Python GUROBI-如何使用50个效率最低的提供商运行优化
我正在使用Python和Gurobi解算器。我正在解决一个混合整数优化问题,我正试图得到50个效率最低的供应商的优化结果。但我得到了47个供应商的结果,而不是预期的50个。 如何运行此优化以实现相同的目标 我试图优化至少50个低效率提供者的代码片段Python GUROBI-如何使用50个效率最低的提供商运行优化,python,mathematical-optimization,gurobi,Python,Mathematical Optimization,Gurobi,我正在使用Python和Gurobi解算器。我正在解决一个混合整数优化问题,我正试图得到50个效率最低的供应商的优化结果。但我得到了47个供应商的结果,而不是预期的50个。 如何运行此优化以实现相同的目标 我试图优化至少50个低效率提供者的代码片段 model.addConstr(quicksum(df_Eff_Spend_p['Tier_Status']), GRB.GREATER_EQUAL, 50) model.update() Efficiency = df_Eff_Spen
model.addConstr(quicksum(df_Eff_Spend_p['Tier_Status']), GRB.GREATER_EQUAL, 50)
model.update()
Efficiency = df_Eff_Spend_p['Efficiency Score'] * df_Eff_Spend_p['Tier_Status']
model.setObjective( Efficiency.sum(), GRB.MINIMIZE)
model.optimize()
Gurobi解决方案的输出如下所示-
Optimize a model with 1 rows, 1121 columns and 1112 nonzeros
Variable types: 0 continuous, 1121 integer (1121 binary)
Coefficient statistics:
Matrix range [1e+00, 2e+02]
Objective range [1e-01, 5e+02]
Bounds range [1e+00, 1e+00]
RHS range [5e+01, 5e+01]
Found heuristic solution: objective 128.2053191
Presolve removed 0 rows and 160 columns
Presolve time: 0.01s
Presolved: 1 rows, 961 columns, 961 nonzeros
Variable types: 0 continuous, 961 integer (819 binary)
Root relaxation: objective 1.978018e+01, 1 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 19.78018 0 1 128.20532 19.78018 84.6% - 0s
H 0 0 19.7952723 19.78018 0.08% - 0s
H 0 0 19.7823530 19.78018 0.01% - 0s
0 0 infeasible 0 19.78235 19.78235 0.00% - 0s
Explored 1 nodes (1 simplex iterations) in 0.06 seconds
Thread count was 4 (of 4 available processors)
Solution count 3: 19.7824 19.7953 128.205
Optimal solution found (tolerance 1.00e-04)
Best objective 1.978235301932e+01, best bound 1.978235301932e+01, gap 0.0000%
---**--- Model Details ---**---
Number of Variables : 1121
Number of Constraints : 1
---**--- Optimum network ---**---
Total Efficiency : 19.782
Runtime : 0
用于检查网络中有多少用户的代码-
count_network = 0
count_all = 0
for v in model.getVars ():
count_network = count_network + v.x
count_all = count_all + 1
print("The total number of selected providers in the network= ", count_network )
print("The total number of providers in the network= ", count_all )
网络中所选提供商的总数=47.0
网络中的提供商总数=1121您如何准确地初始化df_Eff_Spend_p['Tier_Status']?此数据结构是否包含所有1121个变量?如何准确初始化df_Eff_Spend_p[“Tier_Status”]?此数据结构是否包含所有1121变量?