This study reveals a multi-objective framework to reduce rider pressure in food delivery, suggesting improvements for efficiency in smart cities.
To address the imbalance among riders, platforms, and merchants in food delivery within the context of Smart Cities, this study proposes an Intelligent Food Delivery model incorporating Dynamic Weather Conditions. A multi-objective optimization framework considering customer satisfaction, rider delivery pressure, and platform efficiency is established. An Improved Sparrow Search Algorithm (ISSA), enhanced by Sin-chaotic, Golden Sine, Adaptive t-distribution, and Dynamic Selection mechanisms, demonstrates superior convergence speed and accuracy. Simulations under Clear, Rainy, and Snowy scenarios validate ISSA's effectiveness in reducing rider pressure and optimizing delivery routes, providing robust theoretical guidance for intelligent logistics management in Smart Cities.
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Huang et al. (2025) studied this question.
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