Optimization of long-term planning with a constraint satisfaction problem algorithm with a machine learning
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초록
The object of the long-term planning in shipyards is to assign the ordered vessels to the berths with the consideration of the workload balancing. However, there are limitations in establishing an optimized long-term plan because the workload balancing takes too much time due to the size and the complexity of the problem domain. Most shipyards currently overcome the limitations by dividing the long-term planning into two-phase of the berth planning and the capacity planning. The berth planning is being conducted with a heuristic method by considering some rules such as the berth priority and the closeness to delivery date. Then it is followed by the capacity planning, in which the workload data is considered for the workload balancing with the previously planned data. However, the heuristic method has a fundamental problems that the optimized solution is not guaranteed owing to the limits of the search range. Also, the previous production record cannot reflect the newly ordered vessel's workload precisely. In this study, a constraint satisfaction technique is used for the optimization of the berth planning. In addition, the workload prediction model is developed based on the supervised learning with a deep neural network. Finally, proposed methods are tested with the shipyard actual data, that shows the improved results.
Abstract
The object of the long-term planning in shipyards is to assign the ordered vessels to the berths with the consideration of the workload balancing. However, there are limitations in establishing an optimized long-term plan because the workload balancing takes too much time due to the size and the complexity of the problem domain. Most shipyards currently overcome the limitations by dividing the long-term planning into two-phase of the berth planning and the capacity planning. The berth planning is being conducted with a heuristic method by considering some rules such as the berth priority and the closeness to delivery date. Then it is followed by the capacity planning, in which the workload data is considered for the workload balancing with the previously planned data. However, the heuristic method has a fundamental problems that the optimized solution is not guaranteed owing to the limits of the search range. Also, the previous production record cannot reflect the newly ordered vessel's workload precisely. In this study, a constraint satisfaction technique is used for the optimization of the berth planning. In addition, the workload prediction model is developed based on the supervised learning with a deep neural network. Finally, proposed methods are tested with the shipyard actual data, that shows the improved results.
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