• 한국CDE학회논문집
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• 제21권2호
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• pp.177-185
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• 2016

In this paper, an optimal planning system for operating transporters in shipyard is developed. The system is designed to utilize the geometries of shipyard, and manage the data of blocks and transporters directly. There are four major menus such as shipyard map management based on GIS, block transportation request, transporter management, and optimal transportation planning in the system. The geometries and properties of the shops, roads, and addresses are manipulated in the shipyard map management menu. The block transportation requests and the properties of transporters are managed in the block transportation request and transporter management menus, respectively. The optimum transportation is planned automatically for minimizing the unload times of the transporters, and the optimum transportation plans are confirmed and printed to the transporter drivers. The effectiveness of the system was verified through the application to a large-sized shipyard.

• 대한조선학회논문집
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• 제54권5호
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• pp.421-429
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• 2017

It is very important to manage the position of the blocks in the shipyard where the work is completed, or the blocks need to be moved for the next process operation. The moving distance of the block increases according to the position of the block stockyard. As the travel distance increases, the number of trips and travel distance of the transporter increases, which causes a great deal of operation cost. Currently, the selection of the block position in the shipyard is based on the know-how of picking up a transporter worker by the production schedule of the block, and the location where the block is to be placed is determined according to the situation in the stockyard. The know-how to select the position of the block is the result of optimizing the position of the block in the shipyard for a long time. In this study, we used the accumulated data as a result of the operation of the yard in the shipyard and tried to select the location of blocks by learning it. Decision tree learning algorithm was used for learning, and a prototype was developed using it. Finally, we prove the possibility of selecting a block stockyard through this algorithm.