model:
sets:
dmu/1..7/:lambda; !决策单元;
inw/1..4/:s1; !投入变量集;
outw/1..4/:s2; !产出变量集;
inv(inw, dmu):x; !投入数据;
outv(outw, dmu):y; !产出数据;
endsets
data:
n=?; !所评价的决策单元;
x= 349.3313 0.63114 5.59091 6.30942 3.84352 0.50472 37.86518 !AZ;
1620.7 1.2335 17.19304 25.30626 251.4798 44.21377 117.4841 !CA;
75.48889 0.06826 1.51314 6.75239 1.04937 0.08122 40.8765 !NM;
1169.069 1.27549 15.62031 9.99996 9.71338 80.82566 57.3492; !TX;
!消耗*价格;
y= 19110.63 34.52749 305.8581 14526.70504576 3843.5218771 2188.06567 89941.053949 !AZ;
80799.65 61.49606 857.1551 39765.719738 251479.771641 70912.239599 279059.7185637
4030.339 3.64439 80.78612 16038.926614 1049.366891 1043.89315 97093.81923952
65780.31 71.76835 878.9118 22339.449732 9713.3800554 121621.9387228 146673.1547010;
enddata
min=theta;
@for(inw(i):@sum(dmu(j)|j#ne#n:lambda(j)*x(i,j))+s1(i)=theta*x(i,n)); !输入约束;
@for(outw(i):@sum(dmu(j)|j#ne#n:lambda(j)*y(i,j))-s2(i)=y(i,n)); !输出约束;
a = @sum(dmu(j):lambda(j))/theta; !规模有效性;
end