The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics

Robert M. van Steenbergen; Eduardo Lalla-Ruiz; Wouter J.A. van Heeswijk; Martijn Mes

doi:10.1007/978-3-031-43612-3_17

The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics

Robert M. van Steenbergen^*, Eduardo Lalla-Ruiz, Wouter J.A. van Heeswijk, Martijn Mes

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

While distributing essential supplies in volatile environments, humanitarian transport is often exposed to threats such as attacks. To mitigate the negative consequences of attacks, we introduce the heterogeneous fleet risk-constrained vehicle routing problem (HFRCVRP), in which we aim to minimize transportation costs and the expected loss of getting robbed. An Adaptive Large Neighborhood Search (ALNS) heuristic is presented to solve the problem. The trade-off between transportation costs and expected loss of attacks is analyzed with a real-world case in South Sudan. Results show that the trade-off is especially relevant in the heterogeneous variant, in which Unmanned Aerial Vehicles (UAVs) can effectively mitigate risks of truck transport, providing a 7.1% improvement of the objective value compared to the same instance with only trucks. Risks can be completely eliminated by increasing transportation costs by a factor of five. Additionally, the risk variant decreases the objective value by 14.8% compared to considering only transportation costs and ignoring risks.

Original language	English
Title of host publication	Computational Logistics
Subtitle of host publication	14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings
Editors	Joachim R. Daduna, Gernot Liedtke, Xiaoning Shi, Stefan Voß
Publisher	Springer
Pages	276-291
Number of pages	16
ISBN (Electronic)	978-3-031-43612-3
ISBN (Print)	978-3-031-43611-6
DOIs	https://doi.org/10.1007/978-3-031-43612-3_17
Publication status	Published - 7 Sept 2023
Event	14th International Conference on Computational Logistics, ICCL 2023 - German Aerospace Center (DLR) , Berlin, Germany Duration: 6 Sept 2023 → 8 Sept 2023 Conference number: 14

Publication series

Name	Lecture Notes in Computer Science
Volume	14239

Conference

Conference	14th International Conference on Computational Logistics, ICCL 2023
Abbreviated title	ICCL 2023
Country/Territory	Germany
City	Berlin
Period	6/09/23 → 8/09/23

Keywords

Risk-constrained Routing
Humanitarian Logistics
UAVs
Heterogeneous Fleet
Metaheuristic
2023 OA procedure

UN SDGs

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

Access to Document

10.1007/978-3-031-43612-3_17

978-3-031-43612-3_17Final published version, 1.03 MBLicence: Taverne

1 PhD Thesis - Research UT, graduation UT

Evaluation and Coordination of UAVs in Humanitarian Logistics
van Steenbergen, R. M., 13 Sept 2024, Enschede: University of Twente. 282 p.
Research output: Thesis › PhD Thesis - Research UT, graduation UT

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van Steenbergen, R. M., Lalla-Ruiz, E., van Heeswijk, W. J. A., & Mes, M. (2023). The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. In J. R. Daduna, G. Liedtke, X. Shi, & S. Voß (Eds.), Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings (pp. 276-291). (Lecture Notes in Computer Science; Vol. 14239). Springer. https://doi.org/10.1007/978-3-031-43612-3_17

van Steenbergen, Robert M. ; Lalla-Ruiz, Eduardo ; van Heeswijk, Wouter J.A. et al. / The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings. editor / Joachim R. Daduna ; Gernot Liedtke ; Xiaoning Shi ; Stefan Voß. Springer, 2023. pp. 276-291 (Lecture Notes in Computer Science).

