Long Haul Logistics Using Electric Trailers by Incorporating an Energy Consumption Meta-Model Into Agent-Based Model

Dhanan Sarwo Utomo; Adam Gripton; Philip Greening

doi:10.1109/WSC48552.2020.9383999

Long Haul Logistics Using Electric Trailers by Incorporating an Energy Consumption Meta-Model Into Agent-Based Model

Dhanan Sarwo Utomo, Adam Gripton, Philip Greening

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper presents the preliminary result of an Agent-Based Modeling Study (ABMS) that analyzes an electrification strategy for the UK’s long haul logistics operations. Because long haul logistics is very energy intensive, the dynamics of the trailer’s energy consumption must be taken into account. Engineering approaches are computationally expensive and inhibit us from modeling interactions within the entire fleet of trailers. This paper proposes an alternative approach to model the vehicle’s energy consumption. Our model validation shows that the ABMS can replicate a real world operator’s operations with sufficient accuracy. Subsequently we use our ABMS to evaluate the potential benefits of using electric trailers in the operator’s fleet.

Original language	English
Title of host publication	2020 Winter Simulation Conference (WSC)
Publisher	IEEE
Pages	147-158
Number of pages	12
ISBN (Electronic)	9781728194998
DOIs	https://doi.org/10.1109/WSC48552.2020.9383999
Publication status	Published - 29 Mar 2021
Event	Winter Simulation Conference 2020 : Simulation Drives Innovation - virtual Duration: 14 Dec 2020 → 18 Dec 2020 http://meetings2.informs.org/wordpress/wsc2020/

Conference

Conference	Winter Simulation Conference 2020
Abbreviated title	WSC20
Period	14/12/20 → 18/12/20
Internet address	http://meetings2.informs.org/wordpress/wsc2020/

Keywords

agent-based model
supply chain
logistics
electric vehicles

ASJC Scopus subject areas

Software
Computer Science Applications
Modelling and Simulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/WSC48552.2020.9383999

https://informs-sim.org/wsc20papers/012.pdf

Cite this

@inproceedings{e35fb2aed8864420b2384ea6da75495f,

title = "Long Haul Logistics Using Electric Trailers by Incorporating an Energy Consumption Meta-Model Into Agent-Based Model",

abstract = "This paper presents the preliminary result of an Agent-Based Modeling Study (ABMS) that analyzes an electrification strategy for the UK{\textquoteright}s long haul logistics operations. Because long haul logistics is very energy intensive, the dynamics of the trailer{\textquoteright}s energy consumption must be taken into account. Engineering approaches are computationally expensive and inhibit us from modeling interactions within the entire fleet of trailers. This paper proposes an alternative approach to model the vehicle{\textquoteright}s energy consumption. Our model validation shows that the ABMS can replicate a real world operator{\textquoteright}s operations with sufficient accuracy. Subsequently we use our ABMS to evaluate the potential benefits of using electric trailers in the operator{\textquoteright}s fleet.",

keywords = "agent-based model, supply chain, logistics, electric vehicles",

author = "Utomo, {Dhanan Sarwo} and Adam Gripton and Philip Greening",

year = "2021",

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doi = "10.1109/WSC48552.2020.9383999",

language = "English",

pages = "147--158",

booktitle = "2020 Winter Simulation Conference (WSC)",

publisher = "IEEE",

address = "United States",

note = "Winter Simulation Conference 2020 : Simulation Drives Innovation, WSC20 ; Conference date: 14-12-2020 Through 18-12-2020",

url = "http://meetings2.informs.org/wordpress/wsc2020/",

}

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T1 - Long Haul Logistics Using Electric Trailers by Incorporating an Energy Consumption Meta-Model Into Agent-Based Model

AU - Utomo, Dhanan Sarwo

AU - Gripton, Adam

AU - Greening, Philip

PY - 2021/3/29

Y1 - 2021/3/29

N2 - This paper presents the preliminary result of an Agent-Based Modeling Study (ABMS) that analyzes an electrification strategy for the UK’s long haul logistics operations. Because long haul logistics is very energy intensive, the dynamics of the trailer’s energy consumption must be taken into account. Engineering approaches are computationally expensive and inhibit us from modeling interactions within the entire fleet of trailers. This paper proposes an alternative approach to model the vehicle’s energy consumption. Our model validation shows that the ABMS can replicate a real world operator’s operations with sufficient accuracy. Subsequently we use our ABMS to evaluate the potential benefits of using electric trailers in the operator’s fleet.

AB - This paper presents the preliminary result of an Agent-Based Modeling Study (ABMS) that analyzes an electrification strategy for the UK’s long haul logistics operations. Because long haul logistics is very energy intensive, the dynamics of the trailer’s energy consumption must be taken into account. Engineering approaches are computationally expensive and inhibit us from modeling interactions within the entire fleet of trailers. This paper proposes an alternative approach to model the vehicle’s energy consumption. Our model validation shows that the ABMS can replicate a real world operator’s operations with sufficient accuracy. Subsequently we use our ABMS to evaluate the potential benefits of using electric trailers in the operator’s fleet.

KW - agent-based model

KW - supply chain

KW - logistics

KW - electric vehicles

UR - http://www.scopus.com/inward/record.url?scp=85103904145&partnerID=8YFLogxK

U2 - 10.1109/WSC48552.2020.9383999

DO - 10.1109/WSC48552.2020.9383999

M3 - Conference contribution

SP - 147

EP - 158

BT - 2020 Winter Simulation Conference (WSC)

PB - IEEE

T2 - Winter Simulation Conference 2020

Y2 - 14 December 2020 through 18 December 2020

ER -

Long Haul Logistics Using Electric Trailers by Incorporating an Energy Consumption Meta-Model Into Agent-Based Model

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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