Dynamic Vrp Research Based On Multi-objective Immune Evolutionary Algorithm

Vehicle routing problem (VRP) is one of the hot spots in the aspect of the control study, optimization theory and operations research studies and other related disciplines. VRP is also a class of NP problems with combinatorial optimization with multiple constraints. It is difficult to be solved by conventional methods, so intelligent optimization algorithms usually is committed to the study. VRP which can be extended to our society , for example the school bus, taxi dispatch, mail, goods delivery and other logistics, as well as aviation, railway and urban scheduling of water, electricity, gas transport and so on, which is so more important on our scheduling of the daily work.In this paper, comparing with many intelligent optimization algorithms, immune evolutionary algorithm is selected as one of effective methods for the vehicle routing optimization problem. From the base study of multi-objective optimization problem, the clonal selection of immune system and theory of biological evolution are applied to multi-objective optimization calculations. At the same, introducing the memory cells of immune clone theory and the clustering method, the antibody are constantly optimized and renewed, the poor are out. The use of non-uniform mutation operation is promoted for the diversity of species antibody. The cross-cutting operation is used between two antibodies, competitive evolution is remained by antibodies affinity. The affinity of antibody and antibody among species reflects in the individual competition, the affinity of antibody and antigen curbs excessive competition and maintains extensive species. Vehicle Routing Schedule uses one of the way of dynamic vehicle routing distribution strategic planning, which is sub-module optimization strategies for more effective optimization of vehicle routing.In this paper, the contents of the main work and research results are as follows:First of all, multi-objective optimization of vehicle routing research is written from home and abroad. The variety of multi-objective optimization study and the development trend of intelligent algorithm are analyzed, as well as in the information society of the means of intelligent multi-objective VRP is very necessary.Secondly, in view of the characteristics of dynamic vehicle routing optimization, it is thought as strategic planning module that is sub-optimal strategy to deal with sub-working hours, divided into three sub-modules: order processing module, multi-objective optimization of immune evolutionary algorithm module and information storage module. It is able to store, adjust and optimize the dynamic vehicle routing problem.Finally, the simulation experiments are working for three class of the customers point distribution. Experimental results show that: the algorithm of this paper can effectively solve the multi-objective optimization of vehicle routing problem, further more the results of the previous results of the optimal algorithm has significantly improved.In this paper, the use of multi-objective immune evolutionary algorithm is got to a number of non-dominated solutions for decision-makers in the distribution of three customers point. It does not only provide a wide range of personalization options, but also conducive to the realities of decision-makers to make a better basis for decision-making. For our lives and work, It is VRP that provides a brand-new ways and means.