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instance_structure.py
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instance_structure.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Nov 4 17:54:32 2014
@author: cdhagmann
"""
import random
import itertools
import os
import shutil
import pickle
from Crispin.bash import id_generator, cat
import pdat
DISTANCE_IN_MILES = [25, 50, 75, 100, 150,
200, 250, 300, 350, 400,
500, 600, 700, 800, 900, 1000, 1100]
FIXED_COST = [40.42, 45.59, 52.27, 66.13, 75.28,
85.67, 97.5, 110.96, 126.26, 143.71,
186.09, 217.05, 253.17, 297.11, 344.43, 401.77, 468.61]
VARIABLE_FIX = [427.6, 441.14, 482.83, 492.45, 521.43,
541.39, 570.02, 598.03, 622.3, 659.96,
727.61, 802.19, 884.41, 975.07, 1075.01, 1185.2, 1306.68]
VARIABLE = [0.022, 0.027, 0.029, 0.034, 0.036,
0.041, 0.042, 0.045, 0.047, 0.05,
0.055, 0.061, 0.067, 0.074, 0.081, 0.09, 0.099]
def randomize(number, stdev=.1):
"""
Returns a number from the normal distribution described with mean of
number with standard deviation of stdev * number.
"""
random_load = random.normalvariate(number, stdev * number)
return int(max([0, round(random_load, 0)]))
def scale(lower_bound, upper_bound, number):
"""
Create an evenly spaced list of specified number where
returned_list[0] == lower_bound and returned_list[-1] == upper_bound
"""
gen = (1. * n / (number - 1) for n in xrange(number))
return [lower_bound + (upper_bound - lower_bound) * g for g in gen]
class InstanceStructure():
def __init__(self, S, V, P, I, J, T, K, seed=None):
self.seed = round(1000 * random.random(), 2) if seed is None else seed
self.STORES = ["s" + str(s + 1) for s in xrange(S)]
self.VENDORS = ["v" + str(v + 1) for v in xrange(V)]
self.PRODUCTS = ["p" + str(p + 1) for p in xrange(P)]
self.PUTAWAY = ["i" + str(i + 1) for i in xrange(I)]
self.PICKING = ["j" + str(j + 1) for j in xrange(J)]
self.TIMES = [str(t + 1) for t in xrange(T)]
self.SCENARIOS = ["k" + str(k + 1) for k in xrange(K)]
self.case = '_'.join(['{}'] * 7).format(S, V, P, I, J, T, K)
self.S, self.V, self.P, self.T = S, V, P, T
self.I, self.J, self.K = I, J, K
random.seed(self.seed)
self.ID = id_generator()
self.generate_fixed_data()
self.generate_random_data()
self.generate_correlation_matrix()
self.generate_technology_data()
self.generate_demand_data()
self.generate_transportation_data()
self.write()
@classmethod
def from_file(cls, ID):
archive = 'Instances/instance_{}.pickle'.format(ID)
if os.path.isfile(archive):
with open(archive, 'rb') as f:
cls = pickle.load(f)
return cls
def write(self):
archive = 'Instances/instance_{}.pickle'.format(self.ID)
with open(archive, 'wb') as f:
pickle.dump(self, f, protocol=-1)
def generate_correlation_matrix(self):
from correlation_matrix import random_correlation_matrix
correlation_fraction = 0
self.correlation = random_correlation_matrix(self.P, correlation_fraction)
def generate_fixed_data(self):
self.GAMMA = 1.0
