Machine learning approach to analyze the status of forklift vehicles with irregular movement in a shipyard

Abstract

In large shipyards, the management of equipment used to build ships is critical. Because orders vary year to year, shipyard managers are required to determine methods to make the most of their limited resources. A particular difficulty arises in the management of moving vehicles because of the nature and size of shipyards. In recent years, shipbuilding companies have attempted to manage and track the locations and movements of vehicles using global positioning system (GPS) modules. However, because certain vehicles, such as forklifts, move irregularly around a yard, identifying their working status without being onsite is difficult. Simple location information alone is insufficient to determine whether a vehicle is working, moving, waiting, or resting. Status information acquisition requires intelligence algorithms to distinguish GPS data. This study proposes an approach based on machine learning to identify the work status of each forklift. We use the DBSCAN and k-means algorithms to identify the area in which a particular forklift is operating and the type of work it is performing. We developed a business intelligence system to collect data on forklifts equipped with GPS and Internet of Things devices. The system provides visual information on the status of individual forklifts and helps in the efficient management of their movements within large shipyards. © 2021 Elsevier B.V.