NeuHH: A Neuromorphic-Inspired Hyper-Heuristic Framework for Solving the Capacitated Single-Allocation p-Hub Location Routing Problem
This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and ve...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Vehicles |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-8921/7/2/61 |
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| Summary: | This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-heuristics, NeuHH provides biologically plausible, event-driven learning with improved scalability and interpretability. Experimental results on benchmark instances demonstrate that NeuHH outperforms classical metaheuristics, Lagrangian relaxation methods, and reinforcement learning-based hyper-heuristics. Specifically, NeuHH achieves superior performance in total cost minimization (up to 13.6% reduction), load balance improvement (achieving a load balance factor of as low as 1.04), and heuristic adaptability (reflected by higher heuristic switching frequency). These results highlight the framework’s potential for real-time and energy-efficient logistics optimization in large-scale dynamic networks. |
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| ISSN: | 2624-8921 |