self.DELTA = 1.0
self.ETA = 1.0
self.SCRIPTQ = 15000
self.AVERAGE_LOAD = min([4000. / self.P, 50])
self.prob = {k: 1.0 / self.K for k in self.SCENARIOS}
self.Cz_p, self.Cz_sp, self.Cr_p = {}, {}, {}
for p in self.PRODUCTS:
self.Cz_p[p] = .05
self.Cr_p[p] = .10
for s in self.STORES:
self.Cz_sp[s, p] = .05
def generate_random_data(self):
FT_HOURLY = random.randint(25, 40)
PT_HOURLY = random.randint(20, min([FT_HOURLY - 1, 30]))
self.C_ALPHA = FT_HOURLY * 8.0 * self.T
self.C_BETA = PT_HOURLY * 8.0
self.VOLUME, self.WEIGHT = {}, {}
for p in self.PRODUCTS:
self.VOLUME[p] = round(random.random() * .1 + .01, 2)
self.WEIGHT[p] = round(random.random() * .1 + .90, 2)
self.product_list = {}
for s in self.STORES:
self.product_list[s] = random.sample(self.PRODUCTS, len(self.PRODUCTS)/2)
def generate_technology_data(self):
self.TECH_COST, self.LAMBDA = {}, {}
RATE_SCALE = scale(4800, 48000, self.I)
COST_SCALE = scale(0, .06, self.I)
for i, t in enumerate(self.PUTAWAY):
self.LAMBDA[t] = randomize(RATE_SCALE[i])
self.TECH_COST[t] = randomize(self.LAMBDA[t] * COST_SCALE[i])
RATE_SCALE = scale(800, 8000, self.J)
COST_SCALE = scale(.03, .15, self.J)
for i, t in enumerate(self.PICKING):
self.LAMBDA[t] = randomize(RATE_SCALE[i])
self.TECH_COST[t] = randomize(self.LAMBDA[t] * COST_SCALE[i])
self.Lambda_put = {i: self.LAMBDA[i] for i in self.PUTAWAY}
self.Lambda_pick = {j: self.LAMBDA[j] for j in self.PICKING}
self.Cth_put = {i: self.TECH_COST[i] for i in self.PUTAWAY}
self.Cth_pick = {j: self.TECH_COST[j] for j in self.PICKING}
def generate_demand_data(self):
from correlation_matrix import uniform_demand_distribution, neg_binom_demand_distribution
self.DEMAND = {}
for s in self.STORES:
for k in self.SCENARIOS:
demand = uniform_demand_distribution(self.correlation, self.T, 0, int(2 * self.AVERAGE_LOAD / self.P))
for j, t in enumerate(self.TIMES):
for i, p in enumerate(self.PRODUCTS):
if p in self.product_list[s]:
self.DEMAND[s,p,t,k] = int(demand[i,j])
else:
self.DEMAND[s,p,t,k] = 0
# self.DEMAND = {}
# for s in self.STORES:
# for p in self.PRODUCTS:
# mu = randomize(self.AVERAGE_LOAD, .4)
# for t in self.TIMES:
# demands = [randomize(mu, .25) for _ in xrange(self.K)]
# mu = random.choice(demands)
# for k, d in zip(self.SCENARIOS, sorted(demands)):
# self.DEMAND[s, p, t, k] = d
self.BigM = 0
for s in self.STORES:
for p in self.PRODUCTS:
for k in self.SCENARIOS:
temp = sum(self.DEMAND[s, p, t, k] for t in self.TIMES)
self.BigM = temp + 1 if temp > self.BigM else self.BigM
def generate_transportation_data(self):
self.Cv_v, self.Cf_v = {}, {}
for v in self.VENDORS:
dist_subset = [x for x in DISTANCE_IN_MILES if x >= 250]
distance = random.choice(dist_subset)
idx = DISTANCE_IN_MILES.index(distance)
self.Cf_v[v] = FIXED_COST[idx] + VARIABLE_FIX[idx]
self.Cv_v[v] = VARIABLE[idx]
self.Cv_s, self.Cf_s = {}, {}
for s in self.STORES:
rnd_num = random.random()
if rnd_num <= .4:
dist_subset = [x for x in DISTANCE_IN_MILES if x <= 300]
