摘要
十八大经济工作会议后,政府与企业将绿色低碳放到更重要的位置上来,城镇建设、传统制造等行业均需思考如何变得更绿色,对于物流运输行业更是如此,本文的创新之处在于以燃油消耗最小化为目标,在同时取货送货的逆向物流条件下,建立了一种带行驶距离、货物重量、道路路面、道路坡度等四大影响行驶油耗因素的VRPSDP模型.算法上做了蚁群算法在启发式因子的改进,并在信息素更新方面借鉴并行蚁群算法的优点,通过一个处理机模拟出并行蚁群算法特点,更好的模仿了蚂蚁在真实自然环境中的并行策略,接着探讨了影响蚁群算法的算法参数设置.实证结果表明,优化方案取得了令人满意的效果,从而验证了本文所提出的方法论的科学性和有效性.
After the eighteenth central economic working conference of the CPC, the government and enterprises are giving priority to Green industry, which applies for Towns construction, traditional manufacturing industries, especially for the logistics and transport. Different from the traditional VRP, we set minimizing fuel consumption as the objective rather than the transport distances, and establish a VRP model that take 4 factors that are essential to fuel consumption： running distance, cargo weight, road pavement, and road grade, under the VRP with simultaneous Pick-up and Delivery（VRPSDP）. A new heuristic factor is adopted, the pheromone update is also a new version in order to better mimic the ants＇ parallel strategy in natural environment. Then we explore the parameters setting which affect ACS＇s performances a lot. The empirical results show that the optimization program has achieved satisfactory results, thus validating the scientific and effectiveness of the proposed methodology.
出处
《计算机系统应用》
2013年第7期127-132,160,共7页
Computer Systems & Applications
基金
国家科技部科技支撑计划重大项目(2006BAH02A07)
