Multi-products Optimal Order Quantities For Uncertain Demand

With increasing global competition,customer-centric supply chain management model has gradually replaced the traditional management model with production and product-centric,the prodect of "Manufacturing-distribution\wholesale-retail" process and the process of participating physical activities and relationship coordination are the main task of supply chain management.Purchasing managememt is one of the most important components of the supply chain management,playing an increasing important role in value creation and growth to the whoel supply chain.The enterprise efficient purchasing management can not only reduce their cost and enhance market competitivenss,but also create their own new competitiveness,so as to ensure the fast chaning market are in a leading position.First,the theory of theinventory management and the uncertain theory are reviewed.Enterprise's characteristics are analyzed and summarized,and the problem"How to allocate the order quantities in the multiple order cycle at the same time order a varieties of goods"is proposed based on the downstream customer orders.The random demand and fuzzy random demand are respectively considered to propose the minimum operating costs with minimum order quantity,as well as customer service level,storage capacity and liquidity constraints.Then two multi-products and multi-periods order quantity optimization models which are respectively based on random demand and fuzzy random demand.Then,hybrid intelligent algorithm which is made up with random simulation or fuzzy random simulation,BP neural networks and GA is designed for the uncertain programming model.Finally,the algorithms are implemented with C++ high-level language programming and the simulation experiment of the uncertain programming model is based on part of the actual data of marketing enterprise.The algorithm experiments includes the parameter simulation of the genetic algorithm,the performance analysis of different crossover and mutation operators of genetic algorithm,the comparison of penalty strategy and repairing strategy and comparative analysis of the performance of genetic algorithm and particle swarm optimization algorithm.The model simulation experiment includes parameter sensitivity analysis of the inventory capacity,liquidity,customer service level on the order quantitiy.Simulation results validate the effectiveness and feasibility of the model and algorithms.