Research on Emergency Material Dispatching Model for Multi-disaster Sites Under Natural Disasters

Ming Gao () and Minghai Lv ()
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Ming Gao: Liaoning Technical University, College of Business Administration
Minghai Lv: Liaoning Technical University, College of Business Administration

A chapter in Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024), 2024, pp 62-72 from Springer

Abstract: Abstract Natural disasters pose a serious threat to society and people’s lives, and the timely dispatch of emergency supplies has become a key link in mitigating the impact of disasters and safeguarding the basic livelihood of affected people. However, the diversity, uncertainty and infrastructure destruction of the affected areas make the dispatch of emergency supplies face great challenges. In this paper, a multi-objective optimisation model is developed that integrates the dispatching cost and time. The model aims to find the optimal material dispatch solution under multiple uncertain scenarios. It is validated by a major flood disaster in Hebei Province, China in 2023 as a case study. Under uncertainty conditions, this paper uses a fuzzy algorithm based on two-dimensional Euclidean distance objective assignment to analyse the multi-objective emergency resource scheduling model. By weighing the two components of the multi-objective and determining the weights, the model is transformed into a single-objective model, which is then solved using the LINGO software to obtain the basic data of resource scheduling. The results show that the model and method of this study can cope with the complexity and uncertainty of actual disaster scenarios, provide decision makers with scientific and reasonable suggestions for material dispatch, and maximise the efficiency of disaster relief in disaster-stricken areas and the life safety of the affected people.

Keywords: natural disaster; emergency supplies; dispatch optimization; multi-objective decision making; fuzzy mathematics (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-570-6_7

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