Distribution Network Inventory Coordination Strategy Under Stochastic Demand And Genetic Algorithm

Distribution network optimization is through the integration of the dis-tribution network function on each node, so as to realize the maximization ofcorporate interests.Genetic algorithm is a simulated process of natural evolution to searchthe optimal solution method, has a strong global search ability and robust-ness,performance is superior in solving combinatorial optimization problems.This paper introduces the basic concepts and related theories of geneticalgorithm, and the selection, crossover and mutation operations of geneticalgorithm are introduced in detail. In this paper,the distribution network op-timization combines with inheritance algorithm, a distribution network modeljointed location with stochastic demand is established, which take account offacility fxed costs of the distribution centers, the transportation costs and in-ventory costs of the distribution centers and distribution points. The modeltakes the distribution center service level and inventory capacity as constraintconditions, and assume the distribution centers and distribution points usingthe continuous review (R,Q) inventory control strategy、customer needs obeythe standard normal distribution. For the model, a new appropriate chromo-some encoding technique and genetic operation strategies are given to achievea solution of the model. The numerical simulation results show that the modelis appropriate, and the algorithm is efective and fexible. The paper is dividedinto fve chapters:The frst chapter, the paper introduces the development background and the course of distribution network, and also made an overview of the relevanttheory of inventory control.The second chapter, describes the development process of genetic algo-rithm, both at home and abroad and their research results, and introducesrelated theory and the main operation of genetic algorithm.The third chapter, the site selection of distribution network under stochas-tic demand inventory joint model is established, and then the genetic algorithmis designed on the basis of this model.The fourth chapter, example analysis. Examples using Matlab7.0pro-gramming and calculation is solved, so as to get the optimal distribution cen-ter’s location and the corresponding transportation scheme, and the numericalresults shows that, the design of genetic algorithm is efective and reliable.The ffth chapter, summarize.