Improving Efficiency of Container Inbound Scheduling Using Dual-Spreader Cranes in Container Yards

Container terminals are vital to global trade, and improving operational efficiency is key to competitiveness. Currently, container terminals primarily rely on single-spreader cranes for operations. Some ports have begun introducing dual-spreader cranes to enhance the efficiency of lifting operations. However, the operation of dual-spreader cranes spans multiple bays, significantly increasing the complexity of time-space operations. Research in this area remains scarce. Thus, this paper proposes an optimization method for container inbound scheduling based on dual-spreader cranes. First, a multi-bay time-space network flow model is constructed. Next, a bi-objective function is designed to synergistically optimize the number of obstructing containers and double-lift operations. Existing software has limitations in solving the dual-spreader crane scheduling problem, so this paper develops a novel two-level branch-and-bound algorithm to address it. Experiments show that the proposed algorithm achieves better solution quality for both optimization objectives in the double-spreader crane scenario compared to the greedy heuristic algorithm. Additionally, this paper verifies the effectiveness of various optimization strategies in the proposed algorithm through further experiments. Finally, it was validated that compared to single-spreader cranes, the average number of lifting operations for dual-spreader cranes is reduced by 35.67%. The proposed method provides new insights for improving the operational efficiency of container terminals and holds significant value for future research and practical applications.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3610722
Primary Topic
Industrial Engineering and Technologies
Type
article
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article

Improving Efficiency of Container Inbound Scheduling Using Dual-Spreader Cranes in Container Yards

Chaohui Zhu
IEEE Access
Industrial Engineering and Technologies
article

Improving Efficiency of Container Inbound Scheduling Using Dual-Spreader Cranes in Container Yards

Chaohui Zhu
article en

Abstract

Container terminals are vital to global trade, and improving operational efficiency is key to competitiveness. Currently, container terminals primarily rely on single-spreader cranes for operations. Some ports have begun introducing dual-spreader cranes to enhance the efficiency of lifting operations. However, the operation of dual-spreader cranes spans multiple bays, significantly increasing the complexity of time-space operations. Research in this area remains scarce. Thus, this paper proposes an optimization method for container inbound scheduling based on dual-spreader cranes. First, a multi-bay time-space network flow model is constructed. Next, a bi-objective function is designed to synergistically optimize the number of obstructing containers and double-lift operations. Existing software has limitations in solving the dual-spreader crane scheduling problem, so this paper develops a novel two-level branch-and-bound algorithm to address it. Experiments show that the proposed algorithm achieves better solution quality for both optimization objectives in the double-spreader crane scenario compared to the greedy heuristic algorithm. Additionally, this paper verifies the effectiveness of various optimization strategies in the proposed algorithm through further experiments. Finally, it was validated that compared to single-spreader cranes, the average number of lifting operations for dual-spreader cranes is reduced by 35.67%. The proposed method provides new insights for improving the operational efficiency of container terminals and holds significant value for future research and practical applications.

IEEE AccessVol. 13
Nanchang University (CN)
Openalex Percentile: Top 75%
Industrial Engineering and Technologies
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Improving Efficiency of Container Inbound Scheduling Using Dual-Spreader Cranes in Container Yards — Chaohui Zhu · IEEE Access (2025) | TGRS Research Map | TGRS