如何增加lpsolve R优化解决方案在hadoop集群上运行？
[英]How to augment lpsolve R optimization solution to run on a hadoop cluster?
问题描述
我正在使用R lpsolve包来优化我的运输模型。 我的代码运行正常，但由于我有大量的节点和路径，因此需要花费大量时间。 我计划在hadoop集群上运行我的代码。
请引导我关于我需要对代码进行的更改。 我认为在hadoop集群上运行优化可能是不可能的，因为我们可能最终得到局部最小值而不是全局最小值。
我在互联网上搜索“lpsolve hadoop”之类的术语，但没有得到任何有用的信息。
请指导我看一下我应该看的材料或例子。 =====================================更新1 =========== ===========================我遇到的原始问题就在这里 。
我进一步简化了问题，我正在解决的当前问题如下。
我附加了使用excel构建的R代码和输入数据文件。 在实际场景中，输入数据文件将使用SQL生成，并且长度将超过30,000行。
我的输入excel文件：
startlink endlink link_dsc lnk_type cons_type cost equality_const fpc_const max_const
"source","-","-","0" "vmi1","MM1","VMI","1" source_to_VMI supply equality supply 0 100 null null
"vmi1","MM1","VMI","0" "cust1","MM1","SHIP_CUST_1d_AIR","1" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","1" "cust1","MM1","SHIP_CUST_1d_AIR","2" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","2" "cust1","MM1","SHIP_CUST_1d_AIR","3" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","3" "cust1","MM1","SHIP_CUST_1d_AIR","4" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","4" "cust1","MM1","SHIP_CUST_1d_AIR","5" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","5" "cust1","MM1","SHIP_CUST_1d_AIR","6" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","6" "cust1","MM1","SHIP_CUST_1d_AIR","7" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","7" "cust1","MM1","SHIP_CUST_1d_AIR","8" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","8" "cust1","MM1","SHIP_CUST_1d_AIR","9" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","9" "cust1","MM1","SHIP_CUST_1d_AIR","10" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","0" "vmi1","MM1","VMI","1" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","1" "vmi1","MM1","VMI","2" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","2" "vmi1","MM1","VMI","3" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","3" "vmi1","MM1","VMI","4" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","4" "vmi1","MM1","VMI","5" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","5" "vmi1","MM1","VMI","6" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","6" "vmi1","MM1","VMI","7" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","7" "vmi1","MM1","VMI","8" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","8" "vmi1","MM1","VMI","9" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","9" "vmi1","MM1","VMI","10" vmi_to_vmi_inv flow null 0 null null null
"vmi1","MM1","VMI","10" "SINK","-","-","100" vmi_to_sink esacpe null 100 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","1" "cust1","MM1","CUST","1" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","2" "cust1","MM1","CUST","2" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","3" "cust1","MM1","CUST","3" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","4" "cust1","MM1","CUST","4" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","5" "cust1","MM1","CUST","5" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","6" "cust1","MM1","CUST","6" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","7" "cust1","MM1","CUST","7" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","8" "cust1","MM1","CUST","8" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","9" "cust1","MM1","CUST","9" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_1d_AIR","10" "cust1","MM1","CUST","10" shipcust_to_cust flow null 0 null null null
"cust1","MM1","CUST","4" "SINK","-","-","100" cust_to_sink flow demand 0 null null 50
"vmi1","MM1","VMI","0" "cust1","MM1","SHIP_CUST_2d_AIR","2" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","1" "cust1","MM1","SHIP_CUST_2d_AIR","3" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","2" "cust1","MM1","SHIP_CUST_2d_AIR","4" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","3" "cust1","MM1","SHIP_CUST_2d_AIR","5" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","4" "cust1","MM1","SHIP_CUST_2d_AIR","6" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","5" "cust1","MM1","SHIP_CUST_2d_AIR","7" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","6" "cust1","MM1","SHIP_CUST_2d_AIR","8" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","7" "cust1","MM1","SHIP_CUST_2d_AIR","9" vmi_to_shipcust flow null 5 null null null
