import cvxpy as cpimport pandas as pd
# 读取Excel数据data = pd.read_excel("x.xlsx", header=None)data = data.values
# 定义变量c = data[lbk]:-1, :-1[rbk]d = data[lbk]-1, :-1[rbk]e = data[lbk]:-1, -1[rbk]
# 创建变量x = cp.Variable((8,15), boolean=True)
# 定义目标函数obj = cp.Minimize(cp.sum(cp.multiply(e, x)))
# 定义约束条件constrs = [lbk] cp.sum(x, axis=0) == d, cp.sum(x, axis=1) <= e[rbk]
# 构建并求解优化问题prob = cp.Problem(obj, constrs)prob.solve(solver='GLPK_MI')
# 输出最优值和最优解print("最优值为:", prob.value)print("最优解为:\n", x.value)
# 将最优解保存为Excel文件xd = pd.DataFrame(x.value)xd.to_excel("x.xlsx")
# 读取Excel数据data = pd.read_excel("x.xlsx", header=None)data = data.values
# 定义变量c = data[lbk]:-1, :-1[rbk]d = data[lbk]-1, :-1[rbk]e = data[lbk]:-1, -1[rbk]
# 创建变量x = cp.Variable((8,15), boolean=True)
# 定义目标函数obj = cp.Minimize(cp.sum(cp.multiply(e, x)))
# 定义约束条件constrs = [lbk] cp.sum(x, axis=0) == d, cp.sum(x, axis=1) <= e[rbk]
# 构建并求解优化问题prob = cp.Problem(obj, constrs)prob.solve(solver='GLPK_MI')
# 输出最优值和最优解print("最优值为:", prob.value)print("最优解为:\n", x.value)
# 将最优解保存为Excel文件xd = pd.DataFrame(x.value)xd.to_excel("x.xlsx")