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Robust Airfreight forwarder shipment planning with integration and consolidation of shipments
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AI
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J
DOI：10.1080/17509653.2026.2640408.png)
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Abstract
En 中文
Airfreight plays an increasingly vital role in global trade, particularly within complex supply chains and multimodal transport systems. Given the numerous uncertainties inherent in Airfreight Forwarder Shipment Planning (AFSP), a novel robust optimization-based planning approach is developed to construct shipment plans through integration and consolidation strategies, to reduce costs and improve overall transport efficiency. In particular, two robust optimization models are introduced: one based on an ellipsoidal uncertainty set and the other on a polyhedral uncertainty set, to handle uncertainties in AFSP. To solve medium- and large-scale instances efficiently, a Hybrid Genetic Tabu Search with Probabilistic Perturbation Algorithm (HGTP) is designed, incorporating a probability learning-based perturbation mechanism. The effectiveness of the two models under complex management environments and uncertain conditions is validated through real-world case analyses and simulation experiments. Their performance and applicability in AFSP are compared, and corresponding managerial insights are derived.
Keywords:
Airfreight forwarder shipment planning
integration and consolidation
robust optimization
ellipsoidal uncertainty set
polyhedral uncertainty set
M11
Journal
IF:
2.6
Papers:
237
Citations:
739
Organization
Cited Papers