Abstract
| Language | English |
|---|---|
| Pages | 334-348 |
| Journal | European Journal of Operational Research |
| Volume | 252 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Jul 2016 |
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Keywords
- Scheduling
- Combinatorial optimization
- Emergency response
- Disaster management
- Dynamic
Cite this
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Online optimization of casualty processing in major incident response: An experimental analysis. / Wilson, Duncan; Hawe, Glenn; Coates, Graham; Crouch, Roger.
In: European Journal of Operational Research, Vol. 252, No. 1, 01.07.2016, p. 334-348.Research output: Contribution to journal › Article
TY - JOUR
T1 - Online optimization of casualty processing in major incident response: An experimental analysis
AU - Wilson, Duncan
AU - Hawe, Glenn
AU - Coates, Graham
AU - Crouch, Roger
PY - 2016/7/1
Y1 - 2016/7/1
N2 - When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility.
AB - When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility.
KW - Scheduling
KW - Combinatorial optimization
KW - Emergency response
KW - Disaster management
KW - Dynamic
U2 - 10.1016/j.ejor.2016.01.021
DO - 10.1016/j.ejor.2016.01.021
M3 - Article
VL - 252
SP - 334
EP - 348
JO - European Journal of Operational Research
T2 - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 1
ER -