Research Of Logistics Inventory Decision On Immune Algorithm

In the recent years E-Commerce has been developed rapidly. Logistics distribution is always the basis of E-Commerce, and then distributed inventory is the basis of Logistics distribution. Chain Enterprise has unique advantages, such as business network covering every place, the overall effect of large-scale, specialization, standardization, information technology, which provide a strong guarantee for logistics and distribution.As the cache of supply and demand, controlling and scheduling Inventory directly relates to supply chain performance. Nowadays, more and more enterprises recognize that reducing logistics costs particularly Inventory costs has come to be the source of corporate profits in the premise of meeting customer needs.After analyzing the control and dispatch of distributed inventory in agile supply chain, the framework and key technologies of distributed inventory control and dispatch are provided by considering all factors related to the distributed inventory model from an overall perspective.Under the constraint of inventory capacity, supply ability, capital ability, etc., the model of inventory order and inventory dispatch under ability constraints is presented. On the basis of assuring certain degree of customer satisfaction, the total cost of inventory is minimized. And also the model extends some traditional inventory control models, so that it has more universality and practicability.In this thesis, two kinds of antibody encode are put forward for the model of inventory order and inventory dispatch, which can be solved by Immune genetic algorithm and random simulation approach.To develop the assistant decision and assistant inventory control system is a very important aspect in the research of inventory control. The system should have higher reliability and maneuverability. In this thesis, an algorithm of distributed inventory control and dispatch in agile supply chain is accomplished, and some instance analyses are also provided, which show that the method provided is feasible and efficient.