Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256845" target="_blank" >RIV/61989100:27240/24:10256845 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0360544224006996?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544224006996?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.energy.2024.130927" target="_blank" >10.1016/j.energy.2024.130927</a>
Alternative languages
Result language
angličtina
Original language name
Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement
Original language description
This paper establishes a multi -objective optimization model for railway heavy -haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy -haul trains, introducing multi -objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon -inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi -objective transportation problems, the paper proposes a forward -learning pigeon -inspired optimization algorithm based on a surrogate -assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward -learning strategy to enhance learning from non -dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy
ISSN
0360-5442
e-ISSN
1873-6785
Volume of the periodical
294
Issue of the periodical within the volume
květen 2024
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
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UT code for WoS article
001217988700001
EID of the result in the Scopus database
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