@inproceedings{1e2fddd767a940b28300ab6a60cfe7ec,

title = "The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics",

abstract = "While distributing essential supplies in volatile environments, humanitarian transport is often exposed to threats such as attacks. To mitigate the negative consequences of attacks, we introduce the heterogeneous fleet risk-constrained vehicle routing problem (HFRCVRP), in which we aim to minimize transportation costs and the expected loss of getting robbed. An Adaptive Large Neighborhood Search (ALNS) heuristic is presented to solve the problem. The trade-off between transportation costs and expected loss of attacks is analyzed with a real-world case in South Sudan. Results show that the trade-off is especially relevant in the heterogeneous variant, in which Unmanned Aerial Vehicles (UAVs) can effectively mitigate risks of truck transport, providing a 7.1% improvement of the objective value compared to the same instance with only trucks. Risks can be completely eliminated by increasing transportation costs by a factor of five. Additionally, the risk variant decreases the objective value by 14.8% compared to considering only transportation costs and ignoring risks.",

keywords = "Risk-constrained Routing, Humanitarian Logistics, UAVs, Heterogeneous Fleet, Metaheuristic, 2023 OA procedure",

author = "{van Steenbergen}, {Robert M.} and Eduardo Lalla-Ruiz and {van Heeswijk}, {Wouter J.A.} and Martijn Mes",

note = "Funding Information: Acknowledgements. This work was funded in the course of the project VIPES (FFG project number 893963) by the Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK) as part of the call for proposals Mobilit{\"a}t der Zukunft. FFG is the central national funding organization and strengthens Austria{\textquoteright}s innovative power. We also want to thank Matthias Wastian, Senior Data Scientist at dwh GmbH, for providing all the necessary information to formulate the model and use cases to run the algorithm. Funding Information: This research was partially supported by Dicyt projects 062217QV and 062217DG, Vicerrector{\'i}a de Investigaci{\'o}n, Desarrollo e Innovaci{\'o}n, Universidad de Santiago de Chile. Funding Information: Acknowledgments. The authors acknowledge the support by Internal Funds KU Leuven and the Research Foundation Flanders (FWO) through the Strategic Basic Research project Data-driven logistics (S007318N). Funding Information: Acknowledgements. This research project is supported by the National Research Foundation, Singapore under its AI Singapore Programme (Award No: AISG2-100E-2021-089). We like to thank uParcel and AI Singapore for data, domain and comments, the ICCL PC chairs and reviewers, with special mention of Stefan Voss, for suggestions and meticulous copy-editing during the review process. Funding Information: Acknowledgement. This research was supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant. Funding Information: This work is partially sponsored by the Danish Maritime Fund under grant 2021-069. Funding Information: Acknowledgement. This work is supported by the Commission of Scientific Research Projects of Bursa Uludag University, Project Number FU˙-2022-1042. Funding Information: Acknowledgements. Special thanks go to the 187 respondents of the questionnaire, and the support of FuturePorts Project, DLR (Deutsches Zentrum f{\"u}r Luft-und Raum-fahrt, German Aerospace Center). In addition, the constructive comments provided by the anonymous referees are greatly appreciated. Funding Information: This research was conducted at the Fraunhofer Institute for Material Flow and Logistics as part of the KIK-Dispo project, funded by the Federal Ministry of Education and Research of Germany (Grant No. 01IS19041B). The responsibility for the content lies with the authors. Funding Information: Acknowledgement. The research was funded by the Bavarian State Ministry for Economic Affairs, Regional Development and Energy as part of the R&D program “Information and communication technology” of the Free State of Bavaria, project title: KAnIS: Cooperative Autonomous Intralogistics Systems. Funding Information: This work is partly funded by the Innovation Fund Denmark (IFD) under File No. 0177-00022B. Funding Information: Acknowledgements. The authors gratefully acknowledge the financial support of this project through the German Research Foundation (DFG) under the reference number 418360126. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 14th International Conference on Computational Logistics, ICCL 2023, ICCL 2023 ; Conference date: 06-09-2023 Through 08-09-2023",

year = "2023",

month = sep,

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language = "English",

isbn = "978-3-031-43611-6",

series = "Lecture Notes in Computer Science",

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van Steenbergen, RM, Lalla-Ruiz, E , van Heeswijk, WJA & Mes, M 2023, The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. in JR Daduna, G Liedtke, X Shi & S Voß (eds), Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings. Lecture Notes in Computer Science, vol. 14239, Springer, pp. 276-291, 14th International Conference on Computational Logistics, ICCL 2023, Berlin, Germany, 6/09/23. https://doi.org/10.1007/978-3-031-43612-3_17

