Study On The Inventory Routing Problem With Multi-Period And Stochastic Demand

With the depth development and application of the Supply Chain Management, more and more people has realized that it plays a key role in reducing costs and gaining competitive advantages for enterprises. Meanwhile, enterprises which are in the supply chain business their own way, they only pursue the maximum interests. Obviously, it is not conducive to improve the competitiveness of the entire supply chain. Therefore, it is necessary to integrate the various components of the supply chain in order to get the maximum benefit, particularly the inventory and transportation, which have a higher cost proportion in all logistics elements.The inventory routing problem (IRP) addresses the coordination of inventory and transportation. Many foreign scholars have studied the problem and made some achievements. While in China, neither the theoretical study on IRP nor the application in the logistics strategies appear weak in current rapidly developing logistics research and practice. Consequently, this article is meant to discuss IRP theoretically, and illustrate the conclusion of discussion in practice. In order to achieve this goal, this article arranges contents as follows.Firstly, through the analysis of relevant literature and summary, IRP is defined and its study characteristics is given. The article has done many researches which focuses on stochastic demand, multi-period or unlimited planning period. In the researches, the shortcomings in existing researches is analyzed, IRP with multi-period and stochastic demand (IRPSD with multi-period) is determined as the research direction of the article.Secondly, the article researches IRPSD with multi-period in single vehicle condition. Through description and analysis of the problem, the mathematical model is established, then the inventory and transportation policy is analyzed, at last the genetic algorithm is designed as the solution of the model. On this basis, author expands single vehicle to multi-vehicle, and then researches the more general problem-IRPSD with multi-period in multi-vehicle condition. According to multi-vehicle characteristics, the particle swarm algorithm is designed to solve the model which is adjusted from the single vehicle model.Finally, the article combines the model and algorithm above to develop an inventory routing optimization system (IROS), which is developed in the Visual Studio 2008 environment, and uses C# as its programming language. The article accomplishes analysis and comparison successfully, and verifies the correctness of the researches by listing a multi-vehicle example.