感谢crx 91，这是一个线性规划问题--〉整数规划--〉0-1整数规划--〉背包问题。
纸浆包是有用的，下面是我写的一个python代码，只针对这个简单的question.（我在考虑更复杂的条件）

from pulp import *
    prob = LpProblem("re-assign", LpMinimize)
    weight=[0.25,0.32,0.4,0.4,0.4,0.4,0.4,0.4,0.45,0.46]
    cost=[120.2,148.2,120.2,120.2,125.9,120.2,120.2,120.2,120.2,120.2]

    status_metro=[LpVariable("x{}".format(i+1), cat="Binary") for i in range(len(weight))]

    prob +=pulp.lpSum([(i*cost1) for i ,cost1 in zip(status_metro,cost)])
    prob +=pulp.lpSum([(i*weight1) for i, weight1 in zip(status_metro,weight)])<=3.6
    prob +=pulp.lpSum([(i*weight1) for i , weight1 in zip(status_metro,weight)])>=3

    prob.solve()
    for v in prob.variables():
        print(v.name,"=",v.varValue)
    print("sum",pulp.value(prob.objective))