Research on strategies for improving the resilience of tobacco logistics distribution systems
In response to the insufficient resilience, anti-interference and self-repair capabilities of tobacco logistics distribution systems during highly destructive emergencies such as earthquakes and floods, this study constructs an optimization model for enhancing the recovery of tobacco distribution systems. The model maximizes the resilience value of the system with constraints including vehicle replacement completion time, replacement vehicle completion time, and operational level response. An adaptive genetic algorithm is designed to solve this model, with detailed settings for adaptive pa-rameters. Using data from a tobacco logistics distribution system in a specific city as a case study, the effectiveness of the recovery optimization model and the impact of different recovery strategies on sys-tem resilience are verified. The study considers three disturbance scenarios, which include one random attack and two purposeful attacks.It implements four replacement methods to determine optimal recov-ery strategies:target replacement, random replacement, preference-based on departure order, and preference-based on vehicle importance. Results indicate that the proposed recovery optimization model is effective and stable, with the adaptive genetic algorithm demonstrating convergence. Across the three disturbance scenarios, the target replacement method consistently outperforms the other strategies in enhancing system resilience. Applying the adaptive genetic algorithm to solve the recov-ery optimization model achieves a maximum resilience improvement of up to 47％ for the tobacco lo-gistics distribution system.
logistics distribution systemresilience assessmentrepair strategyadaptive genetic algorithmtobacco
万方数据
维普
