Solving energy-efficient lock group co-scheduling problem with ship lift and approach channel using a collaborative adaptive multi-objective algorithm

• Published in: Expert systems with applications Vol. 242; p. 122712
• Main Authors: Zheng, Qian-Qian, Zhang, Yu, Guo, Wen-Jing, Tian, Hong-Wei, He, Li-Jun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.05.2024
• Subjects: Approach channel; Collaborative adaptive multi-objective algorithm; Energy-efficient lock group co-scheduling; Ship lift; Three Gorges-Gezhou Dam Hub; Energy-efficient lock group co-scheduling; Collaborative adaptive multi-objective algorithm; Ship lift; Three Gorges-Gezhou Dam Hub; Approach channel
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.122712

There is growing interest in the lock group co-scheduling research because of serious capacity imbalance between two dams at Three Gorges-Gezhou Dams Hub (TGDH). However, most current studies ignore the impact of ship lift and approach channel on navigation efficiency, and the energy consumption from vessels on ecological environment. Encouraged by this, we investigate an energy-efficient lock group co-scheduling problem at the TGDH with the consideration of ship lift as well as approach channel. A new multi-objective model for the problem is proposed, aiming to simultaneously optimize the average area utilization of all locks, average tardiness of vessels and total energy consumption of vessels. A collaborative adaptive multi-objective algorithm (CAMOA) is well-designed to solve the studied problem. The CAMOA makes use of a well-tailored two-layer encoding scheme and a three-stage group-shift decoding approach to represent and decode each solution. Next, an adaptive adjustment search strategy based on step control factor is periodically triggered to reinforce local exploitation capability, where a novel fuzzy correlation entropy analysis is coupled to evaluate the neighborhood solutions. Extensive simulation experiments are implemented according to the real-world data from the TGDH. The statistical results demonstrate that the proposed CAMOA is efficient and reliable in solving the studied problem. This work is very significant for TGDH to improve the passing efficiency and reduce the energy consumption.