Efficiency Analysis¶
Reference¶
SAS/OR example: https://go.documentation.sas.com/?docsetId=ormpex&docsetTarget=ormpex_ex22_toc.htm&docsetVersion=15.1&locale=en
SAS/OR code for example: http://support.sas.com/documentation/onlinedoc/or/ex_code/151/mpex22.html
Model¶
import sasoptpy as so
import pandas as pd
from sasoptpy.util import iterate, concat
from sasoptpy.actions import (
read_data, create_data, cofor_loop, for_loop, solve, if_condition, diff,
print_item, inline_condition)
def test(cas_conn, get_tables=False):
input_list = pd.DataFrame(
['staff', 'showroom', 'pop1', 'pop2', 'alpha_enq', 'beta_enq'],
columns=['input'])
input_data = cas_conn.upload_frame(
data=input_list, casout={'name': 'input_data', 'replace': True})
output_list = pd.DataFrame(
['alpha_sales', 'beta_sales', 'profit'], columns=['output'])
output_data = cas_conn.upload_frame(
data=output_list, casout={'name': 'output_data', 'replace': True})
problem_data = pd.DataFrame([
['Winchester', 7, 8, 10, 12, 8.5, 4, 2, 0.6, 1.5],
['Andover', 6, 6, 20, 30, 9, 4.5, 2.3, 0.7, 1.6],
['Basingstoke', 2, 3, 40, 40, 2, 1.5, 0.8, 0.25, 0.5],
['Poole', 14, 9, 20, 25, 10, 6, 2.6, 0.86, 1.9],
['Woking', 10, 9, 10, 10, 11, 5, 2.4, 1, 2],
['Newbury', 24, 15, 15, 13, 25, 19, 8, 2.6, 4.5],
['Portsmouth', 6, 7, 50, 40, 8.5, 3, 2.5, 0.9, 1.6],
['Alresford', 8, 7.5, 5, 8, 9, 4, 2.1, 0.85, 2],
['Salisbury', 5, 5, 10, 10, 5, 2.5, 2, 0.65, 0.9],
['Guildford', 8, 10, 30, 35, 9.5, 4.5, 2.05, 0.75, 1.7],
['Alton', 7, 8, 7, 8, 3, 2, 1.9, 0.7, 0.5],
['Weybridge', 5, 6.5, 9, 12, 8, 4.5, 1.8, 0.63, 1.4],
['Dorchester', 6, 7.5, 10, 10, 7.5, 4, 1.5, 0.45, 1.45],
['Bridport', 11, 8, 8, 10, 10, 6, 2.2, 0.65, 2.2],
['Weymouth', 4, 5, 10, 10, 7.5, 3.5, 1.8, 0.62, 1.6],
['Portland', 3, 3.5, 3, 2, 2, 1.5, 0.9, 0.35, 0.5],
['Chichester', 5, 5.5, 8, 10, 7, 3.5, 1.2, 0.45, 1.3],
['Petersfield', 21, 12, 6, 8, 15, 8, 6, 0.25, 2.9],
['Petworth', 6, 5.5, 2, 2, 8, 5, 1.5, 0.55, 1.55],
['Midhurst', 3, 3.6, 3, 3, 2.5, 1.5, 0.8, 0.2, 0.45],
['Reading', 30, 29, 120, 80, 35, 20, 7, 2.5, 8],
['Southampton', 25, 16, 110, 80, 27, 12, 6.5, 3.5, 5.4],
['Bournemouth', 19, 10, 90, 12, 25, 13, 5.5, 3.1, 4.5],
['Henley', 7, 6, 5, 7, 8.5, 4.5, 1.2, 0.48, 2],
['Maidenhead', 12, 8, 7, 10, 12, 7, 4.5, 2, 2.3],
['Fareham', 4, 6, 1, 1, 7.5, 3.5, 1.1, 0.48, 1.7],
['Romsey', 2, 2.5, 1, 1, 2.5, 1, 0.4, 0.1, 0.55],
['Ringwood', 2, 3.5, 2, 2, 1.9, 1.2, 0.3, 0.09, 0.4],
], columns=['garage_name', 'staff', 'showroom', 'pop1', 'pop2', 'alpha_enq',
'beta_enq', 'alpha_sales', 'beta_sales', 'profit'])
garage_data = cas_conn.upload_frame(
data=problem_data, casout={'name': 'garage_data', 'replace': True})
with so.Workspace(name='efficiency_analysis', session=cas_conn) as w:
inputs = so.Set(name='INPUTS', settype=so.string)
read_data(table=input_data, index={'target': inputs, 'key': 'input'})
outputs = so.Set(name='OUTPUTS', settype=so.string)
read_data(table=output_data, index={'target': outputs, 'key': 'output'})
garages = so.Set(name='GARAGES', settype=so.number)
garage_name = so.ParameterGroup(garages, name='garage_name', ptype=so.string)
input = so.ParameterGroup(inputs, garages, name='input')
output = so.ParameterGroup(outputs, garages, name='output')
