It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics, taking city of Erdos as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Erdos and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

The aim of paper is to calculate the optimized size of Mobile Harbor(MH) which would be operated in South Korea coast area. MH is the combined entity which has the function of both ship and container port. In estimating the optimized size, the total cost concept is applied to the different size of MH. Trade-off factors for calculating total cost are MH cost and the over-capacity lost cost. The factors for MH cost estimation are the cargo demand, distance from origin to destination, voyage route and MH's fixed and variable cost in both sailing and port. The other cost is the over-capacity lost cost which is occurred from dead space in case of oversize compared with a voyage demand. The alternatives for the least cost are 250TEU, 500TEU, 750TEU and 1,000TEU sized vessel. The result of research is that 250TEU sized vessel is optimized in a South Korea costal service. If the coastal area be separated in terms of voyage distance or the specific area in considering trade, the optimized size is changed depending upon distance.

As the competition among the container terminals in Korea has become increasingly fierce, every terminal is striving to increase its investments constantly and lower its operational costs in order to maintain the competitive edge and provide satisfactory services to terminal users. The unreasoning behavior, however, has induced that substantial waste and inefficiency exists in container terminal production. Therefore, it is of great importance for the terminal to know whether it has fully used its existing infrastructures and that output has been maximized given the input. From this perspective, data envelopment analysis (DEA) provides a more appropriate benchmark. This study applies three models of DEA to acquire a variety of analytical results about the operational efficiency to the Korean container terminals. According to efficiency value analysis, this study first finds the reason of inefficiency. It is followed by identification of the potential areas of improvement for inefficient terminals by applying slack variable method and giving the projection results. Finally, return to scale approach is used to assess whether each terminal is in a state of increasing, decreasing, or constant return to scale. The results of this study can provide terminal managers with insight into resource allocation and optimization of the operating performance.

The container port industry has been variously studied by many researchers, because the contemporary container transportation and container port industries play a pivotal role in globalization of the world economy. For container terminals, the productivity, affected by many factors, is an important target in measuring container terminal performance. Under this background, finding the critical factors affecting the productivity is necessary. Regression analysis can be used to identify which independent variables are related to the dependent variable, and explore the relationships of them. The aim of paper is to evaluate the factors affecting the productivity of Chinese major terminals by using a regression statistical analysis modeling approach, which is to establish the variable preprocessing model (VPM) and regression analysis model (RAM), by means of collecting the major Chinese container terminals data in the year of 2008.

This study intends to surveyrequirements for port and logistics supply chain management with RFID {Radio Frequency IDentification)technology. Port and logistics supply chain management has become a critical issue due to the necessity of efficiency, visibility, trace-ability, etc. Since the introduction of RFID technology, its performance, reliability, validity, and safety have been a concern in most industries. Particularly, in port and logistics supply chain management, RFID has the potential to track the movement of containers and to provide in-transit visibility toward customers. In thispaper we consider some critical issues related to port and logistics supply chain management, which previously adopted RFID technology. In order to successfully design and adopt RFID technology and utilize it as optimally as is possible in the port and logistics industry, it is necessary to understand the potential of shipping companies and their requirements in adopting RFID technology in port and logistics.