def greedy_allocation(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
"""
# Number of retailers
R = parameters['retailers']
if not parameters['distribution']:
print 'No distribution set...abort'
exit(1)
# elif parameters['distribution'] == 'truncnorm':
# rvs = [truncnorm_custom(parameters['min_intrv{}'.format(i)],
# parameters['max_intrv{}'.format(i)],
# parameters['mu{}'.format(i)],
# parameters['sigma{}'.format(i)]) for i in xrange(1, R+1)]
elif parameters['distribution'] == 'norm':
rvs = [norm(parameters['mu{}'.format(i)],
parameters['sigma{}'.format(i)]) for i in xrange(1, R+1)]
elif parameters['distribution'] == 'uniform':
rvs = [uniform(loc=parameters['mu{}'.format(i)],
scale=parameters['sigma{}'.format(i)]) for i in xrange(1, R+1)]
elif parameters['distribution'] == 'triang':
rvs = [triang(loc=parameters['min_intrv{}'.format(i)],
scale=parameters['max_intrv{}'.format(i)],
c=parameters['mu{}'.format(i)]) for i in xrange(1, R+1)]
else:
print 'Distribution not recognized...abort'
exit(1)
A = parameters['A']
Q = {i: 0 for i in xrange(R)}
while A > 0:
best_probability = -1
best_retailer = -1
for n, r in enumerate(rvs):
p = 1 - r.cdf(Q[n]+1)
if p > best_probability:
best_probability = p
best_retailer = n
Q[best_retailer] += 1
A -= 1
for i in xrange(1, R+1):
parameters['Q{}'.format(i)] = Q[i-1]
ret = {'Q{}'.format(i): Q[i-1] for i in xrange(1, R+1)}
ret['PROB'] = integral(parameters)
return ret
def integral_wrapper(x0, parameters):
R = parameters['retailers']
for i in xrange(1, R+1):
parameters['Q{}'.format(i)] = x0[i-1]
return -1. * integral(parameters)