Affiliations: [a] The Tender and Procurement Centre, Neijiang Normal University, Neijiang, Sichuan, China | [b] The School of Artificial Intelligence, Neijiang Normal University, Neijiang, Sichuan, China
Correspondence:
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Corresponding author: Min Zhang, The Tender and Procurement Centre, Neijiang Normal University, Neijiang, Sichuan 641000, China. E-mail: zhangmin@njtc.edu.cn.
Abstract: In response to the low efficiency of intelligence in traditional logistics industry and transaction services, the lack of effective consensus mechanisms, and the difficulty in fully considering the mutual influence and conflict between multiple objectives, this article adopts multi-objective consensus algorithm (MOCA) to study the optimization of objectives in the logistics industry and transaction services. Firstly, the needs and objective functions of the shipper, logistics service provider, and recipient can be modeled, and the Analytic Hierarchy Process (AHP) and exponential smoothing methods can be used for target weighting and dynamic weight adjustment. Then, the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm can be introduced to balance the benefits of all parties and find the optimal solution set for the optimization objective. Finally, a consensus mechanism can be established by combining Nash equilibrium and bargaining solution game theory models to achieve fairness in cooperation among the shipper, logistics service provider, and recipient. The experiment collected daily operation records and management systems from a logistics enterprise in Wuhan from June to December 2023. The results showed that in terms of overall efficiency, MOPSO game theory reached 96.5%, an improvement of 6.3% compared to PSO (Particle Swarm Optimization) game theory, and the Benefit Fairness Index (BFI) reached 90.5%. By combining MOCA to optimize the objectives of logistics and transaction services, the intelligence efficiency is greatly improved, ensuring the maximization of benefits and satisfaction for stakeholders.
Keywords: Logistics industry, transaction services, multi-objective consensus algorithm, game theory model, consensus mechanism