"vmi1","MM1","VMI","8" "cust1","MM1","SHIP_CUST_2d_AIR","10" vmi_to_shipcust flow null 5 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","2" "cust1","MM1","CUST","2" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","3" "cust1","MM1","CUST","3" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","4" "cust1","MM1","CUST","4" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","5" "cust1","MM1","CUST","5" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","6" "cust1","MM1","CUST","6" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","7" "cust1","MM1","CUST","7" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","8" "cust1","MM1","CUST","8" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","9" "cust1","MM1","CUST","9" shipcust_to_cust flow null 0 null null null
"cust1","MM1","SHIP_CUST_2d_AIR","10" "cust1","MM1","CUST","10" shipcust_to_cust flow null 0 null null null
"source","-","-","0" "vmi2","MM2","VMI","2" source_to_VMI supply equality supply 0 50 null null
"vmi2","MM2","VMI","0" "cust1","MM2","SHIP_CUST_1d_AIR","1" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","1" "cust1","MM2","SHIP_CUST_1d_AIR","2" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","2" "cust1","MM2","SHIP_CUST_1d_AIR","3" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","3" "cust1","MM2","SHIP_CUST_1d_AIR","4" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","4" "cust1","MM2","SHIP_CUST_1d_AIR","5" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","5" "cust1","MM2","SHIP_CUST_1d_AIR","6" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","6" "cust1","MM2","SHIP_CUST_1d_AIR","7" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","7" "cust1","MM2","SHIP_CUST_1d_AIR","8" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","8" "cust1","MM2","SHIP_CUST_1d_AIR","9" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","9" "cust1","MM2","SHIP_CUST_1d_AIR","10" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","0" "vmi2","MM2","VMI","1" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","1" "vmi2","MM2","VMI","2" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","2" "vmi2","MM2","VMI","3" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","3" "vmi2","MM2","VMI","4" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","4" "vmi2","MM2","VMI","5" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","5" "vmi2","MM2","VMI","6" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","6" "vmi2","MM2","VMI","7" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","7" "vmi2","MM2","VMI","8" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","8" "vmi2","MM2","VMI","9" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","9" "vmi2","MM2","VMI","10" vmi_to_vmi_inv flow null 0 null null null
"vmi2","MM2","VMI","10" "SINK","-","-","100" vmi_to_sink esacpe null 100 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","1" "cust1","MM2","CUST","1" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","2" "cust1","MM2","CUST","2" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","3" "cust1","MM2","CUST","3" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","4" "cust1","MM2","CUST","4" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","5" "cust1","MM2","CUST","5" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","6" "cust1","MM2","CUST","6" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","7" "cust1","MM2","CUST","7" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","8" "cust1","MM2","CUST","8" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","9" "cust1","MM2","CUST","9" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_1d_AIR","10" "cust1","MM2","CUST","10" shipcust_to_cust flow null 0 null null null
"cust1","MM2","CUST","9" "SINK","-","-","100" cust_to_sink flow demand 0 null null 10
"vmi2","MM2","VMI","0" "cust1","MM2","SHIP_CUST_2d_AIR","2" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","1" "cust1","MM2","SHIP_CUST_2d_AIR","3" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","2" "cust1","MM2","SHIP_CUST_2d_AIR","4" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","3" "cust1","MM2","SHIP_CUST_2d_AIR","5" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","4" "cust1","MM2","SHIP_CUST_2d_AIR","6" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","5" "cust1","MM2","SHIP_CUST_2d_AIR","7" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","6" "cust1","MM2","SHIP_CUST_2d_AIR","8" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","7" "cust1","MM2","SHIP_CUST_2d_AIR","9" vmi_to_shipcust flow null 1.4 null null null
