def main(argc, argv):
ampl = AMPL(Environment(r'C:\Users\etjones\Desktop\AI OPS\AMPL\amplide.mswin64'))
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model and data files.
#modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
#ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
#ampl.readData(os.path.join(modelDirectory, 'diet/diet.dat'))
ampl.read(r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.mod')
ampl.readData(r'C:\Users\etjones\Desktop\AI OPS\AMPL\custom_models\transp.dat')
# Solve
print('\n' + "AMPL MODEL:" + '\n')
ampl.solve()
totalcost = ampl.getObjective('Total_Cost')
print("Minimum Cost:", totalcost.value())
print('\n' + "Optimal Flow:")
ampl.display('Trans')
print('\n' + "Compare pdf flow to the above table to confirm optmial flows")
except Exception as e:
print(e)
raise
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
# Create an AMPL instance
ampl = AMPL()
"""
# If the AMPL installation directory is not in the system search path:
from amplpy import Environment
ampl = AMPL(
Environment('full path to the AMPL installation directory'))
"""
ampl.eval("set CITIES; set LINKS within (CITIES cross CITIES);")
ampl.eval("param cost {LINKS} >= 0; param capacity {LINKS} >= 0;")
ampl.eval("data; set CITIES := PITT NE SE BOS EWR BWI ATL MCO;")
cost = [2.5, 3.5, 1.7, 0.7, 1.3, 1.3, 0.8, 0.2, 2.1]
capacity = [250, 250, 100, 100, 100, 100, 100, 100, 100]
links_from = ["PITT", "PITT", "NE", "NE", "NE", "SE", "SE", "SE", "SE"]
links_to = ["NE", "SE", "BOS", "EWR", "BWI", "EWR", "BWI", "ATL", "MCO"]
# Using amplpy.DataFrame
df = DataFrame(("LINKSFrom", "LINKSTo"), ("cost", "capacity"))
df.set_column("LINKSFrom", links_from)
df.set_column("LINKSTo", links_to)
df.set_column("cost", cost)
df.set_column("capacity", capacity)
print(df)
ampl.set_data(df, "LINKS")
ampl.display("LINKS")
# Using pandas.DataFrame (recommended)
df = pd.DataFrame(
list(zip(links_from, links_to, cost, capacity)),
columns=["LINKSFrom", "LINKSTo", "cost", "capacity"],
).set_index(["LINKSFrom", "LINKSTo"])
print(df)
ampl.eval("reset data LINKS;")
ampl.set_data(df, "LINKS")
ampl.display("LINKS")
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 11 17:20:23 2018
@author: donja
"""
from amplpy import AMPL, Environment
ampl = AMPL(Environment('D:\\amplide.mswin64\\amplide.mswin64'))
ampl.option['solver'] = 'cplexamp'
ampl.read('example.mod') #read the model
ampl.eval('objective z; solve;')
ampl.display('z', 'roth', 'traditional', 'surplus')
pstring = """
objective function = {z}
bi-weekly roth contribution = ${roth}
bi-weekly traditional contribution = ${traditional}
surplus (amount left after optimal allocation) = ${surplus}
total (without surplus) = ${total}
total (with surplus) = ${totalS}
"""
z = ampl.getValue('z')
roth = ampl.getValue('roth')
traditional = ampl.getValue('traditional')
surplus = ampl.getValue('surplus')
annual_roth_amt = roth * 12 * 2
annual_traditional_amt = traditional * 12 * 2
def prodalloc(RID, SID, shared_ns=None, f_out=None, f_log=None):
from amplpy import AMPL, DataFrame
if shared_ns == None:
(df_demand, wup_12mavg, ppp_sum12, df_scenario, sw_avail, df_penfunc,
df_relcost) = pull_data(RID, SID)
# =======================================
# instantiate AMPL class and set AMPL options
ampl = AMPL()
