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Simulation study on optimization of delivery path for community group buying
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DOI：10.3389/ffutr.2025.1671246.png)
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Abstract
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This paper addresses the optimization of community group-buying distribution paths by considering both cost efficiency and time constraints. Focusing on dense neighbourhoods where such services thrive, the study highlights how current distribution strategies often prioritize proximity over effectiveness. To minimize total costs-including vehicle fixed costs, fuel expenses, and time-constrained penalties-a mathematical model is developed and solved using an improved genetic algorithm. The model incorporates real-world constraints from community group-buying platforms. Simulation in AnyLogic, using actual order data from the Flowers and Fruits platform, demonstrates that the proposed approach reduces distribution costs by 31.62%, achieving the lowest-cost distribution path while meeting time window requirements. The results validate the model's effectiveness in balancing economic and operational efficiency.
Keywords:
community group buying
distribution routes
genetic algorithm
anylogic simulation
cost optimization
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