r = read_data(table=garage_data, index={'target': garages, 'key': so.N},
columns=[garage_name])
with iterate(inputs, 'i') as i:
r.append({'index': i, 'target': input[i, so.N], 'column': i})
with iterate(outputs, 'i') as i:
r.append({'index': i, 'target': output[i, so.N], 'column': i})
k = so.Parameter(name='k', ptype=so.number)
efficiency_number = so.ParameterGroup(garages, name='efficiency_number')
weight_sol = so.ParameterGroup(garages, garages, name='weight_sol')
weight = so.VariableGroup(garages, name='Weight', lb=0)
inefficiency = so.Variable(name='Inefficiency', lb=0)
obj = so.Objective(inefficiency, name='Objective', sense=so.maximize)
input_con = so.ConstraintGroup(
(so.expr_sum(input[i, j] * weight[j] for j in garages) <= input[i, k]
for i in inputs), name='input_con')
output_con = so.ConstraintGroup(
(so.expr_sum(output[i, j] * weight[j] for j in garages) >= output[i, k] * inefficiency
for i in outputs), name='output_con')
for kk in cofor_loop(garages):
k.set_value(kk)
solve()
efficiency_number[k] = 1 / inefficiency.sol
for j in for_loop(garages):
def if_block():
weight_sol[k, j] = weight[j].sol
def else_block():
weight_sol[k, j] = None
if_condition(weight[j].sol > 1e-6, if_block, else_block)
efficient_garages = so.Set(
name='EFFICIENT_GARAGES',
value=[j.sym for j in garages if j.sym.under_condition(efficiency_number[j] >= 1)])
inefficient_garages = so.Set(value=diff(garages, efficient_garages), name='INEFFICIENT_GARAGES')
p1 = print_item(garage_name, efficiency_number)
ed = create_data(table='efficiency_data', index={'key': ['garage']}, columns=[
garage_name, efficiency_number
])
with iterate(inefficient_garages, 'inefficient_garage') as i:
wd = create_data(table='weight_data_dense',
index={'key': [i], 'set': [i.get_set()]},
columns=[garage_name, efficiency_number])
with iterate(efficient_garages, 'efficient_garage') as j:
wd.append({
'name': concat('w', j),
'expression': weight_sol[i, j],
'index': j
})
filtered_set = so.InlineSet(
lambda: ((g1, g2)
for g1 in inefficient_garages
for g2 in efficient_garages
if inline_condition(weight_sol[g1, g2] != None)))
wds = create_data(table='weight_data_sparse',
index={'key': ['i', 'j'], 'set': [filtered_set]},
columns=[weight_sol])
print(w.to_optmodel())
w.submit()
print('Print Table:')
print(p1.get_response())
print('Efficiency Data:')
print(ed.get_response())
print('Weight Data (Dense):')
print(wd.get_response())
print('Weight Data (Sparse):')
print(wds.get_response())
if get_tables:
return obj.get_value(), ed.get_response()
else:
return obj.get_value()
Output¶
In [1]: import os
In [2]: hostname = os.getenv('CASHOST')
In [3]: port = os.getenv('CASPORT')
In [4]: from swat import CAS
In [5]: cas_conn = CAS(hostname, port)
In [6]: import sasoptpy
In [7]: from examples.server_side.efficiency_analysis import test
In [8]: test(cas_conn)
NOTE: Cloud Analytic Services made the uploaded file available as table INPUT_DATA in caslib CASUSER(casuser).
NOTE: The table INPUT_DATA has been created in caslib CASUSER(casuser) from binary data uploaded to Cloud Analytic Services.
NOTE: Cloud Analytic Services made the uploaded file available as table OUTPUT_DATA in caslib CASUSER(casuser).
