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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform
verfasst von : Min-Yan Tsai, Guo-Yu Lin, Jiang-Yi Zeng, Chia-Mu Yu, Chi-Yuan Chen, Hsin-Hung Cho
Erschienen in: Bio-inspired Information and Communications Technologies
Verlag: Springer Nature Switzerland
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Abstract
Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA’s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.