Stochastic modeling and analysis of two-phase operations in container terminal with automated lifting vehicles: A novel queuing approach

Govind Lal Kumawat, Divyansh Tripathi and Vishal Bansal

International Journal of Production Economics, 2025, vol. 286, issue C

Abstract: Container terminals worldwide are embracing automation to improve their efficiency and throughput performance for dynamic business requirements. However, to achieve the desired cost and operational efficiency of an automated container terminal, a thorough analysis of its performance under various design parameters is necessary at the design stage. In this paper, we propose a novel queuing model for the performance analysis of a container terminal that uses automated lifting vehicles for container transport. A special type of two-phase server is used to capture complex two-phase operations of quay cranes, automated lifting vehicles, and stack cranes, which were ignored in previous studies. Since existing methods cannot solve our resulting queuing model, we develop a new network decomposition-based approach to solve it. First, we validate the proposed analytical framework by developing a simulation model and find that it is quite accurate with a less than 5% average absolute percentage error in the average throughput time. Next, we quantify the value of accurately modeling the two-phase operations of resources in a container terminal. In our numerical analysis, we find that ignoring two-phase operations can lead to an overestimation of the average throughput time from 25% to 65%. Thus, our new modeling approach assists terminal designers in accurately estimating the performance measures. Furthermore, we illustrate an application of our model in resource planning and provide important design insights.

Keywords: Container terminal; Queuing model; Two-phase server; Automated lifting vehicle (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:286:y:2025:i:c:s0925527325001318

DOI: 10.1016/j.ijpe.2025.109646

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