Greedy-Search-based Multi-Objective Genetic Algorithm for Emergency Humanitarian Logistics Scheduling

• Main Authors: Fu-Sheng Chang, 張福生
• Other Authors: Chung-Nan Lee
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/wp7ygu

博士 === 國立中山大學 === 資訊工程學系研究所 === 104 === To enable the immediate and efficient dispatch of relief to victims of disaster, this thesis proposes a greedy-search-based multi-objective genetic algorithm (GSMOGA) that is capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm merges the features of local search ability of the greedy method and the diversity of multi-objective genetic algorithm to enhance local search speed and diversity explore ability. It uses the Google Map to draw up the available roads which connect the demand points and supply points and applies the Dijkstra algorithm to find the shortest path between each demand point and supply point. It also dynamically adjust distribution schedules from various supply points according to the requirements at demand points, and adopts the NSGAII method to perform rank & sort procedure to find the feasible solution schedules on non-dominated Pareto front in order to minimize the following: unsatisfied demand for resources, time to delivery, and transportation costs. The sequence of three objectives are also applied to be the priority sequence to generate and order routing schedules for the decision maker. The algorithm uses the case of the Chi-Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that with a limited and unlimited number of available vehicles, the proposed algorithm outperforms the multi-objective genetic algorithm (MOGA) and the standard greedy algorithms in ‘time to delivery’ by 56.16% and 64.11%, respectively under the 10,000 generations and average situation. The final routing figures show that the GSMOGA is more comprehensive in the emergency logistics scheduling problem. We study the effect of different crossover methods on the performance of GSMOGA. The results show that order based crossover performs the best. We verify the correctness of GSMOGA by comparing the result using the brute force approach.