Adaptive incentive mechanism with predictors for on-time attended home delivery problem

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

The widespread use of the Internet and smart devices has led to a fast growth in online shopping, offering new chances for online retailers to boost profits. However, this expansion has also brought various challenges, such as the heavy workload faced by delivery riders. To meet customers’ delivery time preferences and increase earnings, riders often work long hours, especially during busy periods. This study explores how historical delivery data can be used to balance workload in on-time attended home delivery. Drawing on the actual delivery operations and data of an online shopping platform, we propose a framework that combines delivery demand and customer behavior predictors with an adaptive incentive system to balance rider workload. Specifically focusing on same-day attended home delivery, we introduce a method to forecast future delivery demand, an algorithm to estimate customer choice behavior using a simple model, and an adaptive incentive system to influence customer decisions and achieve workload balance. We show that as order volume increases, the proposed incentive system achieves the pre-determined workload target. Using real data, we conduct numerical experiments which not only underscore the superior predictive performance of our models but also affirm the efficacy of the proposed incentive structure.

针对准时送货上门问题的带有预测器的自适应激励机制
互联网和智能设备的广泛使用带动了在线购物的快速增长,为在线零售商提供了提高利润的新机会。然而,这种扩张也带来了各种挑战,例如外卖骑手面临的繁重工作量。为了满足客户对送货时间的偏好并增加收入,外卖骑手往往需要长时间工作,尤其是在繁忙时期。本研究探讨了如何利用历史配送数据来平衡准时到家配送的工作量。我们借鉴了一个在线购物平台的实际配送操作和数据,提出了一个将配送需求和客户行为预测与自适应激励系统相结合的框架,以平衡骑手的工作量。特别是针对当天到家的送货服务,我们介绍了一种预测未来送货需求的方法、一种使用简单模型估计客户选择行为的算法,以及一种影响客户决策并实现工作量平衡的自适应激励系统。我们的研究表明,随着订单量的增加,所提出的激励系统能够实现预定的工作量目标。我们利用真实数据进行了数值实验,不仅证明了我们的模型具有卓越的预测性能,而且肯定了所建议的激励结构的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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