Optimization Of Emergency Logistics Distribution Path Based On IACA

The disaster areas need a lot of urgent supplies when massive emergency incidenthappens. Because of lacking reasonable planning emergency logistics, some areas cannot receive emergency supplies timely leading to excess supply. But some of the areas isdistributed for repeating. Then the emergency material distribution effect is notsignificant. In addition, the rescue work is difficult due to the influence factors of rescuework of uncertain disaster. Therefore, the research on emergency logistics distributionpath optimization problem has important significance for achieving the overalloptimization of emergency logistics system and improving the distribution efficiency ofthe whole logistics chain.First of all, this paper describes the research background, the significance of theresearch of emergency logistics distribution optimization problems, analyzes theresearch status at home and abroad, discusses the structure and the function definition,characteristic, the difference between the general logistics and emergency logisticssystem, The structure and function of emergency logistics system, discusses thedefinition and classification of vehicle routing problem situation and distribution of pathoptimization method; secondly, Through the impact analysis of the emergency to theemergency logistics distribution path, sets up a model for emergency logisticsdistribution path optimization highlighting the strict requirements of the emergencylogistics for the rescue time, Considering the road traffic safety, the economy in thedistribution process, randomness of natural disasters and other emergencies factors;again, this paper summarizes the basic principle of immune algorithm and ant colonyalgorithm and the advantages and disadvantages of algorithm, etc., and expounds thedesign idea of immune ant colony algorithm and basic steps; Finally, the problem issolved for using immune ant colony algorithm and ant colony algorithm, the resultsverifies the feasibility and effectiveness of the immune ant colony algorithm.