TY - JOUR

T1 - A comprehensive evacuation planning model and genetic solution algorithm

AU - Goerigk, Marc

AU - Deghdak, Kaouthar

AU - Heßler, Philipp

PY - 2014/11

Y1 - 2014/11

N2 - We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.

AB - We consider the problem of evacuating an urban area. Several planning aspects need to be considered in such a scenario, which are usually considered separately. We propose a macroscopic multi-criteria optimization model that includes several such questions simultaneously, and develop a genetic algorithm to solve the problem heuristically. Its applicability is extended by also considering how to aggregate instance data, and how to generate solutions for the original instance starting from a reduced solution. In computational experiments using real-world data, we demonstrate the effectiveness of our approach and compare different levels of data aggregation.

KW - Data aggregation

KW - Disaster management

KW - Evacuation planning

KW - Location-routing

KW - Multi-criteria genetic algorithms

U2 - 10.1016/j.tre.2014.08.007

DO - 10.1016/j.tre.2014.08.007

M3 - Journal article

AN - SCOPUS:84907811722

VL - 71

SP - 82

EP - 97

JO - Transportation Research Part E: Logistics and Transportation Review

JF - Transportation Research Part E: Logistics and Transportation Review

SN - 1366-5545

ER -