Open Access

ARTICLE

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Rong Wang¹, Xinxin Xu², Zijia Wang^3,*, Fei Ji¹, Nankun Mu⁴

1 School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China
2 School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China
3 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China
4 School of Computer Science, Chongqing University, Chongqing, 400044, China

* Corresponding Author: Zijia Wang. Email:

Computers, Materials & Continua 2024, 80(2), 2363-2385. https://doi.org/10.32604/cmc.2024.053564

Received 04 May 2024; Accepted 01 July 2024; Issue published 15 August 2024

Abstract

Marine container terminal (MCT) plays a key role in the marine intelligent transportation system and international logistics system. However, the efficiency of resource scheduling significantly influences the operation performance of MCT. To solve the practical resource scheduling problem (RSP) in MCT efficiently, this paper has contributions to both the problem model and the algorithm design. Firstly, in the problem model, different from most of the existing studies that only consider scheduling part of the resources in MCT, we propose a unified mathematical model for formulating an integrated RSP. The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective. Secondly, in the algorithm design, a pre-selection-based ant colony system (PACS) approach is proposed based on graphic structure solution representation and a pre-selection strategy. On the one hand, as the RSP can be formulated as the shortest path problem on the directed complete graph, the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP, which effectively avoids the generation of infeasible solutions. On the other hand, the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution. To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model, a set of test cases with different sizes is conducted. Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm, which can significantly outperform other state-of-the-art algorithms.

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Keywords

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