Integrated Scheduling of Yard and Rail Container Handling Equipment and Internal Trucks in a Multimodal Port

Liu, Wenqian; Zhu, Xiaomin; Wang, Li Ye; Zhang, Qin; Tan, Kay Chen

doi:10.1109/tits.2023.3295812

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…They verified the model's feasibility using the Gurobi solver. Liu et al [23] investigated the coscheduling problem involving railroad gantry cranes (RCOGC), YC, and IV in an intermodal container terminal. They formulated a multiobjective model and designed an adaptive large neighborhood search (ALNS) algorithm combined with discrete event simulation (DES) to address the problem.…”

Section: Integrated Scheduling In Container Terminalmentioning

confidence: 99%

“…Equations ( 16)-( 22) mirror the role for the QC as Equations ( 8)-( 14) do for the IGV. Equation (23) specifies that QC q operating on bay a loads the designated containers into bay a. Equation (24) states that if container i is the first loading task assigned to QC q, and it is designated to be loaded at bay a, then bay a will be the initial bay processed by QC q.…”

Section: Constraints (1) Igv Operational Constraintsmentioning

confidence: 99%

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Energy-Aware Integrated Scheduling for Quay Crane and IGV in Automated Container Terminal

Luo,

Liang,

Zhang

et al. 2024

JMSE

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In this study, we address the integrated scheduling problem involving quay cranes and IGVs in automated terminals. We construct a mixed-integer planning model with the aim of minimizing the total energy consumption during quay crane and IGV operations, focusing on the loading-operation mode. The model considers the impact of the actual stowage of container ships on the loading order. We propose a dimension-by-dimension mutation sparrow search algorithm to optimize the model’s solution quality. Building upon the standard sparrow search algorithm, we incorporate cat mapping to enhance the diversity of the initial sparrow population. To improve global search in the early stage and local search in the later stage of the algorithm, we introduce an adaptive t-distribution mutation strategy. Finally, a total of 12 instances with container counts containing 30, 100, and 250 were designed for experiments to validate the effectiveness of the model and algorithm. The experiments demonstrate that, by appropriately increasing the number of quay cranes, configuring more than two or three IGVs can achieve optimal energy consumption for overall operations.

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Section: Integrated Scheduling In Container Terminalmentioning

confidence: 99%

Section: Constraints (1) Igv Operational Constraintsmentioning

confidence: 99%