Quay crane and yard truck dual-cycle scheduling with mixed storage strategy |
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| Affiliation: | 1. Sanjiang Research Institute of Artificial Intelligence and Robotics, Yibin University, Sichuan, China;2. School of Management, Shanghai University, Shanghai, China;1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computing, National University of Singapore, 13 Computing Drive, Singapore 117417, Singapore;3. Department of Mechanical and Electromechanical Engineering, National ILan University, ILan 26041, Taiwan;4. Huazhong University of Science and Technology – Wuxi Research Institute, Wuxi 214000, China;5. School of Mechanical Engineering, Hubei University of Technology, Wuhan 430072, China;1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China;1. Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. National Center of Technology Innovation for Digital Construction, Huazhong University of Science and Technology, Wuhan, Hubei, China;3. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China;4. Weichai Power Co., Ltd, Weifang, Shandong, China;5. Shantui Construction Machinery Co.,Ltd, Jining, Shandong, China |
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| Abstract: | In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained. |
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| Keywords: | Dual-cycle Quay crane Yard truck Mixed storage strategy Container terminals |
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