Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA

Cao, Xia and Yang, Zhong-Yi and Hong, Wei-Chiang and Xu, Rui-Zhe and Wang, Yu-Tian (2022) Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA. Applied Artificial Intelligence, 36 (1). ISSN 0883-9514

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Abstract

With the regular development of the global epidemic, the global port shipping supply is tight. The problem of port congestion, soaring freight rates, and hard-to-find container space has emerged. This paper proposes a new joint berth-quay crane allocation model, namely E-B&QC, by taking the minimum of the time in the port of the ship, the cost of extra transportation distance for collector trucks in the land area of the port, and the cost of extra waiting time for ships. Then, the deficiencies of the sparrow search algorithm (SSA) are considered in solving the E-B&QC model, and the SSA is improved based on the three-dimensional Cat chaos mapping and quantum computing theory. Chaotic Quantum Sparrow Search Algorithm (CQSSA) is proposed, population individual coding rules are formulated, also E-B&QC model solving algorithm is established. Finally, a new berth-crane allocation optimization method, namely, E-B&QC-CQSSA, is proposed. Subsequently, the feasibility and superiority of the proposed allocation model and solution algorithm are tested according to the actual data of a small river port in the south and a medium-sized river port in the north. Simulation examples show that the E-B&QC model can develop different high-quality solutions for container ports under different working conditions, and the more complex the actual situation of the port, the more significant the optimization effect. The proposed CQSSA for E-B&QC model can obtain a better solution.

Item Type: Article
Subjects: STM Digital Library > Computer Science
Depositing User: Unnamed user with email support@stmdigitallib.com
Date Deposited: 14 Jun 2023 07:33
Last Modified: 07 Sep 2024 10:09
URI: http://archive.scholarstm.com/id/eprint/1432

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