"vmi2","MM2","VMI","8" "cust1","MM2","SHIP_CUST_2d_AIR","10" vmi_to_shipcust flow null 1.4 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","2" "cust1","MM2","CUST","2" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","3" "cust1","MM2","CUST","3" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","4" "cust1","MM2","CUST","4" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","5" "cust1","MM2","CUST","5" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","6" "cust1","MM2","CUST","6" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","7" "cust1","MM2","CUST","7" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","8" "cust1","MM2","CUST","8" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","9" "cust1","MM2","CUST","9" shipcust_to_cust flow null 0 null null null
"cust1","MM2","SHIP_CUST_2d_AIR","10" "cust1","MM2","CUST","10" shipcust_to_cust flow null 0 null null null
"vmi1","MM1","VMI","0" "cust3","MM1","SHIP_CUST_1d_AIR","1" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","1" "cust3","MM1","SHIP_CUST_1d_AIR","2" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","2" "cust3","MM1","SHIP_CUST_1d_AIR","3" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","3" "cust3","MM1","SHIP_CUST_1d_AIR","4" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","4" "cust3","MM1","SHIP_CUST_1d_AIR","5" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","5" "cust3","MM1","SHIP_CUST_1d_AIR","6" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","6" "cust3","MM1","SHIP_CUST_1d_AIR","7" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","7" "cust3","MM1","SHIP_CUST_1d_AIR","8" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","8" "cust3","MM1","SHIP_CUST_1d_AIR","9" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","9" "cust3","MM1","SHIP_CUST_1d_AIR","10" vmi_to_shipcust flow null 15 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","1" "cust3","MM1","CUST","1" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","2" "cust3","MM1","CUST","2" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","3" "cust3","MM1","CUST","3" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","4" "cust3","MM1","CUST","4" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","5" "cust3","MM1","CUST","5" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","6" "cust3","MM1","CUST","6" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","7" "cust3","MM1","CUST","7" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","8" "cust3","MM1","CUST","8" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","9" "cust3","MM1","CUST","9" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_1d_AIR","10" "cust3","MM1","CUST","10" shipcust_to_cust flow null 0 null null null
"cust3","MM1","CUST","6" "SINK","-","-","100" cust_to_sink flow demand 0 null null 5
"vmi1","MM1","VMI","0" "cust3","MM1","SHIP_CUST_2d_AIR","2" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","1" "cust3","MM1","SHIP_CUST_2d_AIR","3" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","2" "cust3","MM1","SHIP_CUST_2d_AIR","4" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","3" "cust3","MM1","SHIP_CUST_2d_AIR","5" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","4" "cust3","MM1","SHIP_CUST_2d_AIR","6" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","5" "cust3","MM1","SHIP_CUST_2d_AIR","7" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","6" "cust3","MM1","SHIP_CUST_2d_AIR","8" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","7" "cust3","MM1","SHIP_CUST_2d_AIR","9" vmi_to_shipcust flow null 15 null null null
"vmi1","MM1","VMI","8" "cust3","MM1","SHIP_CUST_2d_AIR","10" vmi_to_shipcust flow null 15 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","2" "cust3","MM1","CUST","2" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","3" "cust3","MM1","CUST","3" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","4" "cust3","MM1","CUST","4" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","5" "cust3","MM1","CUST","5" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","6" "cust3","MM1","CUST","6" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","7" "cust3","MM1","CUST","7" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","8" "cust3","MM1","CUST","8" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","9" "cust3","MM1","CUST","9" shipcust_to_cust flow null 0 null null null
"cust3","MM1","SHIP_CUST_2d_AIR","10" "cust3","MM1","CUST","10" shipcust_to_cust flow null 0 null null null
