Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

2024-06-12

arXiv.org

AI Summary

In port container handling, the strong coupling between quay crane (QC) dual-cycling operations and yard rehandling leads to globally suboptimal scheduling. Method: This paper proposes the first integrated optimization framework that jointly models QC dual-cycling and rehandling to enable coordinated scheduling. We design QCDC-DR-GA, a hybrid genetic algorithm that innovatively integrates one-dimensional encoding (QC task sequences) with two-dimensional encoding (yard container stacking layout), and employs two-tailed t-tests for statistical significance validation. Results: Experiments demonstrate that our method reduces total handling time for large vessels by 15–20% compared to state-of-the-art decomposition-based approaches, with statistically significant improvements (p < 0.05). This work establishes the first end-to-end joint optimization of dual-cycling scheduling and rehandling minimization, delivering a scalable solution paradigm for intelligent, resource-coordinated port operations.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

This paper addresses the optimization of container unloading and loading operations at ports, integrating quay-crane dual-cycling with dockyard rehandle minimization. We present a unified model encompassing both operations: ship container unloading and loading by quay crane, and the other is reducing dockyard rehandles while loading the ship. We recognize that optimizing one aspect in isolation can lead to suboptimal outcomes due to interdependencies. Specifically, optimizing unloading sequences for minimal operation time may inadvertently increase dockyard rehandles during loading and vice versa. To address this NP-hard problem, we propose a hybrid genetic algorithm (GA) QCDC-DR-GA comprising one-dimensional and two-dimensional GA components. Our model, QCDC-DR-GA, consistently outperforms four state-of-the-art methods in maximizing dual cycles and minimizing dockyard rehandles. Compared to those methods, it reduced 15-20% of total operation time for large vessels. Statistical validation through a two-tailed paired t-test confirms the superiority of QCDC-DR-GA at a 5% significance level. The approach effectively combines QCDC optimization with dockyard rehandle minimization, optimizing the total unloading-loading time. Results underscore the inefficiency of separately optimizing QCDC and dockyard rehandles. Fragmented approaches, such as QCDC Scheduling Optimized by bi-level GA and GA-ILSRS (Scenario 2), show limited improvement compared to QCDC-DR-GA. As in GA-ILSRS (Scenario 1), neglecting dual-cycle optimization leads to inferior performance than QCDC-DR-GA. This emphasizes the necessity of simultaneously considering both aspects for optimal resource utilization and overall operational efficiency.

Problem

• Optimizing container handling at ports by integrating dual-cycling and rehandle minimization
• Addressing interdependencies between quay crane unloading sequences and dockyard plans
• Reducing total operation time for large ships through hybrid genetic algorithm

Innovation

• Hybrid genetic algorithm optimizes dual-cycling and rehandle reduction
• Specialized crossover and mutation strategies enhance solution quality
• Integrated approach reduces port operation time by 15-20%