Abstract
Flood evacuation operations face a difficult task in moving affected people to safer locations. Uneven distributions of transport, untimely assistance and poor coordination at the operation level are among the main problems in the evacuation process. This is attributed to the lack of research focus on evacuation vehicle routing. This paper proposes an improved discrete particle swarm optimization (DPSO) with a random particle priority value and decomposition procedure as a searching strategy to solve evacuation vehicle routing problem (EVRP). The search strategies are proposed to reduce the searching space of the particles to avoid local optimal problem. This algorithm was computationally experimented with different number of potentially flooded areas, various types of vehicles, and different speed of vehicles with DPSO and genetic algorithm (GA). The findings show that an improved DPSO with a random particle priority value and decomposition procedure is highly competitive. It offers outstanding performance in its fitness value (total travelling time) and processing time.
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Yusoff, M., Ariffin, J., Mohamed, A. (2012). DPSO Based on Random Particle Priority Value and Decomposition Procedure as a Searching Strategy for the Evacuation Vehicle Routing Problem. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_82
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DOI: https://doi.org/10.1007/978-3-642-34487-9_82
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