# set options
ampl.setOption('presolve', False)
ampl.setOption('solver', 'gurobi_ampl')
# ampl.setOption('solver', 'cbc')
ampl.setOption('gurobi_options',
'iisfind=1 iismethod=1 lpmethod=4 mipgap=1e-6 warmstart=1')
ampl.setOption('reset_initial_guesses', True)
ampl.setOption('solver_msg', True)
# real model from model file
d_cur = os.getcwd()
f_model = os.path.join(d_cur, 'model.amp')
ampl.read(f_model)
if shared_ns != None:
df_demand = shared_ns.df_demand.query(
f'RealizationID == {RID}').loc[:, ['wpda', 'dates', 'Demand']]
dates = df_demand.loc[:, ['dates']].values
df_demand.loc[:, 'dates'] = [
pd.to_datetime(d).strftime('%Y-%b') for d in dates
]
df_demand.set_index(keys=['wpda', 'dates'], inplace=True)
wpda = sorted(list(set([w for (w, m) in df_demand.index])))
monyr = df_demand.loc[('COT', ), ].index.values
nyears = int(len(monyr) / 12.0)
# lambda for determine number of days in a month
dates = dates[0:(12 * nyears)]
f_ndays_mo = lambda aday: (aday + dt.timedelta(days=32)).replace(day=1
) - aday
ndays_mo = map(f_ndays_mo, [pd.to_datetime(d[0]) for d in dates])
ndays_mo = [i.days for i in ndays_mo]
# add data to sets -- Demand
ampl.getParameter('nyears').value = nyears
ampl.getSet('monyr').setValues(monyr)
ampl.getSet('wpda').setValues(wpda)
ampl.getParameter('demand').setValues(DataFrame.fromPandas(df_demand))
ampl.getParameter('ndays_mo').setValues(ndays_mo)
# index to year number
yearno = [(i // 12) + 1 for i in range(len(dates))]
ampl.getParameter('yearno').setValues(np.asarray(yearno))
monthno = [pd.to_datetime(d[0]).month for d in dates]
ampl.getParameter('monthno').setValues(np.asarray(monthno))
# ---------------------------------------
# WUP and preferred ranges
if shared_ns != None:
wup_12mavg = shared_ns.wup_12mavg
ampl.getParameter('wup_12mavg').setValues(
DataFrame.fromPandas(wup_12mavg.loc[:, ['wup_12mavg']]))
ampl.getParameter('prod_range_lo').setValues(
DataFrame.fromPandas(wup_12mavg.loc[:, ['prod_range_lo']]))
ampl.getParameter('prod_range_hi').setValues(
DataFrame.fromPandas(wup_12mavg.loc[:, ['prod_range_hi']]))
# add ppp_sum12
if shared_ns != None:
ppp_sum12 = shared_ns.ppp_sum12
ppp_sum12.loc[:, 'monyr'] = [i for i in range(1, 12)] * 3
ppp_sum12.set_index(keys=['WF', 'monyr'], inplace=True)
ampl.getParameter('ppp_sum12').setValues(DataFrame.fromPandas(ppp_sum12))
# Relative cost of water per a million gallon
if shared_ns != None:
df_relcost = shared_ns.df_relcost
ampl.getParameter('relcost').setValues(DataFrame.fromPandas(df_relcost))
# ---------------------------------------
# Scenario's data
if shared_ns != None:
df_scenario = shared_ns.df_scenario.query(
f'ScenarioID=={SID}').loc[:, ['ParameterName', 'MonthNo', 'Value']]
AVAIL_PCTILE = df_scenario.query(f"ParameterName == 'AVAIL_PCTILE'")
AVAIL_PCTILE = AVAIL_PCTILE.loc[AVAIL_PCTILE.index, 'Value'].values[0]
RES_INIT = df_scenario.query(f"ParameterName == 'RES_INIT'")
RES_INIT = RES_INIT.loc[RES_INIT.index, 'Value'].values[0]
# Surface Water Availability Data by month repeated for nyears
ampl.getParameter('avail_pctile').value = AVAIL_PCTILE
if shared_ns != None:
sw_avail = shared_ns.sw_avail.query(
f'Percentile == {AVAIL_PCTILE}'
