Development of fuzzy RUASP model - Grasp metaheuristics with time window: Case study of Mount Semeru eruption in East Java

Due to its geographical location, Indonesia is very vulnerable to the volcano eruption. Volcanic eruptions always bring impact to the surrounding community. To mitigate the negative effects, especially the impact on the safety of the population, disaster management is required. One of the concerns o...

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Bibliographic Details
Published in:IOP conference series. Earth and environmental science 2019-02, Vol.235 (1), p.12081
Main Authors: Santoso, A, Sutanto, R A P, Prayogo, D N, Parung, J
Format: Article
Language:English
Subjects:
Algorithms
Completion time
Disaster management
Disasters
Emergency preparedness
Evacuation
Geographical distribution
Geographical locations
Heuristic methods
Historical account
Model testing
Optimization
Safety
Scheduling
Teams
Volcanic activity
Volcanic eruptions
Volcanoes
Windows (intervals)
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Summary:Due to its geographical location, Indonesia is very vulnerable to the volcano eruption. Volcanic eruptions always bring impact to the surrounding community. To mitigate the negative effects, especially the impact on the safety of the population, disaster management is required. One of the concerns of disaster management is a proper allocation and scheduling of rescue teams to minimize evacuation time with expectations of saving as many lives as possible. For that purpose, the RUASP (Rescue Unit Assignment and Scheduling Problem) optimization model is developed to minimize a completion time of evacuation. The model considers the severity of damages and capability of the rescue team as well as safety time for the rescue team. There is a period for rescue teams to be able to perform their duties safely and fuzzy time on the fields. Physical limitations and device limitations are the inhibiting factors for evacuation activities during low light conditions. We also developed a GRASP Metaheuristics algorithm for solving the proposed RUASP model. To test the model we use a certain historical data. The completion time of the proposed model is 21.92% faster than the computation time of the previous model, 12.56 seconds.
ISSN:1755-1307
1755-1315
1755-1315
DOI:10.1088/1755-1315/235/1/012081