Novel Bio-Inspired Physics-Based Learning and Evolutionary Guidance for Dynamic Multi-Objective Cold Chain Routings.

Area of Science:

• Biomimetics and bio-inspired artificial intelligence.
• Data-driven methods for engineering control and optimization.

Background:

• Agricultural cold chain logistics face challenges like perishability, high carbon emissions, and time constraints, worsened by disruptions.
• Current methods struggle with adaptability, multi-objective convergence, and cold-start issues.

Purpose of the Study:

• To develop a novel hybrid optimization framework (H-MODRL) inspired by nature for complex agricultural cold chain logistics.
• To address limitations of existing methods by integrating biomimetic principles, swarm intelligence, and deep learning.

Main Methods:

• The H-MODRL framework integrates a Genetic Algorithm (GA), Sparrow Search Algorithm (SSA), and an Arrhenius-based freshness-decay model.
• A three-stage hybrid evolutionary mechanism includes heuristic warm-start, evolutionary policy guidance, and deep reinforcement learning.
• Fast online replanning is supported by pre-computed shortest paths and dynamic-disruption indexing.

Main Results:

• H-MODRL outperforms state-of-the-art algorithms across logistics cost, carbon emissions, terminal freshness, and delivery time.
• The framework demonstrates robust and low-variance performance on simulated terrains based on real geospatial data.
• Experiments validate the engineering robustness and practical value of the H-MODRL framework.

Conclusions:

• The H-MODRL framework effectively tackles real-world logistics complexity using bio-inspired strategies.
• This approach offers significant improvements in efficiency and sustainability for agricultural cold chain operations.
• The study highlights the potential of biomimetics and AI in solving complex logistical challenges.