).loc[:, ['source', 'monthno', 'value']]
# sw_avail = temp.copy()
# if nyears > 1:
# for i in range(1, nyears):
# sw_avail = sw_avail.append(temp)
# srcs = sw_avail.loc[:, 'source'].unique()
# for j in srcs:
# sw_avail.loc[sw_avail['source']==j,'monthno'] = [i+1 for i in range(len(monyr))]
# sw_avail.set_index(keys=['source', 'monthno'], inplace=True)
# ampl.getParameter('ngw_avail').setValues(DataFrame.fromPandas(sw_avail))
sw_avail.set_index(keys=['source', 'monthno'], inplace=True)
ampl.getParameter('ngw_avail').setValues(DataFrame.fromPandas(sw_avail))
# ---------------------------------------
# Penalty functions for under utilization
if shared_ns != None:
df_penfunc = shared_ns.df_penfunc
ampl.getParameter('penfunc_x').setValues(
DataFrame.fromPandas(df_penfunc.loc[:, ['under_limit']]))
ampl.getParameter('penfunc_r').setValues(
DataFrame.fromPandas(df_penfunc.loc[:, ['penalty_rate']]))
'''
# =======================================
# Read fixed allocation from spreadsheet
# f_excel = os.path.join(
# d_cur, 'WY 2019 monthly delivery and supply for budget InitialDraft.xlsx')
sheet_names = ['WY 2019','WY 2020','WY 2021','WY 2022','WY 2023','WY 2024']
# ch_poc: Central Hills delivery (row 16)
# reg_lithia: Regional to Lithia (row 20)
# reg_cot: Regional to City of Tampa (row 26)
# reg_thic: THIC intertie purchase (row 37)
# crw_prod: Carrollwood WF production (row 40)
# eag_prod: Production for Eagle Well (row 41)
# row index is zero based, minus one header row = -2
row_offset = -2
ch_poc, reg_lithia, reg_cot, reg_thic, crw_prod, eag_prod = [], [], [], [], [], []
# These DV is fixed or receives values from other optimizer
bud_fix, ds_fix, swtp_fix=[], [], []
for i in range(nyears):
df_excel = pd.read_excel(f_excel, sheet_names[i], usecols='C:N', nrows=41)
ch_poc .extend(list(df_excel.loc[16 + row_offset, :].values))
reg_lithia.extend(list(df_excel.loc[20 + row_offset, :].values))
reg_cot .extend(list(df_excel.loc[26 + row_offset, :].values))
reg_thic .extend(list(df_excel.loc[37 + row_offset, :].values))
crw_prod .extend(list(df_excel.loc[40 + row_offset, :].values))
eag_prod .extend(list(df_excel.loc[41 + row_offset, :].values))
bud_fix .extend(list(df_excel.loc[22 + row_offset, :].values))
ds_fix .extend(list(df_excel.loc[36 + row_offset, :].values))
swtp_fix.extend(list(df_excel.loc[38 + row_offset, :].values))
'''
ch_poc = df_scenario[df_scenario.ParameterName == 'ch_poc'].Value
reg_lithia = df_scenario[df_scenario.ParameterName == 'reg_lithia'].Value
reg_cot = df_scenario[df_scenario.ParameterName == 'reg_cot'].Value
reg_thic = df_scenario[df_scenario.ParameterName == 'reg_thic'].Value
crw_prod = df_scenario[df_scenario.ParameterName == 'crw_prod'].Value
eag_prod = df_scenario[df_scenario.ParameterName == 'eag_prod'].Value
ampl.getParameter('ch_poc').setValues(np.asarray(ch_poc, dtype=np.float32))
ampl.getParameter('reg_lithia').setValues(
np.asarray(reg_lithia, dtype=np.float32))
ampl.getParameter('reg_cot').setValues(
np.asarray(reg_cot, dtype=np.float32))
ampl.getParameter('reg_thic').setValues(
np.asarray(reg_thic, dtype=np.float32))
ampl.getParameter('crw_prod').setValues(
np.asarray(crw_prod, dtype=np.float32))
ampl.getParameter('eag_prod').setValues(
np.asarray(eag_prod, dtype=np.float32))
