A robust stochastic casualty collection points location problem
dc.contributor.author | Alizadeh, M | |
dc.contributor.author | Mehdi, A-A | |
dc.contributor.author | Mustafee, N | |
dc.contributor.author | Matilal, S | |
dc.date.accessioned | 2019-06-24T11:35:22Z | |
dc.date.issued | 2019-06-14 | |
dc.description.abstract | In this paper, a Casualty Collection Points (CCPs) location problem is formulated as a two-stage robust stochastic optimization model in an uncertain environment. In this modelling approach, the network design decisions are integrated with the multi-period response operational decisions where the number of casualties with different levels of injuries coming from the affected areas is uncertain. Furthermore, the transportation capacity for the evacuation of casualties to CCPs and hospitals is also uncertain. To solve this complex problem, a robust sample average approximation method with the feasibility restoration technique is proposed, and its efficiency is examined through a statistical validation procedure. We then evaluate the proposed methodology in the backdrop of a hypothetical case of Bhopal gas tragedy (with the same hazard propagation profile) at the present day. We also report the solution robustness and model robustness of 144 instances of the case-study to show the proficiency of our proposed solution approach. Results analysis reveals that our modelling approach enables the decision makers to design a humanitarian logistic network in which not only the proximity and accessibility to CCPs is improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach convergences rapidly and more efficiently. We hope that our methodology will encourage urban city planners to pre-identify CCP locations, and, in the event of a disaster, help them decide on the subset of these CCPs that could be rapidly mobilised for disaster response. | en_GB |
dc.identifier.citation | Published online 14 June 2019 | en_GB |
dc.identifier.doi | 10.1016/j.ejor.2019.06.018 | |
dc.identifier.uri | http://hdl.handle.net/10871/37644 | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2019 The Authors. Published by Elsevier B.V. Open access: Published under a Creative Commons license: https://creativecommons.org/licenses/by/4.0/ | en_GB |
dc.subject | Humanitarian logistics | en_GB |
dc.subject | Casualty collection points | en_GB |
dc.subject | Stochastic programming | en_GB |
dc.subject | Robust optimization | en_GB |
dc.subject | OR in disaster relief | en_GB |
dc.title | A robust stochastic casualty collection points location problem | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-06-24T11:35:22Z | |
dc.identifier.issn | 0377-2217 | |
dc.identifier.journal | European Journal of Operational Research | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-06-10 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-06-14 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-06-21T18:52:40Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-06-24T11:35:28Z | |
refterms.panel | C | en_GB |
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Except where otherwise noted, this item's licence is described as © 2019 The Authors. Published by Elsevier B.V. Open access: Published under a Creative Commons license: https://creativecommons.org/licenses/by/4.0/