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<p>In modern manufacturing, optimizing the scheduling of automated guided vehicle (AGV) has become essential due to the increasing production complexity and the demand for efficient logistics management. This research focuses on the multi-compartment AGV scheduling problem (MC-AGVSP), aiming to minimize the total transportation and service costs in matrix-structured manufacturing workshops. A mixed integer linear programming (MILP) model is developed to formally characterize the problem. To achieve efficient solution performance, a problem-tailored discrete Harris Hawk Optimization (DHHO) algorithm is proposed, which integrates a hybrid initialization approach that combines an enhanced nearest-neighbor heuristic with an improved sweep heuristic specifically designed for matrix manufacturing layouts. The algorithm also incorporates discretized update procedures that use crossover and neighborhood operators to balance exploration and exploitation phases. In addition, an archive update mechanism and a population regeneration strategy are employed to avoid premature convergence to local optima, complemented by a heuristic method designed to reduce computational overhead. Experiments conducted on 100 real-world problem instances reveal that the DHHO algorithm outperforms eight benchmark methods including five state-of-the-art meta-heuristics for fair comparison and three classical heuristics as performance baseline, achieving an average relative percentage deviation (RPD) of 1.984% and a best RPD of 0.212%, thereby demonstrating its efficacy and practical relevance in complex manufacturing settings.</p>

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Scheduling Multi-Compartment AGVs in Matrix Manufacturing Workshops Using a Discrete Harris Hawk Optimizer

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Junhai Zeng,
  • Wei Xie,
  • Mi Pan,
  • Langwen Zhang

摘要

In modern manufacturing, optimizing the scheduling of automated guided vehicle (AGV) has become essential due to the increasing production complexity and the demand for efficient logistics management. This research focuses on the multi-compartment AGV scheduling problem (MC-AGVSP), aiming to minimize the total transportation and service costs in matrix-structured manufacturing workshops. A mixed integer linear programming (MILP) model is developed to formally characterize the problem. To achieve efficient solution performance, a problem-tailored discrete Harris Hawk Optimization (DHHO) algorithm is proposed, which integrates a hybrid initialization approach that combines an enhanced nearest-neighbor heuristic with an improved sweep heuristic specifically designed for matrix manufacturing layouts. The algorithm also incorporates discretized update procedures that use crossover and neighborhood operators to balance exploration and exploitation phases. In addition, an archive update mechanism and a population regeneration strategy are employed to avoid premature convergence to local optima, complemented by a heuristic method designed to reduce computational overhead. Experiments conducted on 100 real-world problem instances reveal that the DHHO algorithm outperforms eight benchmark methods including five state-of-the-art meta-heuristics for fair comparison and three classical heuristics as performance baseline, achieving an average relative percentage deviation (RPD) of 1.984% and a best RPD of 0.212%, thereby demonstrating its efficacy and practical relevance in complex manufacturing settings.