Connecting a population dynamic model with a multi-period location-allocation problem for post-disaster relief operations

The SADC region has seen both man-made and natural disasters killing over 90 thousand people and affecting millions in the past 33 years. Most of these deaths were as a result of lack of infrastructure and preparedness. Looking at the challenges for providing relief to victims/evacuees throughout the entire disaster and post-disaster periods in the region, the emphasis of this thesis is on last mile transportation of resources, victims, emergency supplies, aiming to optimize the effectiveness (quickI response) and efficiency (low-cost) of logistics activities including humanitarian supply chain. A survey was used for data collection. Statistical analysis helped determine the impact of disaster relief chains and lead to the development of a mathematical model that shall equip the region with mechanisms for response and recovery operations. An EXCEL optimization tool was used to find the optimal way of transporting relief in the region in case of a disaster.

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Humanitarian supply chain management: modeling the pre and post-disaster relief operations

International Journal of Disaster Resilience in the Built Environment ◽

10.1108/ijdrbe-10-2020-0107 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sachin Agarwal ◽

Ravi Kant ◽

Ravi Shankar

Keyword(s):

Mathematical Model ◽

Supply Chain ◽

Facility Location ◽

Disaster Relief ◽

Evacuation Planning ◽

Content Type ◽

Disaster Relief Operations ◽

Relief Operations ◽

Humanitarian Supply Chain ◽

Post Disaster

Purpose This study proposed a mathematical model for decision-making in the pre- and post-disaster phases. This research aims to develop a mathematical model for three important fields in the context of humanitarian logistics; stock prepositioning, facility location and evacuation planning in the humanitarian supply chain (HSC) network design. Design/methodology/approach This study applied three optimization techniques; classical approach (CA), pattern search algorithm (PSA) and Genetic Algorithm (GA) to solve the proposed mathematical model. The proposed mathematical model attempts to minimize the total relief items supply chain cost and evacuation chain cost of the HSC. A real case study of cyclone Fani, 2019 in Orissa, India is applied to validate the proposed mathematical model and to show the performance of the model. Findings The results demonstrate that heuristic approach; PSA performs better and optimal solutions are obtained in almost all the cases as compared to the GA and CA. Research limitations/implications This study is limited to deterministic demands in the affected regions, and different scenarios of the disaster events are not considered. Social implications The finding reveals that the proposed model can help the humanitarian stakeholders in making decisions on facility location, relief distribution and evacuation planning in disaster relief operations. Originality/value The results of this study may offer managerial insights to practitioners and humanitarian logisticians who are engaged in HSC implementation.

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