# overloaded function 'VariableInstance_fix' need float64
bud_fix = np.asarray(
df_scenario[df_scenario.ParameterName == 'bud_fix'].Value,
dtype=np.float64)
ds_fix = np.asarray(
df_scenario[df_scenario.ParameterName == 'ds_fix'].Value,
dtype=np.float64)
swtp_fix = np.asarray(
df_scenario[df_scenario.ParameterName == 'swtp_fix'].Value,
dtype=np.float64)
ampl.getParameter('bud_fix').setValues(
np.asarray(bud_fix, dtype=np.float32))
ampl.getParameter('ds_fix').setValues(np.asarray(ds_fix, dtype=np.float32))
ampl.getParameter('swtp_fix').setValues(
np.asarray(swtp_fix, dtype=np.float32))
# ---------------------------------------
# initialize/fix variable values
ampl.getParameter('res_init').value = RES_INIT
res_vol = ampl.getVariable('res_vol')
res_vol[0].fix(RES_INIT)
gw_prod = ampl.getVariable('gw_prod')
bud_prod = [
gw_prod[j, i] for ((j, i), k) in gw_prod.instances() if j == 'BUD'
]
for i in range(len(bud_prod)):
bud_prod[i].fix(bud_fix[i])
ds_prod = ampl.getVariable('ds_prod')
for i in range(ds_prod.numInstances()):
ds_prod[i + 1].fix(ds_fix[i])
swtp_prod = ampl.getVariable('swtp_prod')
for i in range(swtp_prod.numInstances()):
swtp_prod[i + 1].fix(swtp_fix[i])
# ---------------------------------------
# dump data
with open('dump.dat', 'w') as f:
with stdout_redirected(f):
ampl.display('wpda,monyr')
ampl.display('demand')
ampl.display('nyears')
ampl.display('ndays_mo,yearno,monthno')
# ampl.display('years')
# ampl.display('dem_total')
ampl.display(
'ch_poc,reg_lithia,reg_cot,reg_thic,crw_prod,eag_prod,bud_fix,ds_fix'
)
ampl.display('wup_12mavg')
ampl.display('ppp_sum12')
ampl.display('ngw_avail')
ampl.display('prod_range_lo,prod_range_hi')
ampl.display('relcost')
ampl.display('penfunc_x,penfunc_r')
# =======================================
# silence solver
with open('nul', 'w') as f:
with stdout_redirected(f):
ampl.solve()
if f_out != None:
with open(f_out, 'w') as f:
print(r'# *** SOURCE ALLOCATION MODEL ****', file=f)
print(r'# Monthly Delivery and Supply for Budgeting', file=f)
print('\n# Objective: {}'.format(
ampl.getObjective('mip_obj').value()),
file=f)
if (f_log != None) & (ampl.getObjective('mip_obj').result() == 'solved'):
with open(f_log, "w") as f:
print('\n\nDump Variable and Constraint Values', file=f)
with stdout_redirected(f):
write_log(ampl)
if ampl.getObjective('mip_obj').result() == 'infeasible':
if f_log != None:
with open(f_log, "w") as f:
with stdout_redirected(f):
write_iis(ampl)
else:
write_iis(ampl)
# =======================================
# print output
# groundwater production
temp = ampl.getVariable('gw_prod').getValues().toPandas().join(
ampl.getVariable('gw_under').getValues().toPandas()).join(
ampl.getVariable('gw_over').getValues().toPandas())
temp.columns = [i.replace('.val', '') for i in temp.columns]
# pivoting df by source
cwup_prod = temp.loc[[i for i in temp.index if i[0] == 'CWUP'], :].assign(
index=monyr).set_index('index').rename(columns={
'gw_prod': 'cwup_prod',
'gw_under': 'cwup_under',
'gw_over': 'cwup_over'
})
bud_prod = temp.loc[[i for i in temp.index if i[0] == 'BUD'], :].assign(
index=monyr).set_index('index').rename(columns={
'gw_prod': 'bud_prod',
'gw_under': 'bud_under',
'gw_over': 'bud_over'
})
