Constitution System Of Supply Chain Logistics Capability And Its Key Capabilities Optimization

The logistics ability representation in the total logistics activity process has decisive influence on the whole supply chain competitiveness because of the logistics particularity among the supply chain. So the constitution system of supply chain logistics capability is constructed and its key capability optimization is researched in this dissertation. The main contents could be summarized as follows.On the basis of the general structure mode of logistics system, the conception and intension of logistics capability has been illuminate in a totally new way from the supply chain. The system constitution and frame construction of logistics capability has been built from three points of view, that is the logistics activity attribute, the logistics system nonrepresentational characteristic and the logistics capability constitution idiosyncrasy. For most logistics capability based on quantity and time, the supply chains is regarded as complete distribution balance network to analysis and deduct the maximum circulation quantity , integrate circulation quantity and response time , through which the quantization formula for index measuring is determined. Via analysis of the formula, the ways to improve circulation and shorten the response time of the supply chain is studied. The truck loading is researched to improve node logistics capability. A mathematical model has been set up for the problem of multi-trucks with multi-category goods under some appropriate hypothesis. According to the ratio difference of volume unit weight (volume/weight) between the trucks and the goods, an optimization algorithm is designed in the combinatorial theory on the basis of different benchmarks. Light goods are marked with the truck's load capacity so as to promote volume utilization rate of the trucks on the premise that the load capacity of the trucks is in full use. While to heavy goods, their dimensions are marked with the truck's load volume. Even goods are marked with the ration of volume unit weight to optimize the truck's load volume and capacity spontaneously, as both the dimension and load of the goods are even in trucks. In the end, comparison and analysis, via value emulation, are made between this benchmark algorithm and other optimization algorithms, and the result verified the effectiveness of this algorithm.The goods transshipment is researched to improve route logistics capability. Based on the stochastic inventory model of single goods, the two-hierarchy stochastic inventory model with multi-varieties was established. In this inventory system, the total cost was composed of goods transport costs, storage costs and loss costs due to lack of goods. The transport costs consisted of the costs of goods transported from the central warehouse to the sub-depots, and that of the goods transshipped among the sub-depot s. After the expenses structure of this inventory system was analyzed, the expenses expression was established. By using the mathematic methods and computer simulation, the optimization stocks of different goods could be determined. And the transshipment amount of different goods among the Sub-depot s under the different stock levels was analyzed through a practical example.The plant location of single finished product in the supply chain network (SCN) is studied to optimize network logistics capability specially. A supply chain network combined with the bill of materials (BOM) is established, which consists of supplier, manufacturer, distributor and retailer. By dividing up all the possible circulation of both manufacturing and transportation, the original SCN is transformed into one single way. Each node of the SCN is simplified to only bear the single function of single output if it bears different functions, outputs and raw material sources. According to the layer of BOM, all the same layer nodes are combined to include all the raw material sources of the finished product. The most suitable plants and routes under the time restriction are worked out through applying the generalized permanent labeling algorithm to the amalgamated SCN. The real optimal plants location and routes selection is confirmed by tracing backward to the original SCN.