目的 为探索包装废弃物排放量不确定性对收运路径的影响，提高收运作业质量，本文研究了考虑模糊排放量的包装废弃物多转运中心收运路径问题。方法 首先，基于模糊可信性理论，引入三角模糊数刻画收集点废弃物排放量，构建以最小收运里程为目标函数、含决策者主观偏好约束的废弃物多转运中心收运路径优化模型。其次，设计了包含12种邻域准则的自适应大邻域搜索(Adaptive Large Neighborhood Search，ALNS)算法。最后，算例部分比较了确定排放量与模糊排放量下的不同废弃物收运方案，并利用多种智能优化算法求解，同时对决策者主观偏好值进行了灵敏度分析。结果 考虑模糊排放量的废弃物收运里程比确定排放量收运里程多262.34 km，ALNS算法求解性能优于蚁群优化算法(Ant Colony Optimization，ACO)等3种算法，决策者最佳主观偏好值是0.6。结论 本研究能有效应对不确定排放量的挑战，为相关领域提供理论支持和参考依据。

The work aims to investigate the multi-depot packaging waste vehicle routing problem considering fuzzy emission to study the impact of uncertain packaging waste generation on collection routes and improve the quality of collection operations. First, based on the fuzzy credibility theory, triangular fuzzy numbers were introduced to represent waste generation at collection points, and an optimization model was constructed with the objective of minimizing collection mileage while incorporating the decision maker's subjective preference constraints. Secondly, an Adaptive Large Neighborhood Search (ALNS) algorithm with 12 neighborhood criteria was designed. Finally, in the case study, different waste collection schemes under both deterministic and fuzzy emission conditions were compared, employing various intelligent optimization algorithms and conducting sensitivity analysis on the decision maker's subjective preference values. The collection mileage considering fuzzy emission was 262.34 km longer than that under deterministic demand. The ALNS algorithm outperformed Ant Colony Optimization (ACO) and three other algorithms in terms of solution performance, with the optimal subjective preference value for the decision maker being 0.6. This study effectively addresses the challenges posed by uncertain emissions, providing theoretical support and reference for related fields.