NOTE: The table OUTPUT_DATA has been created in caslib CASUSER(casuser) from binary data uploaded to Cloud Analytic Services.
NOTE: Cloud Analytic Services made the uploaded file available as table GARAGE_DATA in caslib CASUSER(casuser).
NOTE: The table GARAGE_DATA has been created in caslib CASUSER(casuser) from binary data uploaded to Cloud Analytic Services.
proc optmodel;
set <str> INPUTS;
read data INPUT_DATA into INPUTS=[input] ;
set <str> OUTPUTS;
read data OUTPUT_DATA into OUTPUTS=[output] ;
set GARAGES;
str garage_name {GARAGES};
num input {INPUTS, GARAGES};
num output {OUTPUTS, GARAGES};
read data GARAGE_DATA into GARAGES=[_N_] garage_name {i in INPUTS} < input[i, _N_]=col(i) > {i in OUTPUTS} < output[i, _N_]=col(i) >;
num k;
num efficiency_number {GARAGES};
num weight_sol {GARAGES, GARAGES};
var Weight {{GARAGES}} >= 0;
var Inefficiency >= 0;
max Objective = Inefficiency;
con input_con {o21 in INPUTS} : input[o21, k] - (sum {j in GARAGES} (input[o21, j] * Weight[j])) >= 0;
con output_con {o33 in OUTPUTS} : sum {j in GARAGES} (output[o33, j] * Weight[j]) - output[o33, k] * Inefficiency >= 0;
cofor {o46 in GARAGES} do;
k = o46;
solve;
efficiency_number[k] = (1) / (Inefficiency.sol);
for {o58 in GARAGES} do;
if Weight[o58].sol > 1e-06 then do;
weight_sol[k, o58] = Weight[o58].sol;
end;
else do;
weight_sol[k, o58] = .;
end;
end;
end;
set EFFICIENT_GARAGES = {{o69 in GARAGES: efficiency_number[o69] >= 1}};
set INEFFICIENT_GARAGES = GARAGES diff EFFICIENT_GARAGES;
print garage_name efficiency_number;
create data efficiency_data from [garage] garage_name efficiency_number;
create data weight_data_dense from [inefficient_garage] = {{INEFFICIENT_GARAGES}} garage_name efficiency_number {efficient_garage in EFFICIENT_GARAGES} < col('w' || efficient_garage)=(weight_sol[inefficient_garage, efficient_garage]) >;
create data weight_data_sparse from [i j] = {{{o83 in INEFFICIENT_GARAGES, o85 in EFFICIENT_GARAGES: weight_sol[o83, o85] ne .}}} weight_sol;
quit;
NOTE: Added action set 'optimization'.
NOTE: There were 6 rows read from table 'INPUT_DATA' in caslib 'CASUSER(casuser)'.
NOTE: There were 3 rows read from table 'OUTPUT_DATA' in caslib 'CASUSER(casuser)'.
NOTE: There were 28 rows read from table 'GARAGE_DATA' in caslib 'CASUSER(casuser)'.
NOTE: The COFOR statement is executing in single-machine mode.
NOTE: Problem generation will use 8 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.01 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 2.788571E+01 0
P 2 6 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.02 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.185408E+01 0
P 2 6 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.02 seconds.
NOTE: Problem generation will use 6 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.669507E+01 0
P 2 15 1.152977E+00 0
NOTE: Optimal.
NOTE: Objective = 1.1529771581.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 5 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.771196E+01 0
P 2 5 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.00 seconds.
NOTE: Problem generation will use 8 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 9.367804E+01 0
P 2 18 1.191606E+00 0
NOTE: Optimal.
NOTE: Objective = 1.1916056975.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.282477E+01 0
P 2 7 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.02 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 2.113425E+02 0
P 2 7 1.141723E+00 0
NOTE: Optimal.
NOTE: Objective = 1.141723356.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 8.984164E+01 0
P 2 20 1.190229E+00 0
NOTE: Optimal.
NOTE: Objective = 1.1902294108.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 7.496160E+01 0
P 2 7 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.137365E+01 0
P 2 9 1.011903E+00 0
NOTE: Optimal.
NOTE: Objective = 1.0119030842.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 5.430205E+01 0
P 2 18 1.018276E+00 0
NOTE: Optimal.
NOTE: Objective = 1.0182756046.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 9.121193E+01 0
P 2 17 1.170487E+00 0
NOTE: Optimal.
