910. Integrating Heterogeneous Suffering in Disaster Response: A Robust Estimation and Optimization Framework
Invited abstract in session TB-55: Robust and Stochastic Models in Disaster Management, stream Humanitarian Operations.
Tuesday, 10:30-12:00Room: Liberty 1.09
Authors (first author is the speaker)
| 1. | Yuxin Hong
|
| Tianjin Uinversity | |
| 2. | Ning Zhu
|
| Tianjin University |
Abstract
The increasing frequency and severity of natural disasters present significant challenges for effective humanitarian logistics, especially in the aftermath of the disaster. In this paper, we propose a joint vehicle routing and supply allocation model that integrates an extended area-level Deprivation Cost Function (DCF) to mitigate human suffering. Moreover, we incorporate regression residuals to capture heterogeneity in suffering, thereby providing a nuanced assessment of population-level impacts and enhancing resource prioritization for vulnerable communities.To address estimation uncertainties inherent in data collection and the potential ambiguity in residual distribution,we propose a robust satisficing model that accounts for these uncertainties and approximates it as a finite-dimensional program. Leveraging the structure of the cost function, we develop an efficient L-shaped method and introduce a conservative approximation to enhance computational scalability. Extensive numerical experiments validate the algorithm's efficiency and demonstrate the model's robustness in mitigating estimation errors and distribution ambiguities.The comparison of different strategies reveals that accounting for heterogeneous suffering leads to more balanced and effective relief operations. Additionally, strategically prioritizing aid to less resilient areas, even at the cost of moderate distribution imbalances, significantly reduces extreme suffering without compromising overall fairness.
Keywords
- Disaster and Crisis Management
- Algorithms
- Robust Optimization
Status: accepted
Back to the list of papers