The work aims to propose an approach to allocate orders and plan routes while balancing the interests of all stakeholders and enhancing platform efficiency and cost reduction, so as to address issues such as unreasonable order allocation and delivery delays in on-demand delivery. Firstly, a scheduling model was constructed to minimize the total scheduling cost of the food delivery platform, considering the rights and interests of delivery riders, customers, and merchants, factors like time windows and delivery sequences, and real-time weather changes. Secondly, an order consolidation strategy was introduced, which simultaneously satisfied the proximity of delivery points and the similarity of order preparation times, solved using the K-means++ clustering method. Subsequently, path planning for the consolidated order sets was conducted, incorporating the impact of weather on delivery speed and customer satisfaction, with optimal routes solved using a genetic algorithm. Finally, simulation and comparative analysis of case data demonstrated the strategy's effectiveness. Results indicated that when the order volume ranged from 2 to 6, punctuality was generally ensured. Compared with traditional dispatching methods, the time-space similarity-based dispatch strategy improved punctuality by 14.55%, reduced delivery distance and cost by 58.61% and 59.56%, respectively, and outperformed space-similarity-based methods by enhancing punctuality by 6.36%, while cutting delivery distance and cost by 1.90% and 7.28%, respectively. The proposed models, algorithms, and order consolidation strategies can allocate orders and plan delivery routes based on real-time weather conditions, effectively reducing costs and improving order punctuality and providing a reference for food delivery platforms to enhance efficiency and reduce costs.
Key words
instant delivery /
order consolidation /
path planning
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