基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题，提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题，并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划，而后在每个时域末，综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明，相较于传统的静态收运方案，动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时，显著降低由于清运不及时造成的环境二次污染的风险。

Research on optimization of dynamic collection and transportation route of garbage classification based on intelligent garbage cans

In order to solve the problem of secondary environmental pollution and uncertainty of waste generation in the process of urban domestic waste sorting and transportation, this paper proposed a dynamic vehicle collection and transportation route optimization method based on intelligent garbage cans. Taking the minimum of carbon emission cost, fuel consumption cost, fixed cost and vehicle late arrival penalty cost as the optimization objective, this paper established dynamic vehicle routing optimization model. The dynamic vehicle route optimization method transformed the dynamic problem into a series of static problems by means of rolling time domain, and designed a two-stage algorithm to solve the model. Firstly, this method used particle swarm algorithm to plan the path of the collection and transportation vehicles, and then at the end of each time domain, comprehensively considered the location of newly added garbage cans and the amount of garbage, the location and loading of vehicles to dynamically adjust existing vehicle routes. The research results show that, compared with the traditional static collection and transportation scheme, the dynamic garbage collection and transportation scheme can significantly reduce the risk of secondary environmental pollution caused by untimely removal and transportation while reducing vehicle transportation costs and carbon emission costs.