1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
| import gurobipy
import pandas as pd
# 分页显示数据, 设置为 False 不允许分页
pd.set_option('display.expand_frame_repr', False)
# 最多显示的列数, 设置为 None 显示全部列
pd.set_option('display.max_columns', None)
# 最多显示的行数, 设置为 None 显示全部行
pd.set_option('display.max_rows', None)
class DEA(object):
def __init__(self, DMUs_Name, X, Y, AP=False):
self.m1, self.m1_name, self.m2, self.m2_name, self.AP = X.shape[1], X.columns.tolist(), Y.shape[1], Y.columns.tolist(), AP
self.DMUs, self.X, self.Y = gurobipy.multidict({DMU: [X.loc[DMU].tolist(), Y.loc[DMU].tolist()] for DMU in DMUs_Name})
print(f'DEA(AP={AP}) MODEL RUNING...')
def __CCR(self):
for k in self.DMUs:
MODEL = gurobipy.Model()
OE, lambdas, s_negitive, s_positive = MODEL.addVar(), MODEL.addVars(self.DMUs), MODEL.addVars(self.m1), MODEL.addVars(self.m2)
MODEL.update()
MODEL.setObjectiveN(OE, index=0, priority=1)
MODEL.setObjectiveN(-(sum(s_negitive) + sum(s_positive)), index=1, priority=0)
MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.X[i][j] for i in self.DMUs if i != k or not self.AP) + s_negitive[j] == OE * self.X[k][j] for j in range(self.m1))
MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.Y[i][j] for i in self.DMUs if i != k or not self.AP) - s_positive[j] == self.Y[k][j] for j in range(self.m2))
MODEL.setParam('OutputFlag', 0)
MODEL.optimize()
self.Result.at[k, ('效益分析', '综合技术效益(CCR)')] = MODEL.objVal
self.Result.at[k, ('规模报酬分析', '有效性')] = '非 DEA 有效' if MODEL.objVal < 1 else 'DEA 弱有效' if s_negitive.sum().getValue() + s_positive.sum().getValue() else 'DEA 强有效'
self.Result.at[k, ('规模报酬分析', '类型')] = '规模报酬固定' if lambdas.sum().getValue() == 1 else '规模报酬递增' if lambdas.sum().getValue() < 1 else '规模报酬递减'
for m in range(self.m1):
self.Result.at[k, ('差额变数分析', f'{self.m1_name[m]}')] = s_negitive[m].X
self.Result.at[k, ('投入冗余率', f'{self.m1_name[m]}')] = 'N/A' if self.X[k][m] == 0 else s_negitive[m].X / self.X[k][m]
for m in range(self.m2):
self.Result.at[k, ('差额变数分析', f'{self.m2_name[m]}')] = s_positive[m].X
self.Result.at[k, ('产出不足率', f'{self.m2_name[m]}')] = 'N/A' if self.Y[k][m] == 0 else s_positive[m].X / self.Y[k][m]
return self.Result
def __BCC(self):
for k in self.DMUs:
MODEL = gurobipy.Model()
TE, lambdas = MODEL.addVar(), MODEL.addVars(self.DMUs)
MODEL.update()
MODEL.setObjective(TE, sense=gurobipy.GRB.MINIMIZE)
MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.X[i][j] for i in self.DMUs if i != k or not self.AP) <= TE * self.X[k][j] for j in range(self.m1))
MODEL.addConstrs(gurobipy.quicksum(lambdas[i] * self.Y[i][j] for i in self.DMUs if i != k or not self.AP) >= self.Y[k][j] for j in range(self.m2))
MODEL.addConstr(gurobipy.quicksum(lambdas[i] for i in self.DMUs if i != k or not self.AP) == 1)
MODEL.setParam('OutputFlag', 0)
MODEL.optimize()
self.Result.at[k, ('效益分析', '技术效益(BCC)')] = MODEL.objVal if MODEL.status == gurobipy.GRB.Status.OPTIMAL else 'N/A'
return self.Result
def dea(self):
columns_Page = ['效益分析'] * 3 + ['规模报酬分析'] * 2 + ['差额变数分析'] * (self.m1 + self.m2) + ['投入冗余率'] * self.m1 + ['产出不足率'] * self.m2
columns_Group = ['技术效益(BCC)', '规模效益(CCR/BCC)', '综合技术效益(CCR)','有效性', '类型'] + (self.m1_name + self.m2_name) * 2
self.Result = pd.DataFrame(index=self.DMUs, columns=[columns_Page, columns_Group])
self.__CCR()
self.__BCC()
self.Result.loc[:, ('效益分析', '规模效益(CCR/BCC)')] = self.Result.loc[:, ('效益分析', '综合技术效益(CCR)')] / self.Result.loc[:,('效益分析', '技术效益(BCC)')]
return self.Result
def analysis(self, file_name=None):
Result = self.dea()
file_name = 'DEA 数据包络分析报告.xlsx' if file_name is None else f'\\{file_name}.xlsx'
Result.to_excel(file_name, 'DEA 数据包络分析报告')
|
