Xiaoyu Zhang , Peng Guo , Jianbin Xin , Andrea D’Ariano , Yi Wang
{"title":"Enabling within-the-hour fresh food deliveries: Integrated order batching and zone-picking through overhead conveyors","authors":"Xiaoyu Zhang , Peng Guo , Jianbin Xin , Andrea D’Ariano , Yi Wang","doi":"10.1016/j.tre.2025.104133","DOIUrl":null,"url":null,"abstract":"<div><div>New retail concepts that embrace a hybrid “online + offline” business paradigm promise super-fast order fulfillment of groceries within the next hour. In this ”online + offline” retail scenario, it is crucial to efficiently fulfill many online orders within the stipulated time while adhering to the layout rules of offline retail products. Zone-picking and overhead conveyors have been introduced to cope with the significant volume of orders batching, picking, and delivering fresh products. This has led to a new integrated order batching and picking decision problem, aiming for human-machine reconciliation in Industry 5.0. For such a problem, two new mixed integer linear programming models are developed, considering minimizing the number of picking task releases and the total delay time of all orders. The computational complexity of the two problems is provided. A customized two-stage heuristic framework is developed to solve the two models with distinct solution space structures. Numerical experiments have been conducted to test the performance of the proposed methods and provide solution analysis for practical insights. The results show that the proposed heuristic reduces the number of picking tasks for workers by 19% and the total delay in completing orders by 74% compared to prevailing store practices. The proposed framework complements the existing models in the literature. It contributes to developing a comprehensive analysis of order picking by integrating human factors into operational efficiency improvement in the new retailing industry.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"199 ","pages":"Article 104133"},"PeriodicalIF":8.3000,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525001747","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
New retail concepts that embrace a hybrid “online + offline” business paradigm promise super-fast order fulfillment of groceries within the next hour. In this ”online + offline” retail scenario, it is crucial to efficiently fulfill many online orders within the stipulated time while adhering to the layout rules of offline retail products. Zone-picking and overhead conveyors have been introduced to cope with the significant volume of orders batching, picking, and delivering fresh products. This has led to a new integrated order batching and picking decision problem, aiming for human-machine reconciliation in Industry 5.0. For such a problem, two new mixed integer linear programming models are developed, considering minimizing the number of picking task releases and the total delay time of all orders. The computational complexity of the two problems is provided. A customized two-stage heuristic framework is developed to solve the two models with distinct solution space structures. Numerical experiments have been conducted to test the performance of the proposed methods and provide solution analysis for practical insights. The results show that the proposed heuristic reduces the number of picking tasks for workers by 19% and the total delay in completing orders by 74% compared to prevailing store practices. The proposed framework complements the existing models in the literature. It contributes to developing a comprehensive analysis of order picking by integrating human factors into operational efficiency improvement in the new retailing industry.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.