NOTE: Objective = 1.170487106.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 7.130206E+01 0
P 2 12 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 7.942787E+01 0
P 2 15 1.090062E+00 0
NOTE: Optimal.
NOTE: Objective = 1.0900621118.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 3.333800E+01 0
P 2 8 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 3.269636E+01 0
P 2 5 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.00 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 1.483009E+02 0
P 2 12 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 7.522880E+01 0
P 2 9 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 8.552410E+01 0
P 2 14 1.160239E+00 0
NOTE: Optimal.
NOTE: Objective = 1.1602389558.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 6.743350E+01 0
P 2 20 1.029858E+00 0
NOTE: Optimal.
NOTE: Objective = 1.0298577511.
NOTE: The Dual Simplex solve time is 0.00 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 1.119904E+02 0
P 2 6 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 3.162476E+01 0
P 2 5 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 1.462372E+02 0
P 2 9 1.205868E+00 0
NOTE: Optimal.
NOTE: Objective = 1.2058683067.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 9.913941E+01 0
P 2 16 1.228251E+00 0
NOTE: Optimal.
NOTE: Objective = 1.2282514234.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 1.428026E+02 0
P 2 5 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 9.575369E+01 0
P 2 16 1.213087E+00 0
NOTE: Optimal.
NOTE: Objective = 1.2130872456.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 3.928295E+01 0
P 2 7 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: Problem generation will use 7 threads.
NOTE: The problem has 29 variables (0 free, 0 fixed).
NOTE: The problem has 9 linear constraints (0 LE, 0 EQ, 9 GE, 0 range).
NOTE: The problem has 255 linear constraint coefficients.
NOTE: The problem has 0 nonlinear constraints (0 LE, 0 EQ, 0 GE, 0 range).
NOTE: The OPTMODEL presolver is disabled for linear problems.
NOTE: The LP presolver value AUTOMATIC is applied.
NOTE: The LP presolver time is 0.00 seconds.
NOTE: The LP presolver removed 0 variables and 0 constraints.
NOTE: The LP presolver removed 0 constraint coefficients.
NOTE: The presolved problem has 29 variables, 9 constraints, and 255 constraint coefficients.
NOTE: The LP solver is called.
NOTE: The Dual Simplex algorithm is used.
Objective
Phase Iteration Value Time
D 2 1 4.906079E+01 0
P 2 5 1.000000E+00 0
NOTE: Optimal.
NOTE: Objective = 1.
NOTE: The Dual Simplex solve time is 0.01 seconds.
NOTE: The output table 'EFFICIENCY_DATA' in caslib 'CASUSER(casuser)' has 28 rows and 3 columns.
NOTE: The output table 'WEIGHT_DATA_DENSE' in caslib 'CASUSER(casuser)' has 17 rows and 14 columns.
NOTE: The output table 'WEIGHT_DATA_SPARSE' in caslib 'CASUSER(casuser)' has 43 rows and 3 columns.
NOTE: The output table 'SOLUTION' in caslib 'CASUSER(casuser)' has 29 rows and 6 columns.
NOTE: The output table 'DUAL' in caslib 'CASUSER(casuser)' has 9 rows and 4 columns.
