A knowledge discovery and reuse method for time estimation in ship block manufacturing planning using DEA. (January 2019)

• Record Type: Journal Article
• Authors: Li, Jinghua; Sun, Miaomiao; Han, Duanfeng; Wang, Jiaxuan; Mao, Xuezhang; Wu, Xiaoyuan
• Is Part Of: Advanced engineering informatics. Volume 39(2019)
• Page Start: 25
• Page End: 40
• Publication Date: 2019-01
• Subjects: Estimation -- Shipbuilding -- Block manufacturing -- DEA -- K-Means -- Neural network; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.11.005
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: A novel three-stage time estimation method based on data mining is proposed. A strategy with the expert's opinion considered is designed when using K-Means clustering algorithm. An adapted DEA model is presented to evaluate the efficiency of the time arrangement. Estimation models are trained using the genetic algorithm-backpropagation neural network. Abstract: Rational and precise time estimation in a manufacturing plan is critical to the success of a shipbuilding project. However, due to the large number of various ship blocks, existing means are somehow inadequate to make the expected estimation. This paper proposes a novel three-stage method to discover and reuse the knowledge about how the duration and the slack time is essential while manufacturing a specific ship block. An efficient arrangement of the duration and the slack time means that the activity is more likely to be finished within the allocated duration, or if not, the extra consumed time does not exceed the given slack time which is at its lowest level. With such knowledge, planners can rapidly estimate the time allocation of all the manufacturing activities in the planning stage, which raises the possibility of successful execution within the limited budget. Different from previous studies, this research utilizes the execution data to find efficiency frontiers of the planned time arrangement (the duration and the slack time). For the sake of the evaluation validity, ship blocks are primarily clustered according to their features using the K-Means algorithm. In the second stage, an adapted data envelopment analysis (DEA) model is presented to evaluate the planned time arrangement. By processing the results, efficient time arrangements for manufacturing all the blocks can be obtained, hence, forming a data basis to boost the time estimation accuracy. In the last stage, genetic algorithm-backpropagation neural network (GA-BPNN) models are trained to capture the knowledge for further reuse by planners. Verified through experiments, this research almost outperforms several peer methods in terms of precision.