Budget allocation of food procurement for natural disaster response
Budget allocation of food procurement for natural disaster response
This paper studies a variant of the lot sizing problem that arises in the context of disaster management. In this problem, a fixed budget has to be allocated efficiently over multiple time periods to procure large quantities of a staple food that will be stored and later delivered to people affected by disaster strikes whose numbers are unknown in advance. Starting from the deterministic model where perfect information is assumed, different formulations to address the uncertainties are constructed: classical robust optimisation, risk-minimisation stochastic programming, and adjustable robust optimisation. Experiments conducted using data from West Java, Indonesia allow us to discuss the advantages and drawbacks of each method. Our methods constitute a toolbox to support decision makers with making procurement decisions and answering managerial questions such as which annual budget is fair and safe, or when storage peaks are likely to occur.
disaster management, humanitarian logistics, procurement lot sizing, robust optimisation, stochastic programming
754-768
Dang, Duc-Cuong
e894d36c-bcc7-4f97-abd7-ab0f35f9181c
Currie, Christine S. M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Chaerani, Diah
079ace6d-135f-4fbf-b01d-de9d6d88c9fc
Achmad, Audi
73a7c1d8-9cc4-4be7-b8df-1100b1bd13c9
1 December 2023
Dang, Duc-Cuong
e894d36c-bcc7-4f97-abd7-ab0f35f9181c
Currie, Christine S. M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Chaerani, Diah
079ace6d-135f-4fbf-b01d-de9d6d88c9fc
Achmad, Audi
73a7c1d8-9cc4-4be7-b8df-1100b1bd13c9
Dang, Duc-Cuong, Currie, Christine S. M., Onggo, Bhakti Stephan, Chaerani, Diah and Achmad, Audi
(2023)
Budget allocation of food procurement for natural disaster response.
European Journal of Operational Research, 311 (2), .
(doi:10.1016/j.ejor.2023.05.015).
Abstract
This paper studies a variant of the lot sizing problem that arises in the context of disaster management. In this problem, a fixed budget has to be allocated efficiently over multiple time periods to procure large quantities of a staple food that will be stored and later delivered to people affected by disaster strikes whose numbers are unknown in advance. Starting from the deterministic model where perfect information is assumed, different formulations to address the uncertainties are constructed: classical robust optimisation, risk-minimisation stochastic programming, and adjustable robust optimisation. Experiments conducted using data from West Java, Indonesia allow us to discuss the advantages and drawbacks of each method. Our methods constitute a toolbox to support decision makers with making procurement decisions and answering managerial questions such as which annual budget is fair and safe, or when storage peaks are likely to occur.
Text
EJOR-D-22-01464_R2
- Accepted Manuscript
Text
1-s2.0-S0377221723003788-main
- Proof
More information
Accepted/In Press date: 8 May 2023
e-pub ahead of print date: 13 May 2023
Published date: 1 December 2023
Additional Information:
Funding Information:
This research is funded by the UK EPSRC GCRF grant number EP/T00360X/1, the support and availability of Prof. Anna Nagurney and from the other members of the advisory board of this project are fully acknowledged. The authors would like to thank Dr. Stefano Coniglio and colleagues from CORMSIS at the University of Southampton for fruitful discussions. The use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work is fully acknowledged. The aggregated historical data and scenarios are available from https://github.com/stephanong/relief-ops/. The data which were used in simulating these scenarios are provided by the West Java Government, the West Java Regional Disaster Management Agency (BPBD), the Bureau of Logistics (BULOG) of West Java, and the Indonesian National Agency for Disaster Countermeasure (BNPB), their support is fully acknowledged.
Funding Information:
This research is funded by the UK EPSRC GCRF grant number EP/T00360X/1, the support and availability of Prof. Anna Nagurney and from the other members of the advisory board of this project are fully acknowledged. The authors would like to thank Dr. Stefano Coniglio and colleagues from CORMSIS at the University of Southampton for fruitful discussions. The use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work is fully acknowledged. The aggregated historical data and scenarios are available from https://github.com/stephanong/relief-ops/ . The data which were used in simulating these scenarios are provided by the West Java Government, the West Java Regional Disaster Management Agency (BPBD), the Bureau of Logistics (BULOG) of West Java, and the Indonesian National Agency for Disaster Countermeasure (BNPB), their support is fully acknowledged.
Publisher Copyright:
© 2023 The Authors
Keywords:
disaster management, humanitarian logistics, procurement lot sizing, robust optimisation, stochastic programming
Identifiers
Local EPrints ID: 477469
URI: http://eprints.soton.ac.uk/id/eprint/477469
ISSN: 0377-2217
PURE UUID: 165b2426-050c-4597-8466-074de61258a5
Catalogue record
Date deposited: 06 Jun 2023 17:14
Last modified: 18 Mar 2024 03:50
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Contributors
Author:
Diah Chaerani
Author:
Audi Achmad
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