Cross regional online food delivery: Service quality optimization and real-time order assignment

IF 4.1 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Operations Research Pub Date : 2024-10-24 DOI:10.1016/j.cor.2024.106877
Farhana Huq , Nahar Sultana , Palash Roy , Md. Abdur Razzaque , Shamsul Huda , Mohammad Mehedi Hassan
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Abstract

Online food delivery (OFD) represents a rapidly evolving e-business application that leverages cloud computing data centers, playing a crucial role in meeting the demands of urban lifestyles. With diverse order fulfillment features and increasing expectations for service quality, the task of effectively assigning riders for timely long-distance, cross-regional deliveries presents a significant engineering challenge. Previous studies often relied on traditional rider allocation methods that fail to account for varying capacities, or they utilized non-intelligent systems that did not adequately address fluctuating order demands and service delays. In this study, we introduce a robust Mixed Integer Linear Programming (MILP) optimization framework designed to minimize the total service time and delivery cost for cross-regional orders. This framework divides a large OFD area into multiple regions and utilizes both transfer vehicles and riders to optimize deliveries. To enhance the predictive accuracy of our model, we incorporate advanced machine learning techniques. Specifically, we employ the Long Short-Term Memory (LSTM) model to forecast regional order demands accurately, reflecting the dynamic nature of the marketplace. Additionally, Extreme Gradient Boosting (XGBoost) is tailored to dynamically predict travel times from restaurants to customer locations, facilitating more precise scheduling and resource allocation within the MILP framework. These machine learning techniques significantly bolster the MILP framework by providing detailed, accurate predictions that improve decision-making processes and adaptability to real-time conditions. Acknowledging the complexity of this optimization problem, we further enhance our approach by integrating a meta-heuristic algorithm, Adaptive Large Neighbor Search (ALNS), which efficiently assigns orders to the appropriate transfer vehicles and riders within polynomial time. Our Cross Regional Online Food Delivery (XROFD) system is meticulously designed to optimize both customer satisfaction and rider incentives. Simulation experiments confirm that the XROFD system not only reduces service times and delivery costs but also markedly enhances customer satisfaction and provides superior incentives for riders, outperforming existing state-of-the-art methods.
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跨区域在线食品配送:服务质量优化和实时订单分配
在线食品配送(OFD)是一种利用云计算数据中心快速发展的电子商务应用,在满足城市生活方式的需求方面发挥着至关重要的作用。随着订单履行功能的多样化和人们对服务质量期望的不断提高,如何有效分配骑手以实现及时的长距离、跨区域配送成为一项重大的工程挑战。以往的研究通常依赖于传统的骑手分配方法,这种方法未能考虑到不同的容量,或者使用的是非智能系统,不能充分解决订单需求波动和服务延迟问题。在本研究中,我们引入了一个稳健的混合整数线性规划(MILP)优化框架,旨在最大限度地减少跨区域订单的总服务时间和交付成本。该框架将一个大的 OFD 区域划分为多个区域,并利用换乘车辆和乘客来优化配送。为了提高模型的预测准确性,我们采用了先进的机器学习技术。具体来说,我们采用了长短期记忆(LSTM)模型来准确预测区域订单需求,以反映市场的动态性质。此外,我们还采用了极端梯度提升(XGBoost)技术,以动态预测从餐厅到客户所在地的旅行时间,从而在 MILP 框架内实现更精确的调度和资源分配。这些机器学习技术大大加强了 MILP 框架,提供了详细、准确的预测,改善了决策过程,提高了对实时条件的适应性。考虑到这一优化问题的复杂性,我们通过整合元启发式算法--自适应大邻域搜索(ALNS)--进一步增强了我们的方法,该算法可在多项式时间内高效地将订单分配给适当的转运车辆和乘客。我们的跨区域在线食品配送(XROFD)系统经过精心设计,可同时优化客户满意度和骑手激励机制。模拟实验证实,XROFD 系统不仅缩短了服务时间,降低了配送成本,还显著提高了客户满意度,并为乘客提供了优越的激励机制,优于现有的最先进方法。
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 & Operations Research
Computers & Operations Research 工程技术-工程:工业
CiteScore
8.60
自引率
8.70%
发文量
292
审稿时长
8.5 months
期刊介绍: Operations research and computers meet in a large number of scientific fields, many of which are of vital current concern to our troubled society. These include, among others, ecology, transportation, safety, reliability, urban planning, economics, inventory control, investment strategy and logistics (including reverse logistics). Computers & Operations Research provides an international forum for the application of computers and operations research techniques to problems in these and related fields.
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