빅데이터를 이용한 부산항신항 ITT 효율적인 운영방안에 관한 연구

Abstract

The global maritime crisis and the financial slowdown are accelerating a fierce competition to induce container cargo volumes against other global ports.

In particular, this competition among them is more intensified by offering large-scale incentives or free port dues in order to attract global alliances of liners.

First above all, shipping lines constantly request that Busan Port Authority introduce the ITT, so that the subsidy of extra operating costs caused by ITT can secure transshipment container cargoes.

Despite that there are many container terminal operators which have been splitted for a long time and disintegrated terminals have caused unnecessarily extra operating costs, however, an ITT operative problem in Busan Port can be solved by integrating all terminal operators into a unified single terminal.

In fact, due to our current difficulty of the terminal integration, therefore, only establishing an ITT platform can be the key to solve ITT issues in the first place.

This paper shows that an ITT platform can adopt an ITT platform design and configuration algorithm which should be first simulated utilizing Big Data tool.

Big Data is one of the latest IT technologies in the 4th Industrial Revolution. It is a way to re-create valuable data through correlation analysis.

In order that the data are used for Big Data, I’d like to propose a scenario to optimize the ITT platform construction by confining the optimal design requiring actual data after data mining through analysis tool (R).

Particularly, optimized in-and-out container cargoes can be calculated by analysis of loading and unloading rates of transshipment cargoes between nearby two terminals and the turnaround time. Besides, an ITT platform can be optimized to the establishment of an ITT platform via analysis of the best route of terminal-to-terminal TS on a basis of day and time and the transportation pattern of cargoes.

Thus, it is possible to reduce the failure rate of the ITT platform by performing the simulation once using the big data before execution, and it could be reflected in the future ITT platform design.