The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. / van Steenbergen, Robert M.; Lalla-Ruiz, Eduardo ; van Heeswijk, Wouter J.A. et al.
Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings. ed. / Joachim R. Daduna; Gernot Liedtke; Xiaoning Shi; Stefan Voß. Springer, 2023. p. 276-291 (Lecture Notes in Computer Science; Vol. 14239).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics

AU - van Steenbergen, Robert M.

AU - Lalla-Ruiz, Eduardo

AU - van Heeswijk, Wouter J.A.

AU - Mes, Martijn

N1 - Conference code: 14

PY - 2023/9/7

Y1 - 2023/9/7

N2 - While distributing essential supplies in volatile environments, humanitarian transport is often exposed to threats such as attacks. To mitigate the negative consequences of attacks, we introduce the heterogeneous fleet risk-constrained vehicle routing problem (HFRCVRP), in which we aim to minimize transportation costs and the expected loss of getting robbed. An Adaptive Large Neighborhood Search (ALNS) heuristic is presented to solve the problem. The trade-off between transportation costs and expected loss of attacks is analyzed with a real-world case in South Sudan. Results show that the trade-off is especially relevant in the heterogeneous variant, in which Unmanned Aerial Vehicles (UAVs) can effectively mitigate risks of truck transport, providing a 7.1% improvement of the objective value compared to the same instance with only trucks. Risks can be completely eliminated by increasing transportation costs by a factor of five. Additionally, the risk variant decreases the objective value by 14.8% compared to considering only transportation costs and ignoring risks.

AB - While distributing essential supplies in volatile environments, humanitarian transport is often exposed to threats such as attacks. To mitigate the negative consequences of attacks, we introduce the heterogeneous fleet risk-constrained vehicle routing problem (HFRCVRP), in which we aim to minimize transportation costs and the expected loss of getting robbed. An Adaptive Large Neighborhood Search (ALNS) heuristic is presented to solve the problem. The trade-off between transportation costs and expected loss of attacks is analyzed with a real-world case in South Sudan. Results show that the trade-off is especially relevant in the heterogeneous variant, in which Unmanned Aerial Vehicles (UAVs) can effectively mitigate risks of truck transport, providing a 7.1% improvement of the objective value compared to the same instance with only trucks. Risks can be completely eliminated by increasing transportation costs by a factor of five. Additionally, the risk variant decreases the objective value by 14.8% compared to considering only transportation costs and ignoring risks.

KW - Risk-constrained Routing

KW - Humanitarian Logistics

KW - UAVs

KW - Heterogeneous Fleet

KW - Metaheuristic

KW - 2023 OA procedure

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DO - 10.1007/978-3-031-43612-3_17

M3 - Conference contribution

SN - 978-3-031-43611-6

T3 - Lecture Notes in Computer Science

SP - 276

EP - 291

BT - Computational Logistics

A2 - Daduna, Joachim R.

A2 - Liedtke, Gernot

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PB - Springer

T2 - 14th International Conference on Computational Logistics, ICCL 2023

Y2 - 6 September 2023 through 8 September 2023

ER -

van Steenbergen RM, Lalla-Ruiz E , van Heeswijk WJA , Mes M. The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics. In Daduna JR, Liedtke G, Shi X, Voß S, editors, Computational Logistics: 14th International Conference, ICCL 2023, Berlin, Germany, September 6–8, 2023, Proceedings. Springer. 2023. p. 276-291. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-43612-3_17

The Heterogeneous Fleet Risk-Constrained Vehicle Routing Problem in Humanitarian Logistics

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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Research output

Evaluation and Coordination of UAVs in Humanitarian Logistics

Cite this