sch_prod = temp.loc[[i for i in temp.index if i[0] == 'SCH'], :].assign(
index=monyr).set_index('index').rename(columns={
'gw_prod': 'sch_prod',
'gw_under': 'sch_under',
'gw_over': 'sch_over'
})
temp = cwup_prod.join(sch_prod.join(bud_prod))
gw_results = temp
temp_avg = temp.groupby(by=yearno).mean().reset_index()
temp_avg = temp_avg.loc[:,
[i for i in temp_avg.columns[1:temp_avg.shape[1]]]]
if f_out != None:
with open(f_out, 'a') as f:
# print heading
print('\n\n# Monthly Groudwater Production', file=f)
for l in range(len(temp)):
if (l % 12) == 0:
print(('\n%10s' % 'Yr-Month') +
('%11s' * len(temp.columns) % tuple(temp.columns)),
file=f)
print(('%10s' % monyr[l]) + ('%11.3f' * len(temp.columns) %
tuple(temp.iloc[l, :].values)),
file=f)
if (l + 1) % 12 == 0:
print(('%10s' % 'Average') +
('%11.3f' * len(temp_avg.columns) %
tuple(temp_avg.iloc[l // 12, :].values)),
file=f)
print(
('\n%10s' % 'Total Avg') + ('%11.3f' * len(temp_avg.columns) %
tuple(temp_avg.mean().values)),
file=f)
# ---------------------------------------
# SWTP Production
to_swtp = ampl.getVariable('to_swtp').getValues().toPandas()
to_res = ampl.getVariable('to_res').getValues().toPandas()
idx = to_swtp.index
temp = ampl.getVariable('swtp_prod').getValues().toPandas()
temp = temp.assign(tbc_swtp=to_swtp.loc[[i for i in idx
if i[0] == 'TBC']].values)
temp = temp.assign(
alf_swtp=to_swtp.loc[[i for i in idx if i[0] == 'Alafia']].values)
temp = temp.join(ampl.getVariable('res_eff').getValues().toPandas())
temp = temp.assign(tbc_res=to_res.loc[[i for i in idx
if i[0] == 'TBC']].values)
temp = temp.assign(alf_res=to_res.loc[[i for i in idx
if i[0] == 'Alafia']].values)
temp = temp.join(ampl.getVariable('res_inf').getValues().toPandas()).join(
ampl.getVariable('res_vol').getValues().toPandas())
# Add availability columns
temp = temp.assign(tbc_avail=sw_avail.loc[[('TBC', i) for i in monthno],
['value']].values)
temp = temp.assign(alf_avail=sw_avail.loc[[('Alafia', i) for i in monthno],
['value']].values)
# Add SW withdraws
df1 = ampl.getVariable('sw_withdraw').getValues().toPandas()
idx = temp.index
temp = temp.assign(tbc_wthdr=df1.loc[[('TBC', i) for i in idx], :].values)
temp = temp.assign(alf_wthdr=df1.loc[[('Alafia', i)
for i in idx], :].values)
temp.columns = [i.replace('.val', '') for i in temp.columns]
sw_results = temp
# Compute annual average
temp_avg = temp.groupby(by=yearno).mean().reset_index()
temp_avg = temp_avg.loc[:,
[i for i in temp_avg.columns[1:temp_avg.shape[1]]]]
if f_out != None:
with open(f_out, 'a') as f:
# print heading
print('\n\n# Monthly Surface Water Production', file=f)
for l in range(len(temp)):
if (l % 12) == 0:
print(('\n%10s' % 'Yr-Month') +
('%10s' * len(temp.columns) % tuple(temp.columns)),
file=f)
print(('%10s' % monyr[l]) + ('%10.3f' * len(temp.columns) %
tuple(temp.loc[float(l + 1), :])),
file=f)
if ((l + 1) % 12) == 0:
print(('%10s' % 'Average') +
('%10.3f' * len(temp_avg.columns) %
tuple(temp_avg.loc[l // 12, :])),
file=f)
print(
('\n%10s' % 'Total Avg') + ('%10.3f' * len(temp_avg.columns) %
tuple(temp_avg.mean().values)),
file=f)
# ---------------------------------------
# print multi objective values
temp = ampl.getVariable('prodcost_avg').getValues().toPandas()
