Data-driven dynamic stacking strategy for export containers in container terminals

• DOI: 10.1007/s10696-022-09457-8
• Publication Date: 2022-06-27T20:02:51Z
• Authors: Hyun Ji Park; Sung Won Cho; Abhilasha Nanda; Jin Hyoung Park
• Keywords: Stowage

Abstract This study investigates a method for improving real-time decisions regarding the storage location of export containers while are arriving. To manage decision-making process, we propose two module-based data-driven dynamic stacking strategy that facilitates stowage planning. Module 1 generates Gaussian mixture model (GMM) specific to each container group weight classification. 2 implements as an online algorithm determine arriving in real time. Numerical experiments were conducted using real-life data validate effectiveness proposed compared other alternative strategies. These revealed performance is robust, and therefore it can improve yard operations terminal competitiveness.