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Abstract
In this article, we study a humanitarian relief network design problem, where the demand for relief supplies in each affected area is uncertain and can be met by more than one relief facility. Given a certain cost budget, we simultaneously optimize the decisions of relief facility location, inventory pre-positioning, and relief facility to affected area assignment so as to maximize the responsiveness. The problem is formulated as a chance-constrained stochastic programming model in which a joint chance constraint is utilized to measure the responsiveness of the humanitarian relief network. We approximate the proposed model by another model with chance constraints, which can be solved based on two settings of the demand information in each affected area: (i) the demand distribution is given; and (ii) the partial demand information, e.g., the mean, the variance, and the support, is given. We use a case study of the 2014 Typhoon Rammasun to illustrate the application of the model. Computational results demonstrate the effectiveness of the solution approaches and show that the chance-constrained stochastic programming models are superior to the deterministic model for humanitarian relief network design.
Acknowledgments
We would like to thank the DE, the AE, and the referees for their constructive comments that led to this improved version.
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Notes on contributors
Jia Shu
Jia Shu is a professor in the School of Economics and Management, University of Electronic Science and Technology of China. His research focuses on logistics, transportation, and supply chain management. His research has been appeared in such journals as INFORMS Journal on Computing, Operations Research, and Production and Operations Management.
Miao Song
Miao Song is a professor in the Faculty of Business, the Hong Kong Polytechnic University. Her research focuses on the applications of operations research in logistics and supply chain management. Her research has been appeared in such journals as INFORMS Journal on Computing, Management Science, Operations Research, and Production and Operations Management.
Beilun Wang
Beilun Wang is an associate professor in the School of Computer Science, Southeast University. His research focuses on emergency logistics and machine learning. His research has been appeared in such conferences as ICML, IJCAI and AISTATS.
Jing Yang
Jing Yang is a post-graduate student in the School of Economics and Management, Southeast University. Her research focuses on emergency logistics.
Shaowen Zhu
Shaowen Zhu is a PhD student in the School of Economics and Management, Southeast University. His research focuses on supply chain management and logistics.