Hybrid Vehicle Routing Optimization with Endogenous Technology Selection for Sustainable Urban Transportation

This study develops a unified optimization framework to evaluate and compare the performance of electric, conventional, and hybrid vehicle routing strategies under consistent operational conditions. The proposed model integrates routing decisions, energy consumption, infrastructure constraints, and emission considerations within a single mathematical formulation, enabling a comprehensive assessment of both economic and environmental performance. Using a benchmark urban transportation network, the study analyzes route configurations, operational costs, financial feasibility, and carbon emissions across the three vehicle technologies. The results indicate that electric vehicles offer lower and more stable operational costs, with zero direct emissions. In contrast, conventional vehicles offer greater operational flexibility, albeit at the expense of higher costs and a greater environmental impact. The hybrid model, which allows endogenous technology selection at the route level, achieves the most balanced performance by assigning vehicle types based on route-specific characteristics. This leads to significant reductions in both operational costs and emissions compared to single-technology approaches. The findings underscore the importance of integrated and flexible routing strategies in supporting sustainable transportation planning, offering practical insights for policymakers and transport operators in designing efficient and environmentally responsible systems.

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Publication Details

Journal
Future Transportation
Published
2026-10-14
DOI
https://doi.org/10.3390/futuretransp6050230
Primary Topic
Vehicle Routing Optimization Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Hybrid Vehicle Routing Optimization with Endogenous Technology Selection for Sustainable Urban Transportation

Benedictus Rahardjo, Samuel Trinata Winnyarto, I Gede Arei Banyupramesta
Future Transportation
Vehicle Routing Optimization Methods
article

Hybrid Vehicle Routing Optimization with Endogenous Technology Selection for Sustainable Urban Transportation

Benedictus Rahardjo, Samuel Trinata Winnyarto, I Gede Arei Banyupramesta
article en

Abstract

This study develops a unified optimization framework to evaluate and compare the performance of electric, conventional, and hybrid vehicle routing strategies under consistent operational conditions. The proposed model integrates routing decisions, energy consumption, infrastructure constraints, and emission considerations within a single mathematical formulation, enabling a comprehensive assessment of both economic and environmental performance. Using a benchmark urban transportation network, the study analyzes route configurations, operational costs, financial feasibility, and carbon emissions across the three vehicle technologies. The results indicate that electric vehicles offer lower and more stable operational costs, with zero direct emissions. In contrast, conventional vehicles offer greater operational flexibility, albeit at the expense of higher costs and a greater environmental impact. The hybrid model, which allows endogenous technology selection at the route level, achieves the most balanced performance by assigning vehicle types based on route-specific characteristics. This leads to significant reductions in both operational costs and emissions compared to single-technology approaches. The findings underscore the importance of integrated and flexible routing strategies in supporting sustainable transportation planning, offering practical insights for policymakers and transport operators in designing efficient and environmentally responsible systems.

Future TransportationVol. 6(5)
National Taiwan University of Science and Technology (TW), Binus University (ID), Sepuluh Nopember Institute of Technology (ID)
Binus University
Industry, innovation and infrastructure, Sustainable cities and communities, Climate action, Affordable and clean energy
Openalex Percentile: Top 13%
Vehicle Routing Optimization Methods
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