城市垃圾分类运输的多目标优化模型与混合启发式算法——基于中转站选址与碳排放约束的路径调度研究

Multi-Objective Optimization Model and Hybrid Heuristic Algorithm for Urban Waste Transportation—Path Scheduling Research Based on Transfer Station Location and Carbon Emission Constraints

摘要: 针对城市垃圾分类收集与运输过程中的多约束路径优化问题，本文构建集成运输成本、中转站建设成本与碳排放的多目标综合优化模型，统一纳入车辆载重、容积、行驶时间、中转站容量及非对称路网等现实约束。设计遗传算法与贪心策略融合的两阶段混合启发式算法进行求解：第一阶段基于贪心规则完成收集点至中转站的分配；第二阶段采用带约束的路径优化策略实现全局调度优化。针对模型的组合优化特性，通过分配优化与路径优化的协同机制提升求解效率与精度。基于30个收集点与5个候选中转站的算例实验结果显示，算法能够快速获得高质量可行解，优化后的调度方案在运输成本、碳排放与行驶时间等指标上均表现良好，验证了模型的合理性与算法的有效性，可为同类路径优化问题提供参考。

Abstract: Aiming at the multi-constrained path optimization problem in the classified collection and transportation of urban municipal solid waste, this paper constructs a multi-objective comprehensive optimization model integrating transportation cost, transfer station construction cost and carbon emission. The model uniformly incorporates practical constraints such as vehicle load, volume, travel time, transfer station capacity and asymmetric road network. A two-stage hybrid heuristic algorithm combining genetic algorithm and greedy strategy is designed to solve the problem. In the first stage, the allocation from collection points to transfer stations is completed based on the greedy rule. In the second stage, the constrained path optimization strategy is used to realize global scheduling optimization. For the combinatorial optimization characteristics of the model, the solution efficiency and accuracy are improved through the collaborative mechanism of allocation optimization and path optimization. The experimental results based on 30 collection points and 5 candidate transfer stations show that the algorithm can quickly obtain high-quality feasible solutions. The optimized scheduling scheme performs well in transportation cost, carbon emissions and travel time, which verifies the rationality of the model and the effectiveness of the algorithm, and can provide a reference for similar path optimization problems.