Yard and berth planning efficiency with estimated handling time. Issue 1 (4th December 2019)

• Record Type: Journal Article
• Authors: Nishimura, Etsuko
• Is Part Of: Maritime business review. Volume 5:Issue 1(2020)
• Page Start: 5
• Page End: 29
• Publication Date: 2019-12-04
• Subjects: Genetic Algorithm -- Optimization -- Container terminal -- Multiple regression analysis -- Handling time -- Yard arrangement; Shipping -- Periodicals; 387.5
• DOI: 10.1108/MABR-08-2019-0034
• Languages: English
• ISSNs: 2397-3757

Abstract

Abstract : Purpose: To achieve a high container handling efficiency at transshipment hub ports, there are a variety of scheduling problem as ship-to-berth assignment (BAP), container-to-yard arrangement (YAP), etc. As it is difficult to acquire the actual data of an existing terminal under various circumstances, this study aims to develop the time estimation model of container handling. Additionally, to achieve an efficient handling of containers at the yard, this study proposes the way to optimize the yard arrangement along with the berth allocation simultaneously by using estimated handling time. Design/methodology/approach: To obtain the handling time based on various situations of the terminal operated, the discrete simulation model of container handling is constructed. The model to estimate the handling time of a quay crane assigned to a relevant ship by multiple regression analysis is developed. To find a feasible solution to minimize the total service time which includes YAP and BAP simultaneously, a genetic algorithm based on heuristics is developed. Findings: The proposed regression model has high performance to estimate the time spent of container handling. In the total service time, the proposed approach outperformed the existing 2-step process approach. Originality/value: As it is difficult to acquire the actual information of an existing marine terminal under various circumstances, the paper contains a regression model to estimate the container handling time based on simulation data, and the regression model is used in an optimization model to minimize the ship turnaround time.