Author(s)

Rong Liu ¹, Weibin Zhao ¹, Shuhong Zhou ¹

Affiliation(s)

¹ Guangzhou Maritime University, Guangzhou, Guangdong, 510725, China

Corresponding Author

Weibin Zhao

ABSTRACT

With the continuous expansion of the food delivery market, the challenges of order allocation and route optimization in crowdsourced delivery have emerged as a critical research focus. While existing studies primarily concentrate on meeting customer demands, improving delivery efficiency, and reducing costs, they often overlook the interests of delivery riders. Addressing this gap, this paper establishes maximum working hours as a constraint for riders while ensuring service quality and maximizing their earnings. A comprehensive weighting system is designed to balance three key factors: delivery time, order deadlines, and rider compensation. The proposed algorithm optimizes routes through a greedy insertion method, aiming to deliver high-quality solutions within riders' available timeframes. Through computational simulations, the study provides actionable recommendations for enhancing order allocation and route optimization in crowdsourced delivery systems.

KEYWORDS

Crowdsourcing logistics; order allocation; path optimization

CITE THIS PAPER

Rong Liu, Weibin Zhao*, Shuhong Zhou, Research on delivery optimization of food delivery orders based on crowdsourcing platform. Computing, Performance and Communication Systems (2025) Vol. 9: 96-105. DOI: http://dx.doi.org/10.23977/cpcs.2025.090113.

REFERENCES

[1] Nan Feiyan. Research on Service Combinations in Crowdsourced Delivery [D]. Zhejiang Sci-Tech University, 2022.
[2] Xie Naiming, Wu Qiao, Zheng Shaoxiang. A Cross-Supplier Order Allocation Model for Centralized Integrated Scheduling in Cloud Platforms [J]. Control and Decision, 2020,35(03):667-676.
[3] Yang Donglin. Integrated Decision-Making for Delivery and Pickup in O2O Scenarios [D]. Shanghai Jiao Tong University, 2021.
[4] Zhang Qi. Dynamic Order Allocation Optimization with Rider Experience Consideration in O2O Food Delivery [D]. Dongbei University of Finance and Economics, 2021.
[5] Bian Zhe. Research on Crowdsourced Food Delivery Order Allocation and Delivery Optimization Based on Equilibrium Criteria [D]. Xi'an University of Electronic Science and Technology, 2024.
[6] Jiang Li, Wang Jing, Liang Changyong, and Zhao Shuping. A Study on Crowdsourcing Delivery Path Optimization Using an Improved Ant Colony Algorithm [J]. Computer Engineering and Applications, 2022,55(08):244-249.
[7] Li Xiaoman. Research on O2O Food Delivery Crowdsourcing Path Optimization [D]. Dalian Maritime University, 2023.
[8] Chen Luqian. Real-time delivery route optimization for O2O food delivery based on real-time orders [D]. Beijing Jiaotong University, 2024.
[9] Li Hongmei. Research on Optimizing O2O Food Delivery Crowdsourcing Instant Delivery under Different Incentive Mechanisms [D]. Chongqing Jiaotong University, 2022.

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