Print Table:
COL1 garage_name efficiency_number
0 1.0 Bournemouth 1.000000
1 2.0 Henley 1.000000
2 3.0 Woking 0.867320
3 4.0 Alton 1.000000
4 5.0 Dorchester 0.839204
5 6.0 Alresford 1.000000
6 7.0 Ringwood 0.875869
7 8.0 Winchester 0.840174
8 9.0 Weymouth 1.000000
9 10.0 Petworth 0.988237
10 11.0 Reading 0.982052
11 12.0 Weybridge 0.854345
12 13.0 Portsmouth 1.000000
13 14.0 Andover 0.917379
14 15.0 Newbury 1.000000
15 16.0 Maidenhead 1.000000
16 17.0 Basingstoke 1.000000
17 18.0 Salisbury 1.000000
18 19.0 Poole 0.861891
19 20.0 Bridport 0.971008
20 21.0 Portland 1.000000
21 22.0 Petersfield 1.000000
22 23.0 Midhurst 0.829278
23 24.0 Guildford 0.814166
24 25.0 Romsey 1.000000
25 26.0 Chichester 0.824343
26 27.0 Southampton 1.000000
27 28.0 Fareham 1.000000
Efficiency Data:
Selected Rows from Table EFFICIENCY_DATA
garage garage_name efficiency_number
0 1.0 Bournemouth 1.000000
1 2.0 Henley 1.000000
2 3.0 Woking 0.867320
3 4.0 Alton 1.000000
4 5.0 Dorchester 0.839204
5 6.0 Alresford 1.000000
6 7.0 Ringwood 0.875869
7 8.0 Winchester 0.840174
8 9.0 Weymouth 1.000000
9 10.0 Petworth 0.988237
10 11.0 Reading 0.982052
11 12.0 Weybridge 0.854345
12 13.0 Portsmouth 1.000000
13 14.0 Andover 0.917379
14 15.0 Newbury 1.000000
15 16.0 Maidenhead 1.000000
16 17.0 Basingstoke 1.000000
17 18.0 Salisbury 1.000000
18 19.0 Poole 0.861891
19 20.0 Bridport 0.971008
20 21.0 Portland 1.000000
21 22.0 Petersfield 1.000000
22 23.0 Midhurst 0.829278
23 24.0 Guildford 0.814166
24 25.0 Romsey 1.000000
25 26.0 Chichester 0.824343
26 27.0 Southampton 1.000000
27 28.0 Fareham 1.000000
Weight Data (Dense):
Selected Rows from Table WEIGHT_DATA_DENSE
inefficient_garage garage_name efficiency_number ... w22 w25 w27
0 1.0 Bournemouth 1.000000 ... NaN NaN NaN
1 3.0 Woking 0.867320 ... NaN NaN 0.009093
2 5.0 Dorchester 0.839204 ... NaN NaN NaN
3 7.0 Ringwood 0.875869 ... NaN NaN NaN
4 8.0 Winchester 0.840174 ... NaN NaN NaN
5 10.0 Petworth 0.988237 ... 0.015212 NaN NaN
6 11.0 Reading 0.982052 ... NaN NaN NaN
7 12.0 Weybridge 0.854345 ... NaN NaN NaN
8 13.0 Portsmouth 1.000000 ... NaN NaN NaN
9 14.0 Andover 0.917379 ... NaN NaN NaN
10 17.0 Basingstoke 1.000000 ... NaN NaN NaN
11 19.0 Poole 0.861891 ... NaN NaN NaN
12 20.0 Bridport 0.971008 ... NaN NaN NaN
13 23.0 Midhurst 0.829278 ... 0.043482 NaN NaN
14 24.0 Guildford 0.814166 ... NaN NaN NaN
15 26.0 Chichester 0.824343 ... NaN NaN NaN
16 28.0 Fareham 1.000000 ... NaN NaN NaN
[17 rows x 14 columns]
Weight Data (Sparse):
Selected Rows from Table WEIGHT_DATA_SPARSE
i j weight_sol
0 5.0 2.0 0.035318
1 7.0 2.0 0.146485
2 11.0 2.0 2.862469
3 19.0 2.0 0.434419
4 20.0 2.0 0.783097
5 26.0 2.0 0.236367
6 3.0 4.0 0.021078
7 3.0 6.0 0.952525
8 5.0 6.0 0.104478
9 8.0 6.0 0.416268
10 24.0 6.0 0.622715
11 26.0 6.0 0.096820
12 5.0 9.0 0.119287
13 8.0 9.0 0.333333
14 11.0 9.0 0.544410
15 12.0 9.0 0.796562
16 14.0 9.0 0.857143
17 23.0 9.0 0.066511
18 24.0 9.0 0.191804
19 26.0 9.0 0.335428
20 10.0 15.0 0.066345
21 3.0 16.0 0.148376
22 10.0 16.0 0.034089
23 11.0 16.0 0.137534
24 12.0 16.0 0.145236
25 14.0 16.0 0.214286
26 19.0 16.0 0.344634
27 20.0 16.0 0.194894
28 23.0 16.0 0.008940
29 8.0 18.0 0.403284
30 23.0 18.0 0.059574
31 5.0 21.0 0.751632
32 7.0 21.0 0.319728
33 8.0 21.0 0.096138
34 11.0 21.0 1.199139
35 19.0 21.0 0.757330
36 20.0 21.0 0.469693
37 23.0 21.0 0.471893
38 24.0 21.0 0.168067
39 26.0 21.0 0.165227
40 10.0 22.0 0.015212
41 23.0 22.0 0.043482
42 3.0 27.0 0.009093
Out[8]: 1.0000000000000002