research-article

Elastic train scheduling model

Author:

Ahmad Reza Jafarian-MoghaddamAuthors Info & Claims

Volume 110, Issue C

https://doi.org/10.1016/j.asoc.2021.107627

Published: 01 October 2021 Publication History

Abstract

Train scheduling plays a significant role in the optimal use of the railway resources and satisfaction of customers. This paper presents a novel innovation, which is the elasticity of train length, or the possibility of compression and stretching of the train length in accordance with rail route conditions. The proposed sustainable model provides optimal train scheduling with minimum travel time as well as optimal revenue and cost by developing a balance between length, speed, traction power, and energy consumption of trains while there is the possibility of carrying homogeneous and heterogeneous commodities. Thus, the new model is referred to as the sustainable elastic train scheduling model. Implementing the model in a real case using an introduced genetic algorithm proves its success and increases railway revenues by at least 48 percent with the saving of 25 percent in time. Sensitivity analysis of the model reveals the model is more sensitive to changes in the objective function of travel time. In addition to 50% improvement in using railroad capacity, it is possible to achieve the minimum increase of 71% in revenue.

Highlights

•

New Green Train Scheduling mathematical Model.

•

The scheduling of elastic trains.

•

A new perspective for formulating the objective functions of the model.

•

A sustainable railway transportation model (i) Considering the train’s length, rail route conditions, shunting operations, timely, and energy simultaneously. (ii) Possibility to carry single/homogeneous and multiple/heterogeneous commodities.

•

Proposing a solution algorithm based on a genetic algorithm.

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Digital Library

Cited By

Wang RZhang QDai XYuan ZZhang TDing SJin Y(2023)An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockageApplied Soft Computing10.1016/j.asoc.2023.110590145:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110590
Urda DRuiz PTalbi EBouvry PToutouh J(2023)Editorial of the Special Issue Intelligent Solutions for Efficient Logistics and Sustainable TransportationApplied Soft Computing10.1016/j.asoc.2022.109961133:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.asoc.2022.109961

Index Terms

Elastic train scheduling model
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 110, Issue C

Oct 2021

1181 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 October 2021

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Cited By

Wang RZhang QDai XYuan ZZhang TDing SJin Y(2023)An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockageApplied Soft Computing10.1016/j.asoc.2023.110590145:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110590
Urda DRuiz PTalbi EBouvry PToutouh J(2023)Editorial of the Special Issue Intelligent Solutions for Efficient Logistics and Sustainable TransportationApplied Soft Computing10.1016/j.asoc.2022.109961133:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.asoc.2022.109961

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