The Job Scheduling of Two Yard Cranes in a Container Block

• Main Authors: Ying-Jyuan Liou, 劉盈娟
• Other Authors: Yat-Wah Wan
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/k99436

碩士 === 國立東華大學 === 全球運籌管理研究所 === 96 === The trend of global logistics makes sea freight play an ever more important role in freight transportation, amongst which container transport being the core. In a container terminal, to speed up the loading process and to reduce the berthing time of vessels, export containers are temporarily stored in the storage yard before loading on a container vessel. As yard cranes are very expensive, their high utilization is crucial. Up till now, there is no study such that the retrieval order of containers is in group, i.e., containers in the same group can be retrieved in any order, and containers in different groups must be retrieved according to the order as specified for the groups. It is not clear how to construct the model and subsequently find the optimal schedule for a block with multiple yard cranes under such situation. This study is precisely for the above problem: For a block such that the export containers are retrieved in group, to find the optimal schedule of two yard cranes to minimize the time to retrieve all export containers. Naturally, at any time the two yard cranes must keep a safety distance and cannot cross each other. This study tackles the scheduling problem of yard cranes in fours steps with increasing complexity. The mixed integer program constructed in each step is solved with CPLEX 10.0. As it takes long computational time to solve a problem with large number of containers, a heuristic is developed for the problem. The heuristic consists of two parts. One is to assign containers to yard cranes and the other is to determine the schedule of the yard cranes under the given assignment. When compared to the optimal solution, in most of the numerical runs, the heuristic takes much less computation time to get results reasonably close to the optimum.