基于遗传算法的低碳物流配送路径优化研究

发布时间：2018-10-15 15:26

【摘要】：在世界各国关注并推行低碳经济和可持续发展战略的背景下，我国作为主要的能源消耗和碳排放大国，在环境污染和能源消耗日益严重的压力下，通过发展低碳经济模式实现可持续发展已成为必然趋势。以化石燃料燃烧为能源的交通运输业是碳排放的主要来源之一，其低碳经济运行模式备受关注。因此承担起环保责任的运输业必须结合节能减排形势，实施低碳运输，，加快绿色低碳环保交通运输模式的发展。本文主要研究考虑低碳因素的带软时间窗和具有多配送中心的车辆路径优化问题。文章首先结合低碳物流的配送特点，从车辆路径问题相关研究理论出发，在车辆路径优化基本模型中考虑了车辆行驶距离对燃油消耗量和二氧化碳排放量的影响因素，分别建立了以总成本最小化为目标的带软时间窗的低碳车辆路径模型和具有多配送中心的低碳车辆路径模型。在此基础上，根据研究的实际问题和模型需要，利用遗传算法的基本原理对所建模型进行了求解算法设计，并运用MATLAB进行编程，使所建模型求解最终得以实现。最后以G连锁超市在该地区的实际配送数据作为实验数据，选取两个配送实例分别对本文构建的两个模型和设计的的遗传算法进行验证。模型比较和数值分析表明，基于本文构建的模型和自适应遗传算法所提出的低碳车辆路径方案，得到了最小配送成本和最优的配送路线，可以满足现实顾客的需求，具有节省物流成本、提高经济效益的优势；数值算例结果验证了本文提出的关于低碳路径规划两种应用情况的可行性，为节能减排和发展低碳经济提供了新的理论方法，对于物流企业为提高顾客个性化需求的低碳运营提供了具有参考价值的解决方案。
[Abstract]:Under the background of low carbon economy and sustainable development strategy, China, as a major energy consumption and carbon emission country, is under increasing pressure of environmental pollution and energy consumption. It has become an inevitable trend to realize sustainable development by developing low-carbon economy model. Transportation with fossil fuel combustion as the energy source is one of the main sources of carbon emissions, and its low carbon economic operation model has attracted much attention. Therefore, the transportation industry, which bears the responsibility of environmental protection, must combine the situation of energy saving and emission reduction, implement low-carbon transportation, and accelerate the development of green and low-carbon transportation mode. In this paper, the problem of vehicle routing optimization with soft time windows and multiple distribution centers is studied. Based on the distribution characteristics of low-carbon logistics and the related theory of vehicle routing problem, this paper considers the influence factors of vehicle travel distance on fuel consumption and carbon dioxide emissions in the basic model of vehicle path optimization. A low carbon vehicle routing model with soft time window and a low carbon vehicle routing model with multiple distribution centers are established with the goal of minimizing total cost. On this basis, according to the practical problems and the needs of the model, the algorithm of solving the model is designed based on the basic principle of genetic algorithm, and the programming is carried out by using MATLAB, so that the solution of the model can be realized finally. Finally, taking the actual distribution data of G chain supermarket in this area as experimental data, two distribution examples are selected to verify the two models and the genetic algorithm designed in this paper. The model comparison and numerical analysis show that the low carbon vehicle routing scheme based on the model constructed in this paper and the adaptive genetic algorithm can get the minimum distribution cost and the optimal distribution route, which can meet the needs of real customers. It has the advantages of saving logistics cost and improving economic benefit. The numerical results verify the feasibility of the two applications of low-carbon path planning proposed in this paper, and provide a new theoretical method for energy saving and emission reduction and the development of low-carbon economy. For logistics enterprises to improve customer demand for low-carbon operations to provide a reference value of the solution.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP18;F259.2

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