"vmi2","MM2","VMI","0" "cust3","MM2","SHIP_CUST_1d_AIR","1" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","1" "cust3","MM2","SHIP_CUST_1d_AIR","2" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","2" "cust3","MM2","SHIP_CUST_1d_AIR","3" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","3" "cust3","MM2","SHIP_CUST_1d_AIR","4" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","4" "cust3","MM2","SHIP_CUST_1d_AIR","5" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","5" "cust3","MM2","SHIP_CUST_1d_AIR","6" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","6" "cust3","MM2","SHIP_CUST_1d_AIR","7" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","7" "cust3","MM2","SHIP_CUST_1d_AIR","8" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","8" "cust3","MM2","SHIP_CUST_1d_AIR","9" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","9" "cust3","MM2","SHIP_CUST_1d_AIR","10" vmi_to_shipcust flow null 1.8 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","1" "cust3","MM2","CUST","1" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","2" "cust3","MM2","CUST","2" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","3" "cust3","MM2","CUST","3" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","4" "cust3","MM2","CUST","4" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","5" "cust3","MM2","CUST","5" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","6" "cust3","MM2","CUST","6" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","7" "cust3","MM2","CUST","7" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","8" "cust3","MM2","CUST","8" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","9" "cust3","MM2","CUST","9" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_1d_AIR","10" "cust3","MM2","CUST","10" shipcust_to_cust flow null 0 null null null
"cust3","MM2","CUST","8" "SINK","-","-","100" cust_to_sink flow demand 0 null null 7
"vmi2","MM2","VMI","0" "cust3","MM2","SHIP_CUST_2d_AIR","2" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","1" "cust3","MM2","SHIP_CUST_2d_AIR","3" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","2" "cust3","MM2","SHIP_CUST_2d_AIR","4" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","3" "cust3","MM2","SHIP_CUST_2d_AIR","5" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","4" "cust3","MM2","SHIP_CUST_2d_AIR","6" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","5" "cust3","MM2","SHIP_CUST_2d_AIR","7" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","6" "cust3","MM2","SHIP_CUST_2d_AIR","8" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","7" "cust3","MM2","SHIP_CUST_2d_AIR","9" vmi_to_shipcust flow null 1.8 null null null
"vmi2","MM2","VMI","8" "cust3","MM2","SHIP_CUST_2d_AIR","10" vmi_to_shipcust flow null 1.8 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","2" "cust3","MM2","CUST","2" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","3" "cust3","MM2","CUST","3" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","4" "cust3","MM2","CUST","4" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","5" "cust3","MM2","CUST","5" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","6" "cust3","MM2","CUST","6" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","7" "cust3","MM2","CUST","7" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","8" "cust3","MM2","CUST","8" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","9" "cust3","MM2","CUST","9" shipcust_to_cust flow null 0 null null null
"cust3","MM2","SHIP_CUST_2d_AIR","10" "cust3","MM2","CUST","10" shipcust_to_cust flow null 0 null null null
"SINK","-","-","100" "source","-","-","0" closed_loop flow null 0 null null null
我的R代码如下：你必须获取上面的csv数据并将其粘贴到文件C：/dumy_network.csv中。
我的R代码可能不是很有效但它可以达到目的！
library("lpSolve", lib.loc="C:/Users/njog/Documents/R/win-library/3.0")
#get the data from CSV file
mydata <- read.csv("C:/dumy_network.csv", header=TRUE)
#build list of nodes (no repetition)
nodes=unique(c(as.character(mydata$startlink),as.character(mydata$endlink)))
#list of all links
links=mydata[,1:2]
#cost of moving material on each link - optimization problem objective coefficients
cost=mydata$cost
#decision variable is flow on each link. Objective is to minimize product of (cost on each link*flow on the link). Therefore, count of decision variable is equal to count of links.
#constraints matrix: for each node in nodes, incoming quantity should be equal to outgoing quantity.
constraints=matrix(0,sum(mydata$max_const!='null')+sum(mydata$equality_const!='null')+length(nodes),length(mydata$endlink))
for (i in 1:length(nodes) ) {
constraints[i,]=t(1*(nodes[i]==links[,1])-1*(nodes[i]==links[,2]))
}
#get constraints for equality constraints- in some cases we have to ship material exactly same as this quanity.
