A robust stochastic Casualty Collection Points location problem

Alizadeh, Morteza, Amiri-Aref, Mehdi, Mustafee, Navonil and Matilal, Sumohon (2019) A robust stochastic Casualty Collection Points location problem. European Journal of Operational Research (EJOR), 279 (3). pp. 965-983. ISSN 0377-2217

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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 are improved, but also the number of lives lost is decreased. Moreover, it is shown that the proposed robust stochastic optimization approach converges 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.

Item Type: Article
Keywords: Humanitarian logistics, Casualty Collection Points, Stochastic programming, Robust optimization, OR in disaster relief
Schools and Departments: University of Sussex Business School > Accounting and Finance
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 23 Nov 2020 08:05
Last Modified: 06 Jan 2021 10:11
URI: http://sro.sussex.ac.uk/id/eprint/95223

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