Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Svazek periodika

294

Číslo periodika v rámci svazku

květen 2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

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Kód UT WoS článku

001217988700001

EID výsledku v databázi Scopus

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