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

被引：1

作者：

Fan, Tijun ^{[1
]}

Yang, Ming ^{[2
]}

Chen, Jingyi ^{[3
]}

Gu, Qiuchen ^{[1
]}

机构：

[1] East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

[2] East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

[3] Minjiang Univ, Econ & Management Sch, Fuzhou 350108, Peoples R China

来源：

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH | 2025年 / 42卷 / 01期

基金：

中国国家自然科学基金;

关键词：

E-commerce instant delivery; familiarity; learning effect; riders; peak/off-peak period; DISTRIBUTED EVOLUTIONARY ALGORITHMS; VEHICLE-ROUTING PROBLEM; ORDER PICKING; TIME;

D O I：

10.1142/S0217595924400128

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

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.

引用

页数：28

共 26 条

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