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耗费两天一夜不眠不休搞定了pycharm调用gurobi，此时我百感交集、喜极而泣、语无伦次、手舞足蹈。为防止以后换电脑或者重装系统后忘了这套骚操作，遂决定写一篇文档把整个过程记录下来。
文档分为三部分：一.安装Anaconda/Miniconda和Pycharm及环境配置；二.调用Gurobi，三.疑难杂症
先显摆一下调用成功的证明。测试代码1：
from gurobipy import *
# 8部电影
# 7个影厅
# 8个时段
I = list(range(8)) # 时段
J = list(range(7)) # 影厅
K = list(range(8)) # 电影
seat_j = [118, 86, 116, 85, 156, 142, 156]
# 一行为一个影厅,一列为一部电影
price_jk = [[60, 60, 65, 60, 65, 90, 60, 65],
[65, 65, 85, 75, 60, 75, 85, 80],
[60, 70, 75, 80, 75, 80, 80, 75],
[65, 65, 80, 75, 80, 75, 75, 80],
[60, 65, 65, 60, 75, 80, 80, 75],
[60, 65, 65, 80, 75, 75, 80, 75],
[60, 60, 75, 80, 75, 70, 60, 75]]
# 一行为一个时段,一列为一部电影
rate_ik = [[0.50, 0.55, 0.45, 0.50, 0.60, 0.46, 0.55, 0.45],
[0.42, 0.43, 0.41, 0.43, 0.45, 0.30, 0.53, 0.36],
[0.58, 0.63, 0.67, 0.64, 0.70, 0.64, 0.54, 0.57],
[0.62, 0.67, 0.70, 0.65, 0.75, 0.64, 0.53, 0.66],
[0.65, 0.65, 0.73, 0.68, 0.75, 0.74, 0.67, 0.72],
[0.66, 0.69, 0.78, 0.78, 0.78, 0.75, 0.74, 0.70],
[0.67, 0.92, 0.87, 0.87, 0.75, 0.59, 0.68, 0.68],
[0.67, 0.92, 0.87, 0.87, 0.75, 0.59, 0.68, 0.68]]
# 计算满座的票房二维列表,lt_all
all_jk = [[0 for col in K] for row in J]
for j in J:
for k in K:
all_jk[j][k] = price_jk[j][k] * seat_j[j]
# 创建模型
m = Model("ass_mov")
# 创建变量.第i个时段在第j个影厅放映第k部电影
x = m.addVars(I, J, K, vtype=GRB.BINARY)
# 更新变量环境
m.update()
# 创建目标函数
m.setObjective(sum(x[i, j, k] * rate_ik[i][k] * all_jk[j][k]
for i in I for j in J for k in K),
GRB.MAXIMIZE)
# 创建约束条件约束条件
# 每部电影至少放映一次
m.addConstrs(sum(x[i,j,k] for i in I for j in J) >= 1 for k in K)
# 每个时段每个影厅只能放映一部电影
m.addConstrs(sum(x[i,j,k] for k in K) == 1 for i in I for j in J)
# 求解规划模型
m.optimize()
# 输出结果
result = [[0 for col in J] for row in I]
solution = m.getAttr('x',x)
# 得到排片矩阵
for k,v in solution.items():
if v == 1:
result[k[0]][k[1]] = k[2] + 1
# 得到最大收益值
max_get = sum(
x[i, j, k].x * rate_ik[i][k] * all_jk[j][k]
for i in I for j in J for k in K
)
# 打印最大收益值,和排片矩阵
print('最大收益为:',max_get)
print('最佳排片方法:')
print('\n影厅j|', J)
print('-'*28)
for idx,l in enumerate(result) :
print(f'时段{idx}|',l)测试代码1运行结果：
C:\Users\xzr\.conda\envs\py310gurobi\python.exe F:\PycharmProjects\workspace\untitled\jizulunban\question1.py
Gurobi Optimizer version 10.0.0 build v10.0.0rc2 (win64)
CPU model: Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz, instruction set [SSE2|AVX|AVX2]
Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
Optimize a model with 64 rows, 448 columns and 896 nonzeros
Model fingerprint: 0xae490d04
Variable types: 0 continuous, 448 integer (448 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+03, 1e+04]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 1e+00]
Found heuristic solution: objective 325324.25000
Presolve time: 0.00s
Presolved: 64 rows, 448 columns, 896 nonzeros
Variable types: 0 continuous, 448 integer (448 binary)
Found heuristic solution: objective 381565.75000
Root relaxation: objective 3.865421e+05, 65 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 386542.15000 386542.150 0.00% - 0s
Explored 1 nodes (65 simplex iterations) in 0.01 seconds (0.00 work units)
Thread count was 4 (of 4 available processors)
