Estimation of Ship Repair Time Using the Genetic Algorithm

Abstract

Ship maintenance is regulated by both the state and the classification society. The scope of maintenance works depends on the age of the ship and includes a dock, intermediate and special inspection. The problem is to estimate the reliable time of the ship maintenance and the downtime at the shipyard. The purpose of this article is to develop a more accurate model to predict a ship’s overall maintenance time. A multiple linear regression model is developed to describe the impact of historical data on hull repair, painting time, piping, age, structural and hull plate replacement for ship maintenance. In the literature, the least squares method is used to estimate unknown regression coefficients. The original value of the article is the use of a genetic algorithm to estimate coefficient values of the multiple linear regression model. Necessary analysis and simulations are performed on the data collected for oil and chemical or product tankers. As a result, a significant improvement in the adequacy of the presented model was identified.

Keywords

• mean time of dry-docking
• average repair time
• maintenance duration prediction
• maintenance duration estimation
• ship maintenance
• genetic algorithm