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Route and speed optimization for autonomous trucks

Route and speed optimization for autonomous trucks
Route and speed optimization for autonomous trucks
Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed.
0305-0548
89-101
Nasri, Moncef Ilies
3dac3fcf-1108-45db-974b-a1792af863b3
Bektas, Tolga
0db10084-e51c-41e5-a3c6-417e0d08dac9
Laporte, Gilbert
b8210b8f-e942-4c5c-98b1-b55bd916aa70
Nasri, Moncef Ilies
3dac3fcf-1108-45db-974b-a1792af863b3
Bektas, Tolga
0db10084-e51c-41e5-a3c6-417e0d08dac9
Laporte, Gilbert
b8210b8f-e942-4c5c-98b1-b55bd916aa70

Nasri, Moncef Ilies, Bektas, Tolga and Laporte, Gilbert (2018) Route and speed optimization for autonomous trucks. Computers & Operations Research, 100, 89-101. (doi:10.1016/j.cor.2018.07.015).

Record type: Article

Abstract

Autonomous vehicles, and in particular autonomous trucks (ATs), are an emerging technology that is becoming a reality in the transportation sector. This paper addresses the problem of optimizing the routes and the speeds of ATs making deliveries under uncertain traffic conditions. The aim is to reduce the cost of emissions, fuel consumption and travel times. The traffic conditions are represented by a discrete set of scenarios, using which the problem is modeled in the form of two-stage stochastic programming formulations using two different recourse strategies. The strategies differ in the amount of information available during the decision making process. Computational results show the added value of stochastic modeling over a deterministic approach and the quantified benefits of optimizing speed.

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Accepted/In Press date: 18 July 2018
e-pub ahead of print date: 20 July 2018
Published date: December 2018

Identifiers

Local EPrints ID: 422811
URI: http://eprints.soton.ac.uk/id/eprint/422811
ISSN: 0305-0548
PURE UUID: a0836e46-4b07-453b-a5f4-eba7b4c5fa57
ORCID for Tolga Bektas: ORCID iD orcid.org/0000-0003-0634-144X

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Date deposited: 06 Aug 2018 16:30
Last modified: 08 Oct 2020 04:16

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Contributors

Author: Moncef Ilies Nasri
Author: Tolga Bektas ORCID iD
Author: Gilbert Laporte

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