def mypool(parameters, lb, ub, wid):
# print "Start lb: ", lb, " ub: ", ub
# print "parameters received by mypool: ", id(parameters)
print "Starting worker %d..." % wid
results = {}
best = {'worker': wid, 'allocation': (-1, -1, -1), 'value': 0.0}
A = int(parameters['A'])
# print "A: ", A
# counter = 0
for i in xrange(lb, ub, parameters['gridstep']):
for j in xrange(0, A - i + 1, parameters['gridstep']):
k = A - i - j
# print "--->i, j, k: ", i,", ",j,", ", k, ", ", i+j+k
# counter += 1
if i + j + k != A:
print "ERROR: ", i + j + k, " LB: ", lb, " UB: ", ub
# from integer to float, faster than float(i)
parameters['Q1'] = 1. * i
parameters['Q2'] = 1. * j
parameters['Q3'] = 1. * k
results[(i, j, k)] = f3TruncNormRVSnp(parameters)
if best['value'] < results[(i, j, k)]:
# print "old value: ", best['value'], " vs new value: ",results[(i,j,k)]
best['value'] = results[(i, j, k)]
best['allocation'] = (i, j, k)
# print "update best value from worker %d: %f @ %d %d %d" % (wid, best['value'], i,j,k)
print "Worker %d completed!" % wid
return best
def heu_allocation3(df, parameters):
"""
Greedy heuristic for 3 supplier
:param df: dataframe containing the data from the excel file
:param parameters: parameters dict
:return: write o the df and save on the file
"""
for index, row in df.iterrows():
print "Elaborating index {}".format(index)
# read parameters from the excel file
parameters['mu1'] = row['MU1']
parameters['mu2'] = row['MU2']
parameters['mu3'] = row['MU3']
parameters['sigma1'] = row['S1']
parameters['sigma2'] = row['S2']
parameters['sigma3'] = row['S3']
parameters['target'] = row['TARGET']
parameters['A'] = row['AVAIL']
rv1 = truncnorm_custom(parameters['min_intrv1'], parameters['max_intrv1'], parameters['mu1'], parameters['sigma1'])
rv2 = truncnorm_custom(parameters['min_intrv2'], parameters['max_intrv2'], parameters['mu2'], parameters['sigma2'])
rv3 = truncnorm_custom(parameters['min_intrv3'], parameters['max_intrv3'], parameters['mu3'], parameters['sigma3'])
A = parameters['A']
i = 0
Q = {i:0 for i in xrange(3)}
log = []
while A >= 0:
best_probability = -1
best_retailer = -1
l = {}
for n, r in enumerate([rv1, rv2, rv3]):
p = 1 - r.cdf(Q[n]+1)
l[n] = p
# print "n: {} p: {}".format(n,p)
if p > best_probability:
best_probability = p
best_retailer = n
# print "Best retailer: {} with value {}".format(best_retailer, best_probability)
Q[best_retailer] += 1
A -= 1
# print "Current allocation: {}".format(Q)
log.append(l)
# print "best allocation:"
# print Q
parameters['Q1'] = Q[0]
parameters['Q2'] = Q[1]
parameters['Q3'] = Q[2]
df.ix[index, 'ALLOC_HEU'] = str(Q.values())
df.ix[index, 'HEURISTIC_VALUE'] = f3TruncNormRVSnp(parameters)
df.to_excel(parameters['outfile'])
def greedy_allocation3(parameters):
"""
Greedy heuristic for 3 supplier (the same as heu_allocation3 but with different parameters)
Does not write on the file but returns the solution
:param df: dataframe containing the data from the excel file
:param parameters: parameters dict
:return: write o the df and save on the file
"""
if not parameters['distribution']:
print 'No distribution set...abort'
exit(1)
elif parameters['distribution'] == 'truncnorm':
rv1 = truncnorm_custom(parameters['min_intrv1'], parameters['max_intrv1'], parameters['mu1'], parameters['sigma1'])
rv2 = truncnorm_custom(parameters['min_intrv2'], parameters['max_intrv2'], parameters['mu2'], parameters['sigma2'])
rv3 = truncnorm_custom(parameters['min_intrv3'], parameters['max_intrv3'], parameters['mu3'], parameters['sigma3'])
elif parameters['distribution'] == 'norm':
rv1 = norm(parameters['mu1'], parameters['sigma1'])
rv2 = norm(parameters['mu2'], parameters['sigma2'])
rv3 = norm(parameters['mu3'], parameters['sigma3'])
elif parameters['distribution'] == 'uniform':
rv1 = uniform(loc=parameters['mu1'], scale=parameters['sigma1'])
rv2 = uniform(loc=parameters['mu2'], scale=parameters['sigma2'])
rv3 = uniform(loc=parameters['mu3'], scale=parameters['sigma3'])
elif parameters['distribution'] == 'beta':
rv1 = beta(a=parameters['min_intrv1'], b=parameters['max_intrv1'], loc=parameters['mu1'], scale=parameters['sigma1'])
rv2 = beta(a=parameters['min_intrv2'], b=parameters['max_intrv2'], loc=parameters['mu2'], scale=parameters['sigma2'])
rv3 = beta(a=parameters['min_intrv3'], b=parameters['max_intrv3'], loc=parameters['mu3'], scale=parameters['sigma3'])
elif parameters['distribution'] == 'triang':
rv1 = triang(loc=parameters['min_intrv1'], scale=parameters['max_intrv1'], c=parameters['mu1'])
rv2 = triang(loc=parameters['min_intrv2'], scale=parameters['max_intrv2'], c=parameters['mu2'])
rv3 = triang(loc=parameters['min_intrv3'], scale=parameters['max_intrv3'], c=parameters['mu3'])
else:
print 'Distribution not recognized...abort'
exit(1)
A = parameters['A']
Q = {i: 0 for i in xrange(3)}
while A > 0:
best_probability = -1
best_retailer = -1
for n, r in enumerate([rv1, rv2, rv3]):
p = 1 - r.cdf(Q[n]+1)
if p > best_probability:
best_probability = p
best_retailer = n
Q[best_retailer] += 1
A -= 1
parameters['Q1'] = Q[0]
parameters['Q2'] = Q[1]
parameters['Q3'] = Q[2]
return {'Q1': Q[0],
'Q2': Q[1],
'Q3': Q[2],
'PROB': f3TruncNormRVSnp(parameters)}
def f3wrapper(x0, parameters):
parameters['Q1'] = x0[0]
parameters['Q2'] = x0[1]
parameters['Q3'] = x0[2]
return -1. * f3TruncNormRVSnp(parameters)
while A >= 0:
best_probability = -1
best_retailer = -1
l = {}
for n, r in enumerate([rv1, rv2, rv3]):
p = 1 - r.cdf(Q[n]+1)
l[n] = p
# print "n: {} p: {}".format(n,p)
if p > best_probability:
best_probability = p
best_retailer = n
# print "Best retailer: {} with value {}".format(best_retailer, best_probability)
Q[best_retailer] += 1
A -= 1
# print "Current allocation: {}".format(Q)
log.append(l)
# print "best allocation:"
# print Q
parameters['Q1'] = Q[0]
parameters['Q2'] = Q[1]
parameters['Q3'] = Q[2]
df.ix[index, 'ALLOC_HEU'] = str(Q.values())
df.ix[index, 'HEURISTIC_VALUE'] = f3TruncNormRVSnp(parameters)
df.to_excel('ex_6_with_heuristics_results.xlsx')