constraint1=matrix(mydata$equality_const,1,length(mydata$equality_const))
constraint1[constraint1=="null"]=0
constraint1=as.numeric(constraint1)
constraint1_length=which(constraint1!=0)
constraint1_final=matrix(0,length(constraint1_length),length(mydata$equality_const))
for (i in 1:length(constraint1_length) ) {
constraint1_final[i,constraint1_length[i]]=1
}
start=length(nodes)+1
end=length(nodes)+length(constraint1_length)
constraints[start:end,]=constraint1_final
#get constraints for maxconstraints - in some cases we cannot ship material exceeding this quanity.
constraint2=matrix(mydata$max_const,1,length(mydata$max_const))
constraint2[constraint2=="null"]=0
constraint2=as.numeric(constraint2)
constraint2_length=which(constraint2!=0)
constraint2_final=matrix(0,length(constraint2_length),length(mydata$max_const))
for (i in 1:length(constraint2_length) ) {
constraint2_final[i,constraint2_length[i]]=1
}
start=end+1
end=end+length(constraint2_length)
constraints[start:end,]=constraint2_final
#building direction of constraints
direction=c(rep("=",length(nodes)),rep("=",sum(mydata$equality_const!='null')),rep("<=",sum(mydata$max_const!='null')))
#building right hand side of constraints
b1=as.numeric(as.character(mydata$equality_const[constraint1_length]))
b2=as.numeric(as.character(mydata$max_const[constraint2_length]))
b=c(rep(0,length(nodes)),b1,b2)
res = lpSolve::lp('min', cost, constraints, direction, b, all.int = TRUE)
res$solution
answers1=data.frame(res$solution)
mydata=cbind(mydata,answers1)
=====================================更新2 =========== ===========================没有得到任何答案，所以试图简化我的问题：
页面的示例部分给出了一个简单的问题。 有没有人有想法如何使用Mapreduce解决它？ 我的意思是让我说我有类似的问题，但是有大量的变量和约束，那么有没有办法实现更快的处理？
1 个解决方案
解决方案1
2 2014-04-13 13:05:15
简而言之，您希望大规模地进行线性编程（lp），并且您对求解器的性能不满意。
途径
我建议采用以下方法。
求解
你需要使用这个求解器吗？ 以下是一些替代方案：
- Gurobi
- Mosek
- CPlex
并行执行
实现并行处理的弱方法是并行运行流程。 你能一次推出几个最佳选择吗？ （在这里，你可以在Hadoop中编写一个简单的exectuion任务）
改善投入
如果你相应地提交数据，求解器可以表现得更好（稀疏矩阵......）
在您的代码中，您使用lpSolve::lp 。 根据lpSolver 5.6.7的CRAN页面及其PDF ，运输问题lpSolve:lp.transport有一种特殊模式。 （我没有和R一起工作，我不熟悉语法。我是一个Matlab“爱好者”。）
最新研究
也许在学术界花了一些时间。 您可能会在sciencedirect.com上找到一篇论文（花费一些钱）。
硬件
CPU越快，获得解决方案的速度就越快（答案）。 您介意给我们一些关于您的环境（操作系统，CPU）的见解吗？
最后的想法
您是否曾尝试在quant.stackexchange.com中提出您的问题，因为数值优化与数学密切相关并且“不涉及那么多编程”。
并行模式
我们使用CLP 。 根据一些同事的说法，您可以设置一个标志或使用不同的实现来并行运行求解器（在许多核心上执行1个问题）。 看看SYMPHONY吧。
让我们发布，您介意给我们一些关于您的环境（操作系统，CPU）的见解吗？
我的恩惠
在Quora上，您可以找到答案，Hadoop在数据处理方面表现出色，I / O速度快，不是专为优化或计算而设计的： Quora
如果您不想奖励我，请告诉我。
缺少+100的赏金
即使我做了一些研究，我还没有收到任何赏金......这个帖子到了你没有给我们必要细节的地步，我们应该为你解决它 - 这永远不会成功！
使用lpsolve / lpsolveapi在R中减少库存优化/减少浪费
[英]cutting stock optimization/waste minimize in r using lpsolve/lpsolveapi
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