A Location-Inventory-Routing Problem Integration Optimization And Research In Stochastic Environment

Since the reform and opening up,the development of China's economy has been obvious to all.In recent years,China has proposed that the economic development needs to be transformed from high-speed development to high-quality development.With the continuous development of society and economy,both individuals and enterprises have higher requirements on service quality and attach more importance to time.The response time of enterprises is shorter,and their demand for products also shows randomness and diversity.In order to develop,enterprises have to deal with challenges,face various uncertainties,and improve the quality of service to customers.Enterprises can only formulate strategies to deal with various uncertain events and pay more attention to the time factor.For most large enterprises,they must consider and face the problems of location,inventory,and path in the logistics system.How to make more economic and long-term development decisions in the changing market environment is a problem for decision makers to consider.In this context,this paper studies the site-inventory-path integration of logistics system in a random environment,so as to pro vide decision basis for decision makers.Firstly this paper analyzes the site selection of logistics system-integrated optimization problem of inventory-path research status at home and abroad,and the research of this field is not a lot,so in order to further study on this question,this article first studied the distribution center location and distribution center inventory control integrated optimization problem,the location of the distribution center and vehicle routing optimization problem,distribution center inventory control and vehicle routing integrated optimization problem of the research status at home and abroad,then the distribution center location problem of logistics system,distribution center inventory control problems,and the integrated optimization problem of vehicle routing problem research status is analyzed.At present,the research on the site-inventory-path integrated optimization of logistics system is carried out in a certain environment,but there are not many studies on this problem in an uncertain environment.And articles on the site selection,inventory control problems in logistics system and vehicle routing problem is studied respectively,will all costs are taken into account in the process of the whole logistics activity,mainly includes the distribution center location involved costs,operating costs,the site selection of distribution center inventory control,order fixed costs involved,the storage of order variable cost,inventory cost and shipping vehicles involved in the transportation costs,etc.In addition,the total time required to complete the three links of distribution center site selection,distribution center inventory control and vehicle distribution in a complete logistics activity was comprehensively analyzed.The total time was taken as another optimization target,and a dual-objective optimization model was established.And according to the actual situation,the factors such as unit storage cost,unit transportation cost and customer demand in the model are regarded as random variables subject to a certain probability distribution,and a stochastic opportunity constrained programming model is established for the site-inventory-path integration optimization problem of logistics system in a random environment.Then the random chance constrained programming model is established for this article,analyzed the existing algorithm of stochastic optimization problem,by analyzing the advantages and disadvantages of each algorithm,combined with in this paper,we study the complexity of the problem,using stochastic simulation technology to deal with random variable in the model,genetic algorithm,then,to solve the model in this paper.The improvement of genetic algorithm is mainly reflected in the heuristic rules added to the initial population.In order to verify the effectiveness and superiority of the model and the designed algorithm,an example simulation is carried out in chapter 5,and the results are compared with those of the genetic algorithm without improvement.