Greedy Neighborhood Search for Disaster Relief and Evacuation Logistics
IEEE Intelligent Systems
Optimal scheduling of emergency roadway repair and subsequent relief distribution
Computers and Operations Research
Path selection model and algorithm for emergency logistics management
Computers and Industrial Engineering
A Hybrid Heuristic Algorithm for Large Scale Emergency Logistics
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 03
Expert Systems with Applications: An International Journal
A multi-criteria optimization model for humanitarian aid distribution
Journal of Global Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multiple-resource and multiple-depot emergency response problem considering secondary disasters
Expert Systems with Applications: An International Journal
Method of Inequality-Based Multiobjective Genetic Algorithm for Domestic Daily Aircraft Routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi-Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in 'time to delivery' by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.