”

Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

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Section: Resultsmentioning

confidence: 99%

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Laiton-Bonadiez

Bedoya

Zapata-Cortés

et al. 2022

Sensors

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“…is can thus be even useless in some cases for the customers. e practice case considers the distribution of service availability during the year depending on many variables, such as customer demand, holidays, the number of trains, limited resources, and operational constraints [5,17].…”

Section: Methodsmentioning

confidence: 99%

Linear Programming Model and Online Algorithm for Customer-Centric Train Calendar Generation

Bosi

D’Ariano

2021

Journal of Advanced Transportation

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An important objective for train operating companies is to let users, especially commuters, directly query the ICT system about trains’ availability calendar, based on an online approach, and give them clear and brief information, expressed through “intelligent” phrases instead of bit maps. This paper provides a linear programming model of this problem and a fast and flexible heuristic algorithm to create descriptive sentences from train calendars. The algorithmic method, based on the “Divide and Conquer” approach, takes the calendar period queried in its whole and divides it into subsets, which are successively processed one by one. The dominant limitation of previous methods is their strong dependence on the size and complexity of instances. On the contrary, our computational findings show that the proposed online algorithm has a very limited and constant computation time, even when increasing the problem complexity, keeping its processing time between 0 and 16 ms, while producing good quality solutions that differ by an average surplus of 0.13 subsentences compared to benchmark state-of-art solutions.

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“…Although this approach can provide precise solutions, the response time tends to be relatively long. To address this issue, researchers have proposed novel targeted algorithms, including parallel algorithms [7], decomposition methods [19,20], rolling time domain algorithms [21], traffic direction multiplier methods [19,22], and heuristic-assisted methods [23,24], aiming to reduce computational costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

Hu,

Li,

Luo

2024

Applied Sciences

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This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems.

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A multi-objective and dictionary-based checking for efficient rescheduling trains

Cited by 12 publications

References 24 publications

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Industry 4.0 Technologies Applied to the Rail Transportation Industry: A Systematic Review

Linear Programming Model and Online Algorithm for Customer-Centric Train Calendar Generation

Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning

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