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author:

Xu, L. (Xu, L..) [1] | Wu, P. (Wu, P..) [2] | Chu, C. (Chu, C..) [3] | D'Ariano, A. (D'Ariano, A..) [4]

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Abstract:

As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

Autonomous truck carbon emissions lane reservation multi-objective optimisation two-stage algorithm

Community:

  • [ 1 ] [Xu L.]School of Economics and Management, Fuzhou University, Fuzhou, China
  • [ 2 ] [Xu L.]ESIEE Paris, COSYS-GRETTIA, Univ Gustave Eiffel, Marne-la-Vallée, France
  • [ 3 ] [Wu P.]School of Economics and Management, Fuzhou University, Fuzhou, China
  • [ 4 ] [Chu C.]ESIEE Paris, COSYS-GRETTIA, Univ Gustave Eiffel, Marne-la-Vallée, France
  • [ 5 ] [D'Ariano A.]Department of Engineering, Roma Tre University, Rome, Italy

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Source :

International Journal of Production Research

ISSN: 0020-7543

Year: 2024

Issue: 23

Volume: 62

Page: 8239-8259

7 . 0 0 0

JCR@2023

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 2

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