With a population of 277.5 million, Indonesia is the fourth most populous country in the world. It sits on the Ring of Fire, a 25,000-mile chain of volcano and earthquake zones that encircles the Pacific Ocean. As a result, its more than 17,000 islands are prone to volcanic eruptions and earthquakes, as well as experiencing flooding, tsunamis, mudslides, wildfires and more. Indonesia deals with between 2,000 and 5,000 natural disasters every year. There is a natural disaster somewhere in the country at almost all times, from minor to major. For every tsunami that draws the world’s attention and sympathy, or volcano that creates dramatic images online, there are many other events that garner little notice outside the country. Indonesia's population are among the 4.2 billion people worldwide who felt the effects of natural disasters in one way or another in the 20 years from 2000 to 2019. Dealing with the aftermath of these events is an immense task involving national and regional response organisations, NGOs, and volunteers. The logistics are mammoth, with numerous complicating factors. MAKING A DIFFERENCE The Sendai Framework for Disaster Risk Reduction 2015-2030 identifies the enhancement of disaster preparedness for effective response as a key priority risk-reduction area. However, the available mathematical models and solution methodologies fall short of capturing the breadth and complexity of the real-world challenges concerning disaster preparedness and response problems. The available approaches are mostly based on generic assumptions that tend to oversimplify the decisionmaking needs of disaster management agencies. This problem is more pronounced in developing countries such as Indonesia, and this is where our work comes in. MODELLING DISASTER RESPONSE The strategic vision of our RESilient Emergency Preparedness for Natural Disaster Response through OR (RESPOND-OR) and RESPOND-OR X projects was to develop and implement mathematical models and solution algorithms that underpin the development of Decision Support Systems (DSS) for large scale disaster preparedness and response. Our research was focused on disaster response operations in both Indonesia and Sudan – a country affected by flooding and drought – but we will focus here on the former. The models and algorithms developed for Indonesia within the framework of RESPOND-OR are motivated by the emergency preparedness and response decision-making needs and context of Indonesia. Our team here in the Centre for Transport and Logistics (CENTRAL) at Lancaster have collaborated with academic institutions, public organisations and NGOs in Indonesia to develop solutions that work for their specific geographic, social and economic situations. You cannot take in a ready-made generic model and transfer it. You have to develop something specific to their situations. Also, there can be a scarcity of resources. How organisations use efficiently the resources they have is of the utmost importance. This is coupled with the fact that when a natural disaster occurs, it generates high demand for resources like trucks to evacuate people and livestock. Only by working closely with the enduser organisations can we understand all the specificities and complexities and optimise the use of these resources. We developed mathematical models and algorithms to help disaster response management organisations to optimise two key disaster response management decisions: assisted evacuation; and disaster response personnel routing and scheduling. ASSISTED EVACUATION Besides the loss of human lives, another major impact of natural disasters with significant socioeconomic impact is population displacement. In 2020, around 30.7 million people were displaced due to natural disasters. There are two types of evacuation: i) self-evacuation; and ii) assisted evacuation. Assisted evacuation involves people who need assistance to leave disaster zones. They might not have the transport means, so this must be provided. Our models are motivated by disaster management practice in Indonesia and consider time, fairness, and the risk of the assisted evacuation operations. They help determine the number of vehicles needed, where people should 24 |
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