PERMS: An Efficient Rescue Route Planning System in Disasters

Xiaolong Xu, Lei Zhang, MARCELLO TROVATI, Francesco Palmieri, Eleana Asimakopoulou, OLAYINKA JOHNNY, NIKOLAOS BESSIS

Research output: Contribution to journalArticle (journal)peer-review

Abstract

The occurrence of natural and man-made disasters usually leads to significant social and economic disruption, as well as high numbers of casualties. Such occurrences are difficult to predict due to the huge number of parameters with mutual interdependencies which need to be investigated to provide reliable pre- dictive capabilities. In this work, we present high-Performance Emergent Res- cue Management e-System (PERMS ), an efficient rescue route planning scheme operating within a high-performance emergent rescue management system for vehicles based on the mobile cloud computing paradigm. More specifically, an emergency rescue planning problem (ERRP) is investigated as a multiple travel- ling salesman problem (MTSP), as well as a novel phased heuristic rescue route planning scheme. This consists of an obstacle constraints and task of equal division-based K-means++ clustering algorithm (OT-K-means++), which is more suitable for clustering victims in disaster environments, and a glow-worm swarm optimisation algorithm based on chaotic initialisation (GSOCI), which provides the appropriate rescue route for each vehicle. A prototype is developed to evaluate the performance of this proposed approach, which demonstrates a better performance compared to other well-known and widely used algorithms.
Original languageEnglish
Article number107667
JournalApplied Soft Computing
Volume111
Early online date1 Jul 2021
DOIs
Publication statusPublished - 1 Nov 2021

Keywords

  • Emergency rescue planning
  • K-means++
  • Clustering algorithm
  • Chaotic search
  • Glow-worm swarm optimisation

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