引入装卸时间的垃圾收运路线优化
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摘要
随着城市现代化水平的提高,垃圾的收运管理日益得到重视。垃圾的回收中转是垃圾收运系统中的一个重要环节,目前的研究大都集中在路线距离的优化,而未考虑到收运中的垃圾装卸等待时间优化。实际中转站收运中,垃圾的装卸时间同样会影响到中转站整体的运行效率,因此,文中将垃圾收运路线优化与回收车辆在中转站的排队装卸等待时间优化综合进行研究,建立了综合成本优化模型,并在传统蚁群算法的基础上进行改进对模型进行求解,得到近似全局最优方案。使用实际案例进行仿真,证明建立的模型是有效的,适用于当前的中转站收运问题。
Rubbish is the inevitable product of human activity. The increasing of garbage not only takes up the land but also pollutes the environment seriously. And it has caused damage to our health. In the face of a series problem,how to deal with the garbage is becoming an important issue in urban construction. Garbage disposal requires the proper disposal of waste and the reduction of cost and the improvement of efficiency. Transfer collection of garbage is an important link in garbage collection and transportation system. Scholars have done a lot of research in this field,but most of them only focused on the route optimization and did not consider the optimization of loading and unloading time in the process of garbage collection. The loading and unloading time would affect the running efficiency of the transfer station. So this article combines the removal route and the waiting time and the loading and unloading time for comprehensive research,and uses the ant algorithm to get the optimal solution.
Rubbish is the inevitable product of human activity. The increasing of garbage not only takes up the land but also pollutes the environment seriously. And it has caused damage to our health. In the face of a series problem,how to deal with the garbage is becoming an important issue in urban construction. Garbage disposal requires the proper disposal of waste and the reduction of cost and the improvement of efficiency. Transfer collection of garbage is an important link in garbage collection and transportation system. Scholars have done a lot of research in this field,but most of them only focused on the route optimization and did not consider the optimization of loading and unloading time in the process of garbage collection. The loading and unloading time would affect the running efficiency of the transfer station. So this article combines the removal route and the waiting time and the loading and unloading time for comprehensive research,and uses the ant algorithm to get the optimal solution.
引文
[1]明勇,王华军.基于改进混合蛙跳和GIS的城市垃圾车回收路径优化设计[J].计算机测量与控制,2014,22(12).
[2]王芳芳.基于改进蚁群算法的城市生活垃圾收运线路优化研究[D].北京:北京工业大学,2011.
[3]王文梅.基于单亲遗传算法的城市垃圾路线优化研究[D].成都:西南交通大学,2005.
[4]王桂琴,高博,路鹏,李国学.基于GIS和多目标的生活垃圾运输路线优化研究[J].环境科学与技术,2006,33(12).
[5]刘洁,何彦逢.基于GIS的成都市生活垃圾收运路线优化研究[J].西南师范大学学报(自然科学版),2013,38(4):80-87.
[6]宋薇,刘建国,聂永丰.城市生活垃圾收运路线优化研究[J].环境卫生工程.2008,16(1):11-15.
[7]贾学斌,刘冬梅,孙哲.用神经元理论优化生活垃圾收运路线[J].哈尔滨工业大学学报,2004,36(6):819-821.
[8]路玉龙,赵扶摇,韩靖,张鸿雁.城市生活垃圾收运路线优化的数学模型与算法[J].环境科学与管理,2010,35(6):46-50.
[9]吴勇刚,徐香新,刘梅,赵龙.基于遗传算法的最优城市垃圾收运路线探究[J].计算机仿真,2012(4):259-312.
[10]Anderson L E.A mathematical model for the optimization of a waste management system[J].Sanitary Enginetring Research Laboratory,SERL Report,No.68-1,USA.
[11]Anderson D.GIS supports garbage hauler franchising[J].GIS World,1992,5(5):36-38.
[12]M Candida Mourao,M Teresa Almeida.Lower-bounding and heauristic methods for a refuse collection routing problem[J].European Journal of Operational Research,2000,121:420-434.
[2]王芳芳.基于改进蚁群算法的城市生活垃圾收运线路优化研究[D].北京:北京工业大学,2011.
[3]王文梅.基于单亲遗传算法的城市垃圾路线优化研究[D].成都:西南交通大学,2005.
[4]王桂琴,高博,路鹏,李国学.基于GIS和多目标的生活垃圾运输路线优化研究[J].环境科学与技术,2006,33(12).
[5]刘洁,何彦逢.基于GIS的成都市生活垃圾收运路线优化研究[J].西南师范大学学报(自然科学版),2013,38(4):80-87.
[6]宋薇,刘建国,聂永丰.城市生活垃圾收运路线优化研究[J].环境卫生工程.2008,16(1):11-15.
[7]贾学斌,刘冬梅,孙哲.用神经元理论优化生活垃圾收运路线[J].哈尔滨工业大学学报,2004,36(6):819-821.
[8]路玉龙,赵扶摇,韩靖,张鸿雁.城市生活垃圾收运路线优化的数学模型与算法[J].环境科学与管理,2010,35(6):46-50.
[9]吴勇刚,徐香新,刘梅,赵龙.基于遗传算法的最优城市垃圾收运路线探究[J].计算机仿真,2012(4):259-312.
[10]Anderson L E.A mathematical model for the optimization of a waste management system[J].Sanitary Enginetring Research Laboratory,SERL Report,No.68-1,USA.
[11]Anderson D.GIS supports garbage hauler franchising[J].GIS World,1992,5(5):36-38.
[12]M Candida Mourao,M Teresa Almeida.Lower-bounding and heauristic methods for a refuse collection routing problem[J].European Journal of Operational Research,2000,121:420-434.