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A Dynamic Multi-Objective Optimization Algorithm for AGV Routing in Assembly Workshops
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DOI：10.3390/app152011076.png)
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Abstract
En 中文
This study tackles the complex challenge of dynamic multi-objective vehicle routing optimization in large-scale equipment manufacturing, where routing operations significantly impact both economic performance and environmental sustainability. We develop an innovative Dynamic Multi-Objective Vehicle Routing Problem (DMOVRP) model that uniquely integrates three competing objectives: environmental impact reduction, delivery timeliness, and operational robustness. The proposed algorithm combines a dynamic event handler with the NSACOWDRL algorithm-an adaptive multi-objective optimization algorithm with dynamic event handling capability. The proposed system features adaptive mechanisms for handling real-time disruptions through specialized event classification and dynamic rescheduling protocols. Extensive computational experiments demonstrate the algorithm's superior performance with statistically significant improvements using the Wilcoxon signed-rank test (p < 0.05, n = 30 runs per instance), achieving average relative gains of 15.2% in HV, 12.8% in IGD, and 8.9% in GD metrics compared to established methods. This research makes theoretical contributions through its feasibility quantification metric and practical advancements in routing schedule systems. By successfully reconciling traditionally conflicting objectives through dynamic JIT adjustments and robustness-aware optimization, this work provides manufacturers with a versatile decision-support tool that adapts to unpredictable workshop conditions while maintaining sustainable operations.
Keywords:
dynamic vehicle routing problem
multi-objective vehicle routing problem
large equipment manufacturing
deep reinforcement learning
robust optimization
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