Research On Model And Algorithm For Uncertain Arrival Time Of Multi-products Optimal Order Quantities

With the increasing of global competition, customer-centric supply chain management model is gradually replaced the traditional management model with production and product-centric, the product 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 management is one of the most important components of the supply chain management, playing an increasing important role in value creation and growth to the whole supply chain. The enterprise efficient purchasing management can not only reduce their cost and enhance market competitiveness, but also create their own new competitiveness, so as to ensure the fast changing market are in a leading position.Firstly, this thesis reviews the theory of the inventory management, the procurement management and the uncertain theory. Analyze and summarize one enterprise’s characteristics. The uncertainty in the supply chain lead to the uncertain arrival time as well as the uncertain arrive quantity and based on the historical data and expert experience, the thesis proposed the problem "How to allocate the order quantities in the multiple order cycle at the same time order a varieties of goods with an uncertain arrival time". The problem considers the determining demand based on the downstream customer orders, the minimum order quantity limited, storage capacity and liquidity constraints. To minimize the operating costs, based on the uncertain theory, the thesis established an uncertain arrival time of multi-products optimal order quantities optimization model with the fuzzy and random variable using the random fuzzy theory and uncertain programming.Secondly, design and implement the genetic algorithm to solve the proposed model. In the design of algorithms, according to the characteristics of the problem, this thesis designs the integer chromosome coding method to period good order quantity for the gene segment, using a heuristic method to realize population initialization. According to the characteristics of chromosome coding, design two crossover operators:one is the uniform crossover operator in a gene segment and the other is the single point crossover operator among gene segments. The swap mutation operator is based on heuristic mutation within the segment and swap genetic mutation operator within the segment. To the selection operator, this thesis using the mixed strategy of elite selection strategy and roulette wheel selection strategy. At last, this thesis designs a repair strategy to process the infeasible chromosomes.Finally, the algorithms are implemented with C language programming. Application case is given to perform the simulation study and sensitivity analysis, and to verify the feasibility of the proposed model and algorithms. 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 the performance of genetic algorithm. The model simulation experiment includes parameter sensitivity analysis of the inventory capacity, liquidity, adjustable goods price ratio, different types of discounts, as well as the key constraint on the order quantities and operating costs impact by the uncertain arrival time. Analyze the effect of cargo start-up costs and cargo transfer variable costs on the total cost of cargo goods. Simulation results validate the effectiveness and feasibility of the model and algorithms.