A fast-response mathematical programming approach for delivering disaster relief goods: an earthquake case study

ABSTRACT

This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach.

KEYWORDS:

• Disaster management
• humanitarian logistics
• location
• two-stage stochastic programming
• branch and cut
• Benders decomposition

Acknowledgments

The authors are very grateful to the anonymous reviewers, the journal editor-in-chief, and associate editors, whose constructive comments and insightful critiques resulted in a much-improved paper. We also thank those who provided comments on earlier versions of this paper. In this study, we have benefited from the consultations and collaborations of the Geophysics organization in Iran. The authors very gratefully acknowledge their support for the information provided to us for the real-world case study.

Disclosure statement

No potential conflict of interest was reported by the author(s).