Study On Emergency Logistics Vehicle Routing Mode Based On The Clonal Immune Algorithm

In recent years, people at home and abroad continued to suffer the invasion of unexpected events. When unexpected events occur, how to develop programs for emergency supplies and personnel distribution in the shortest possible time, and how to make the damage of people's lives and property and the unexpected events to a minimum, is the core issue of the emergency management system. Relying on the project"Research of emergency logistics scheduling model and algorithm based on axis-spoke network"from Liaoning Province Natural Science Foundation, taking a specific operation of a regional emergency logistics center as the actual study background, this paper makes deep study of the model and solution about the emergency logistics vehicle routing multi-objective optimization which transportation of the loaded vehicle is by road. The research has some theoretical significance and application value.Firstly, based on the review of the research situation about the emergency logistics vehicle routing problem at home and aboard, this paper summarized the five characteristics of emergency logistics, introduced the model, algorithm and goal for algorithm design of the multi-objective problem which providing basic data for establishing new model and solution.Secondly, this paper set the shortest total time and the minimal total cost of the system as the target, focusing on establishing time effect model of emergency logistics. For the presence of the problem that a series of multi-objective optimization cannot be compared, issues such as Pareto-optimal solution, this paper provides the model of immune clonal multi-objective optimization based on the immune clonal algorithm which solves the problem of emergency logistics vehicle routing. Further, taking into account the fact that the constraints of the model are too much and the shortcomings of antibody diversity in cloning operation, in order to improve the convergence rate of finding global optimal solution, this paper provides the improved immune clonal multi-objective unconstrained optimization model. Finally, this paper verifies the feasibility and effectiveness of the above two models through simulation of examples. Based on the comparative analysis of two models, the paper indicates the fact that the convergence speed and run time of the improved immune clonal multi-objective optimization model are better than the previous model which providing new approach to deal with complex issues.