A Time-Dependent Vehicle Routing Problem for Instant Delivery Based on Memetic Algorithm

Cui, Shuxian; Sun, Qian; Zhang, Qian

doi:10.1155/2022/5099008

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Sun et al [35] re-established a dynamic vehicle route planning model for pick-up and delivery according to the impact of four dynamic events on vehicle route planning and distribution services, and they designed a dynamic algorithm framework to solve it. Cui et al [36] proposed a vehicle routing problem model considering two types of customer time windows under time-dependent road networks, and they designed a memetic algorithm combined with genetic algorithm and variable neighborhood search to solve the problem. Bi et al [37] proposed an improved ant lion optimizer (IALO) to solve the problem.…”

Section: Takeout Deliverymentioning

confidence: 99%

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Lu,

Jiang,

et al. 2024

JTAER

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Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

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Section: Takeout Deliverymentioning

confidence: 99%

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Lu,

Jiang,

et al. 2024

JTAER

View full text Add to dashboard Cite

show abstract

“…Chen et al (2022) and Wang et al (2020) proposed a hybrid algorithm to enable the platform to respond to instant delivery at the city level. Cui et al (2022) and Y. Zhang et al (2020) respectively established models for optimizing customer satisfaction under the timely delivery service.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Strategies of the Online-to-Offline Instant Delivery Service of Grocery Retailers

Qin,

Yuhan,

Cuiting

et al. 2023

Sage Open

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The advantages of an O2O instant delivery service over the traditional retail model for grocery retailers in the local market lie in the ability to increase sales by expanding consumer channels. This study aims to explore how merchants can optimize their pricing and delivery service decisions, including order delivery fees, range, and starting price, to maximize profit with the adoption of instant delivery services. Using the Stackelberg game model, the research examines the retailers’ optimal decision-making within the classical Hotelling linear city model while considering a more realistic cost differentiation between online and offline services. The analysis incorporates variations in the number of consumer purchases and geographic locations. The study finds that increasing product prices while maintaining zero delivery fees consistently outperforms charging delivery fees while keeping prices constant in terms of their impact on retailers. Additionally, rarely-discussed aspects like starting delivery price and delivery range are also considered. Comparing parameter variations between the traditional retail model and the O2O instant delivery model leads to three primary conclusions. Firstly, the cost disparity between online and offline services significantly affects the optimal price and profit for the retailer. Secondly, when the cost of online service is slightly higher, setting a starting delivery price can enhance retailers’ profits compared to not having a starting price. Finally, the study outlines three strategies for implementing the O2O instant delivery model and suggests that defining a reasonable delivery range can help merchants reduce costs, improve delivery efficiency, and ultimately increase profits.

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Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery

Hou,

Guo,

Han

et al. 2024

IEEE Trans. Cybern.

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A Time-Dependent Vehicle Routing Problem for Instant Delivery Based on Memetic Algorithm

Cited by 5 publications

References 16 publications

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Optimal Strategies of the Online-to-Offline Instant Delivery Service of Grocery Retailers

Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery

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