How shared information contributes: A novel revenue allocation method for collaborative instant delivery with unmanned vehicles

Meng Liu, Mu Du and Mengqi Yu

Transportation Research Part E: Logistics and Transportation Review, 2025, vol. 202, issue C

Abstract: 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.

Keywords: Contribution-based revenue allocation; Collaborative instant delivery; Value of shared information; Heterogeneous information sharing; Unmanned vehicles (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.tre.2025.104274

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