idx = [i for i in temp.index]
temp = temp.assign(
prod_avg=temp.loc[:, 'prodcost_avg.val'] * 1e-3 /
np.asarray([df_relcost.loc[i[0], 'relcost'] for i in idx]))
temp = temp.join(
ampl.getVariable('uu_penalty').getValues().toPandas()).join(
ampl.getVariable('uu_avg').getValues().toPandas())
temp.columns = [i.replace('.val', '') for i in temp.columns]
temp_avg = temp.groupby([i[0] for i in temp.index]).mean().reset_index()
if f_out != None:
with open(f_out, 'a') as f:
print(
'\n\n# Annual Production and Under Utilization (Opportunity) Costs',
file=f)
for l in range(len(temp)):
if (l % nyears) == 0:
print(('\n%10s%10s' % ('YearNo', 'Source')) +
('%15s' * len(temp.columns) % tuple(temp.columns)),
file=f)
print(('%10d%10s' % (idx[l][1], idx[l][0])) +
('%15.3f' * len(temp.columns) %
tuple(temp.loc[[idx[l]], :].values[0])),
file=f)
if ((l + 1) % nyears) == 0:
print(('%10s' % 'Average') +
(('%10s' + '%15.3f' * len(temp.columns)) %
tuple(temp_avg.iloc[l // nyears, :])),
file=f)
ampl.close()
# prepare data for ploting
if f_out != None:
df_plotdata = df_demand.groupby(level=1).sum().join(df_demand.loc[(
'COT',
), ['Demand']].rename(columns={'Demand': 'COT'})).assign(
TBW_Demand=lambda x: x.Demand - x.COT).loc[:, ['TBW_Demand']].join(
gw_results.join(sw_results.set_index(gw_results.index)))
monyr = [pd.Timestamp(i + '-01') for i in df_plotdata.index]
df_plotdata = df_plotdata.assign(
Dates=monyr).set_index('Dates').sort_index()
df_plotdata = df_plotdata.assign(ndays_mo=ndays_mo)
plot_results(SID, AVAIL_PCTILE, df_plotdata, f_out)
def main(argc, argv):
# You can install amplpy with "python -m pip install amplpy"
from amplpy import AMPL
os.chdir(os.path.dirname(__file__) or os.curdir)
# Create an AMPL instance
ampl = AMPL()
"""
# If the AMPL installation directory is not in the system search path:
from amplpy import Environment
ampl = AMPL(
Environment('full path to the AMPL installation directory'))
"""
if argc > 1:
# ampl.set_option('solver', argv[1])
pass
# Must be solved with a solver supporting the suffix dunbdd
ampl.set_option("solver", "cplex")
ampl.set_option("presolve", False)
ampl.set_option("omit_zero_rows", False)
model_directory = os.path.join(
argv[2] if argc == 3 else os.path.join("..", "models"), "locationtransportation"
)
# Load the AMPL model from file
ampl.read(os.path.join(model_directory, "trnloc2.mod"))
# Read data
ampl.read_data(os.path.join(model_directory, "trnloc.dat"))
# Get references to AMPL's model entities for easy access.
ship_cost = ampl.get_objective("Ship_Cost")
max_ship_cost = ampl.get_variable("Max_Ship_Cost")
build_var = ampl.get_variable("Build")
supply = ampl.get_constraint("Supply")
demand = ampl.get_constraint("Demand")
num_cut_param = ampl.get_parameter("nCUT")
cut_type = ampl.get_parameter("cut_type")
build_param = ampl.get_parameter("build")
supply_price = ampl.get_parameter("supply_price")
demand_price = ampl.get_parameter("demand_price")
num_cut_param.set(0)
max_ship_cost.set_value(0)
build_param.set_values([1] * ampl.get_set("ORIG").size())
num_cuts = 0
while True:
num_cuts += 1
print("Iteration {}".format(num_cuts))
ampl.display("build")
# Solve the subproblem.
ampl.eval("solve Sub;")
result = ship_cost.result()
if result == "infeasible":
