A Humanitarian Logistics-Based Planning for Rescue and Relief Operation After a Devastating Fire Accident

Urban road tunnels are a reasonable remedy for inconvenience due to congested road traffic. However, they bring specific threats, especially those related to the possibility of fire outbreak. This work is a case study for selected urban road tunnels. Considering tunnel specificity, road traffic intensity, and structure and based on the literature data for vehicle fire probability, the chances of a fire accident were estimated for selected tunnels in Poland. It was shown that low power tunnel fires could be expected in the 10–20-year time horizon. Although such threats cannot be disregarded, tunnel systems are designed to cope with them. The chances of a disastrous fire accident were estimated as well. Such events can occur when an HGV with flammable goods or a tanker are involved. Such accidents are fortunately very rare, but, on the other hand, that is the reason why the available data are scanty and burdened with high uncertainty. Therefore, a discussion on the reliability of the obtained results is also provided.

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Reinforcement learning approach for resource allocation in humanitarian logistics

Expert Systems with Applications ◽

10.1016/j.eswa.2021.114663 ◽

2021 ◽

Vol 173 ◽

pp. 114663

Author(s):

Lina Yu ◽

Canrong Zhang ◽

Jingyan Jiang ◽

Huasheng Yang ◽

Huayan Shang

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Humanitarian Logistics ◽

Learning Approach

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Long-Term Economic Sustainability of Humanitarian Logistics—A Multi-Level and Time-Series Data Envelopment Analysis

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052219 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2219

Author(s):

Matthias Klumpp ◽

Dominic Loske

Keyword(s):

Data Envelopment Analysis ◽

Time Series Data ◽

Malmquist Index ◽

Developed Countries ◽

Efficiency Analysis ◽

Humanitarian Logistics ◽

Series Data ◽

Data Envelopment ◽

Least Developed Countries ◽

Window Analysis

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.

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