Improving Instant Delivery Efficiency: Integrating Learning Effects into Strategic Rider Assignment Models

Tijun Fan (), Ming Yang (), Jingyi Chen () and Qiuchen Gu
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Tijun Fan: School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China
Ming Yang: School of Mathematics, East China University of Science and Technology, Shanghai 200237, P. R. China
Jingyi Chen: Economics and Management School, Minjiang University, Fuzhou 350108, P. R. China
Qiuchen Gu: School of Business, East China University of Science and Technology, Shanghai 200237, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2025, vol. 42, issue 01, 1-28

Abstract: High volatility in customer demand orders during peak and off-peak periods is a great challenge for instant delivery. In this paper, considering the rider familiarity with different areas and the learning effect, we establish two models for different rider assignment strategies: Maximum efficiency model during the peak period and Training familiarity model during the off-peak period. Meanwhile, a hybrid algorithm parallel genetic algorithm and a large-scale neighborhood search (PGA-LNS) is designed to solve the models. The results of two comparative experiments and 50-cycle peak and off-peak alternating experiments show that adopting the Maximum efficiency model in the peak period and the Training familiarity model in the off-peak period is beneficial for instant delivery to achieve overall flexibility, stability, and delivery efficiency.

Keywords: E-commerce instant delivery; familiarity; learning effect; riders; peak/off-peak period (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0217595924400128

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