Collaborative Ordering And Dispatching Policies For The Auto Parts System With Multiple Supply Sources And TPL

With the increasingly intensified competition in the emerging global economy, the competition among different auto manufacturers also becomes worse. To better cope with the pressure, the auto manufacturers outsource their logistics business. Usually, the manufacturer makes the ordering decisions, while the third-party logistics (TPL) provider makes the transport decisions. However, both ordering and transport are part of the supply chain process; they affect each other and restrict each other. To optimize the supply chain performance, the auto manufacturer should consider the transportation decisions when making ordering decisions, and vice versa. Based on this consideration, this dissertation firstly discusses the impact of collaborative ordering and dispatching decisions on the supply chain with multiple suppliers, and then proposes some methods to find the optimal ordering and dispatching decisions at both deterministic and stochastic environment. Finally, it tries to put the proposing collaborative ordering and dispatching policies into practice by using case studies. Specifically, the dissertation has contributed in the following aspects:(1) To study how the collaborative ordering and dispatching policies affect the performance of supply chain, a system dynamic simulation model was firstly built by using Venism. Three models with different collaborative level were compared in terms of total cost, dispatching cost, service level and inventory level, The results show that as the collaborative level increases, the total cost reduces. In addition, the impact of different stochastic factors was also investigated and the uncertain supplier yield influences the collaborative effect most.(2) Assume the auto manufacturer and TPL make the ordering and dispatching decisions simultaneously, the manufacturer could purchase from different suppliers with different cost, while TPL could use two different types of cargo. The main properties of this problem were firstly been analysed by induction, which includes the ordering rule regarding to several suppliers, the relationship of ordering period and dispatching period, and the dispatching allocation rule and so on. According to these properties, a dynamic model with single part was constructed and solved by using the networking approach, then a genetic algorithm with two layer parameter concatenated coding method was designed for the multiple part problem, and the effectiveness of result was increased by considering the properties.(3)Using the renewal theory to analyze the two-echelon supply chain with uncertain supply, the uncertain supply is described as on and off state. A model with two uncertain suppliers was firstly constructed, and the structure of ordering cycle and total cost were also been analysed. Then a model with one certain supplier and one uncertain supplier was constructed. Finally, the optimal ordering and dispatching decisions and cost under these three models were compared and the impact of uncertain supply was discussed in detail.(4)To discuss whether the proposed collaborative policies from the above analysis can be put into practice or not, a case study was conducted. Both the ordering system of auto manufacturer JiangQi and the transporting system of TPL JiangLiu were firstly analyzed. Then the benefit of collaboration was also being elaborated by using real data, and the optimal decision was computed by using the model built before. Finally, a specific implementation program considering the actual situation was proposed.