{"title":"共享信息如何贡献:无人驾驶车辆协同即时交付的一种新的收益分配方法","authors":"Meng Liu, Mu Du, Mengqi Yu","doi":"10.1016/j.tre.2025.104274","DOIUrl":null,"url":null,"abstract":"<div><div>Fair revenue allocation is essential for ensuring the stability of coalitions in collaborative logistics. This study examines horizontal collaboration among enterprises that provide instant delivery services using unmanned vehicles (UVs). While data-driven decision-making enhances efficiency, many enterprises hesitate to share operational information due to concerns about competitiveness. Therefore, designing a revenue allocation mechanism that incentivizes information sharing is a key challenge in collaborative delivery. To address this issue, we propose a novel contribution-based revenue allocation method that explicitly accounts for the value of shared information in addition to contributions from providing order and UVs. Specifically, we use the Shapley value to quantify the contribution of shared information on coalition performance. Furthermore, we formulate the decision problems arising in such collaborations and develop efficient solution methods. The numerical results show that both the final coalition structure and enterprises’ profits are influenced by their information disclosure preferences. More importantly, our proposed revenue allocation method effectively incentivizes the sharing of higher-quality information, thereby strengthening collaboration and improving overall system efficiency. This study is the first to explicitly address the heterogeneity of information sharing in collaborative delivery and to quantify the contribution of shared information within a revenue allocation framework, providing valuable insights for designing sustainable and data-driven logistics collaborations.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"202 ","pages":"Article 104274"},"PeriodicalIF":8.8000,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How shared information contributes: A novel revenue allocation method for collaborative instant delivery with unmanned vehicles\",\"authors\":\"Meng Liu, Mu Du, Mengqi Yu\",\"doi\":\"10.1016/j.tre.2025.104274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Fair revenue allocation is essential for ensuring the stability of coalitions in collaborative logistics. This study examines horizontal collaboration among enterprises that provide instant delivery services using unmanned vehicles (UVs). While data-driven decision-making enhances efficiency, many enterprises hesitate to share operational information due to concerns about competitiveness. Therefore, designing a revenue allocation mechanism that incentivizes information sharing is a key challenge in collaborative delivery. To address this issue, we propose a novel contribution-based revenue allocation method that explicitly accounts for the value of shared information in addition to contributions from providing order and UVs. Specifically, we use the Shapley value to quantify the contribution of shared information on coalition performance. Furthermore, we formulate the decision problems arising in such collaborations and develop efficient solution methods. The numerical results show that both the final coalition structure and enterprises’ profits are influenced by their information disclosure preferences. More importantly, our proposed revenue allocation method effectively incentivizes the sharing of higher-quality information, thereby strengthening collaboration and improving overall system efficiency. This study is the first to explicitly address the heterogeneity of information sharing in collaborative delivery and to quantify the contribution of shared information within a revenue allocation framework, providing valuable insights for designing sustainable and data-driven logistics collaborations.</div></div>\",\"PeriodicalId\":49418,\"journal\":{\"name\":\"Transportation Research Part E-Logistics and Transportation Review\",\"volume\":\"202 \",\"pages\":\"Article 104274\"},\"PeriodicalIF\":8.8000,\"publicationDate\":\"2025-07-08\",\"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/S1366554525003151\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525003151","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
How shared information contributes: A novel revenue allocation method for collaborative instant delivery with unmanned vehicles
Fair revenue allocation is essential for ensuring the stability of coalitions in collaborative logistics. This study examines horizontal collaboration among enterprises that provide instant delivery services using unmanned vehicles (UVs). While data-driven decision-making enhances efficiency, many enterprises hesitate to share operational information due to concerns about competitiveness. Therefore, designing a revenue allocation mechanism that incentivizes information sharing is a key challenge in collaborative delivery. To address this issue, we propose a novel contribution-based revenue allocation method that explicitly accounts for the value of shared information in addition to contributions from providing order and UVs. Specifically, we use the Shapley value to quantify the contribution of shared information on coalition performance. Furthermore, we formulate the decision problems arising in such collaborations and develop efficient solution methods. The numerical results show that both the final coalition structure and enterprises’ profits are influenced by their information disclosure preferences. More importantly, our proposed revenue allocation method effectively incentivizes the sharing of higher-quality information, thereby strengthening collaboration and improving overall system efficiency. This study is the first to explicitly address the heterogeneity of information sharing in collaborative delivery and to quantify the contribution of shared information within a revenue allocation framework, providing valuable insights for designing sustainable and data-driven logistics collaborations.
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.