# Add a feasibility cut.
num_cut_param.set(num_cuts)
cut_type.set(num_cuts, "ray")
for index, value in supply.get_values(["dunbdd"]):
supply_price[index, num_cuts] = value
for index, value in demand.get_values(["dunbdd"]):
demand_price[index, num_cuts] = value
elif ship_cost.value() > max_ship_cost.value() + 0.00001:
# Add an optimality cut.
num_cut_param.set(num_cuts)
cut_type.set(num_cuts, "point")
ampl.set_option("display_1col", 0)
ampl.display("Ship")
for index, value in supply.get_values():
supply_price[index, num_cuts] = value
for index, value in demand.get_values():
demand_price[index, num_cuts] = value
else:
break
# Re-solve the master problem.
print("RE-SOLVING MASTER PROBLEM")
ampl.eval("solve Master;")
solve_result = ampl.get_value("solve_result")
if solve_result != "solved":
raise Exception("Failed to solve (solve_result: {})".format(solve_result))
# Copy the data from the Build variable used in the master problem
# to the build parameter used in the subproblem.
build_param.set_values(build_var.get_values())
print("\nProcedure completed in {} iterations\n".format(num_cuts))
ampl.display("Ship")
ampl.param['T'][a] = param_t['t'][d_type['date_type'][w,d],a] # Changes in parameter T[a](appliance time to operate) based on day d
ampl.param['W'][a] = param_w['w'][d_type['date_type'][w,d],a] # Changes in parameter W[a](priority of appliance) based on day d
ampl.param['W'][1] = priority_Washer # weekly required operation : update priority of washer
ampl.param['W'][2] = priority_Dryer # weekly required operation : update priority of dryer
ampl.param['T'][1] = opt_time_Washer # weekly required operation : update operation time of washer
ampl.param['T'][2] = opt_time_Dryer # weekly required operation : update operation time of dryer
print("")
print("Type of day {d} of week {w} : ".format(d=d, w=w), ampl.getValue('DATE[{w},{d}]'.format(w=w, d=d)))
print("PV size :", ampl_rpv*ampl_apv, "kW", "/", "Battery capacity :", x_bat, "kWh")
print("SISC for day {d} of week {w} : ".format(d=d, w=w), ampl.getValue('SISC'))
ampl.solve() # solving the model : obtain pre-schedules of applainces operation based on the forecasted solar irradiance data
ampl.display('sbat[24]*CBAT;') # pre-schedules of applainces operation
daily_energy_generated_forecasted [w,d] = ampl.getValue ('sum{t in Time} EPV[t];')
yearly_energy_generated_forecasted = yearly_energy_generated_forecasted + daily_energy_generated_forecasted [w,d] # yearly energy generated by pv system from AMPL model.
daily_energy_demand_forecasted [w,d] = ampl.getValue ('sum{a in Appliance} EAPP[a] * T[a] + sum{t in Time} EM[t];') # yearly energy demand -> since it doesn't consider operation of weekly appliances, it might be greater than actual.
yearly_energy_demand_forecasted = yearly_energy_demand_forecasted + daily_energy_demand_forecasted [w,d] # yearly energy demand -> since it doesn't consider operation of weekly appliances, it might be greater than actual.
daily_energy_appliance_total_forecasted [w,d] = ampl.getValue ('sum{t in Time, a in Appliance} EAPP[a] * xapp_state[a,t] + sum{t in Time} EM[t];')
yearly_energy_appliance_total_forecasted = yearly_energy_appliance_total_forecasted + daily_energy_appliance_total_forecasted [w,d] # yearly energy used by appliances -> since it doesn't consider operation of weekly appliances, it might be greater than actual.
daily_energy_appliance_pv_forecasted [w,d] = ampl.getValue('sum{t in Time} eapp_pv[t];')
yearly_energy_appliance_pv_forecasted = yearly_energy_appliance_pv_forecasted + daily_energy_appliance_pv_forecasted [w,d] # yearly energy used by appliance from pv
daily_energy_appliance_battery_forecasted [w,d] = ampl.getValue ('sum{t in Time} eapp_bat[t];')
yearly_energy_appliance_battery_forecasted = yearly_energy_appliance_battery_forecasted + daily_energy_appliance_battery_forecasted [w,d] # yearly energy used by appliance from battery