耗费两天一夜不眠不休搞定了pycharm调用gurobi，此时我百感交集、喜极而泣、语无伦次、手舞足蹈。为防止以后换电脑或者重装系统后忘了这套骚操作，遂决定写一篇文档把整个过程记录下来。
文档分为三部分：一.安装Anaconda/Miniconda和Pycharm及环境配置；二.调用Gurobi，三.疑难杂症
先显摆一下调用成功的证明。测试代码1：
from gurobipy import *
# 8部电影
# 7个影厅
# 8个时段
I = list(range(8)) # 时段
J = list(range(7)) # 影厅
K = list(range(8)) # 电影
seat_j = [118, 86, 116, 85, 156, 142, 156]
# 一行为一个影厅,一列为一部电影
price_jk = [[60, 60, 65, 60, 65, 90, 60, 65],
[65, 65, 85, 75, 60, 75, 85, 80],
[60, 70, 75, 80, 75, 80, 80, 75],
[65, 65, 80, 75, 80, 75, 75, 80],
[60, 65, 65, 60, 75, 80, 80, 75],
[60, 65, 65, 80, 75, 75, 80, 75],
[60, 60, 75, 80, 75, 70, 60, 75]]
# 一行为一个时段,一列为一部电影
rate_ik = [[0.50, 0.55, 0.45, 0.50, 0.60, 0.46, 0.55, 0.45],
[0.42, 0.43, 0.41, 0.43, 0.45, 0.30, 0.53, 0.36],
[0.58, 0.63, 0.67, 0.64, 0.70, 0.64, 0.54, 0.57],
[0.62, 0.67, 0.70, 0.65, 0.75, 0.64, 0.53, 0.66],
[0.65, 0.65, 0.73, 0.68, 0.75, 0.74, 0.67, 0.72],
[0.66, 0.69, 0.78, 0.78, 0.78, 0.75, 0.74, 0.70],
[0.67, 0.92, 0.87, 0.87, 0.75, 0.59, 0.68, 0.68],
[0.67, 0.92, 0.87, 0.87, 0.75, 0.59, 0.68, 0.68]]
# 计算满座的票房二维列表,lt_all
all_jk = [[0 for col in K] for row in J]
for j in J:
for k in K:
all_jk[j][k] = price_jk[j][k] * seat_j[j]
# 创建模型
m = Model("ass_mov")
# 创建变量.第i个时段在第j个影厅放映第k部电影
x = m.addVars(I, J, K, vtype=GRB.BINARY)
# 更新变量环境
m.update()
# 创建目标函数
m.setObjective(sum(x[i, j, k] * rate_ik[i][k] * all_jk[j][k]
for i in I for j in J for k in K),
GRB.MAXIMIZE)
# 创建约束条件约束条件
# 每部电影至少放映一次
m.addConstrs(sum(x[i,j,k] for i in I for j in J) >= 1 for k in K)
# 每个时段每个影厅只能放映一部电影
m.addConstrs(sum(x[i,j,k] for k in K) == 1 for i in I for j in J)
# 求解规划模型
m.optimize()
# 输出结果
result = [[0 for col in J] for row in I]
solution = m.getAttr('x',x)
# 得到排片矩阵
for k,v in solution.items():
if v == 1:
result[k[0]][k[1]] = k[2] + 1
# 得到最大收益值
max_get = sum(
x[i, j, k].x * rate_ik[i][k] * all_jk[j][k]
for i in I for j in J for k in K
)
# 打印最大收益值,和排片矩阵
print('最大收益为:',max_get)
print('最佳排片方法:')
print('\n影厅j|', J)
print('-'*28)
for idx,l in enumerate(result) :
print(f'时段{idx}|',l)测试代码1运行结果：
C:\Users\xzr\.conda\envs\py310gurobi\python.exe F:\PycharmProjects\workspace\untitled\jizulunban\question1.py
Gurobi Optimizer version 10.0.0 build v10.0.0rc2 (win64)
CPU model: Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz, instruction set [SSE2|AVX|AVX2]
Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
Optimize a model with 64 rows, 448 columns and 896 nonzeros
Model fingerprint: 0xae490d04
Variable types: 0 continuous, 448 integer (448 binary)
Coefficient statistics:
Matrix range [1e+00, 1e+00]
Objective range [2e+03, 1e+04]
Bounds range [1e+00, 1e+00]
RHS range [1e+00, 1e+00]
Found heuristic solution: objective 325324.25000
Presolve time: 0.00s
Presolved: 64 rows, 448 columns, 896 nonzeros
Variable types: 0 continuous, 448 integer (448 binary)
Found heuristic solution: objective 381565.75000
Root relaxation: objective 3.865421e+05, 65 iterations, 0.00 seconds (0.00 work units)
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
* 0 0 0 386542.15000 386542.150 0.00% - 0s
Explored 1 nodes (65 simplex iterations) in 0.01 seconds (0.00 work units)
Thread count was 4 (of 4 available processors)
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