elif .4 < rnd_num <= .7:
dist_subset = [x for x in DISTANCE_IN_MILES if 300 <= x <= 800]
else:
dist_subset = DISTANCE_IN_MILES
distance = random.choice(dist_subset)
idx = DISTANCE_IN_MILES.index(distance)
self.Cf_s[s] = FIXED_COST[idx] + VARIABLE_FIX[idx]
self.Cv_s[s] = VARIABLE[idx]
def create_node_data(self, method=None):
if method is None:
methods = ('GBB', 'RLT', 'BigM')
elif isinstance(method, (list, tuple)):
for mthd in method:
assert mthd in ('GBB', 'RLT', 'BigM')
methods = tuple(method)
elif isinstance(method, str):
assert method in ('GBB', 'RLT', 'BigM')
methods = (method,)
else:
raise ValueError
for mthd in methods:
shutil.rmtree('models_{}/nodedata'.format(mthd))
os.mkdir('models_{}/nodedata'.format(mthd))
def mpath(archive):
return 'models_{}/nodedata/{}.dat'.format(mthd, archive)
with open(mpath('RootNodeBase'), 'w') as f:
f.write('# Case of size [{}]\n\n'.format(self.case))
pdat.set_dat(f, 'STORES', self.STORES)
pdat.set_dat(f, 'PRODUCTS', self.PRODUCTS)
pdat.set_dat(f, 'VENDORS', self.VENDORS)
pdat.set_dat(f, 'TIMES', self.TIMES)
if mthd in ('RLT', 'BigM'):
pdat.set_dat(f, 'PUTAWAY', self.PUTAWAY)
pdat.set_dat(f, 'PICKING', self.PICKING)
if mthd == 'GBB':
IJ = list(itertools.product(self.PUTAWAY, self.PICKING))
for idx, (i, j) in enumerate(IJ):
file_name = 'Tech{}Node'.format(idx)
with open(mpath(file_name), 'w') as g:
pdat.param_dat(g, 'Lambda_put', self.Lambda_put[i])
pdat.param_dat(g, 'Lambda_pick', self.Lambda_pick[j])
pdat.param_dat(g, 'Cth_put', self.Cth_put[i])
pdat.param_dat(g, 'Cth_pick', self.Cth_pick[j])
else:
pdat.param_dat(f, 'Lambda_put', self.Lambda_put, self.PUTAWAY)
pdat.param_dat(f, 'Lambda_pick', self.Lambda_pick, self.PICKING)
pdat.param_dat(f, 'Cth_put', self.Cth_put, self.PUTAWAY)
pdat.param_dat(f, 'Cth_pick', self.Cth_pick, self.PICKING)
pdat.param_dat(f, 'A_put', self.AVERAGE_LOAD)
pdat.param_dat(f, 'A_pick', self.AVERAGE_LOAD)
pdat.param_dat(f, 'gamma', self.GAMMA)
pdat.param_dat(f, 'delta', self.DELTA)
pdat.param_dat(f, 'eta_put', self.ETA)
pdat.param_dat(f, 'eta_pick', self.ETA)
pdat.param_dat(f, 'ScriptQ', self.SCRIPTQ)
pdat.param_dat(f, 'V_p', self.VOLUME, self.PRODUCTS)
pdat.param_dat(f, 'W_p', self.WEIGHT, self.PRODUCTS)
pdat.param_dat(f, 'Cf_v', self.Cf_v, self.VENDORS)
pdat.param_dat(f, 'Cv_v', self.Cv_v, self.VENDORS)
pdat.param_dat(f, 'Cf_s', self.Cf_s, self.STORES)
pdat.param_dat(f, 'Cv_s', self.Cv_s, self.STORES)
pdat.param_dat(f, 'Cz_p', self.Cz_p, self.PRODUCTS)
pdat.param_dat(f, 'Cz_sp', self.Cz_sp, [self.STORES, self.PRODUCTS])
pdat.param_dat(f, 'Cr_p', self.Cr_p, self.PRODUCTS)
pdat.param_dat(f, 'Ca', self.C_ALPHA)
pdat.param_dat(f, 'Cb', self.C_BETA)
if mthd == 'GBB':
pass
elif mthd == 'RLT':
pdat.param_dat(f, 'M_alpha', 200)
pdat.param_dat(f, 'M_beta', 200)
else:
pdat.param_dat(f, 'BigM', self.BigM)
def create_scenario_data(self, method=None):
if method is None:
methods = ('GBB', 'RLT', 'BigM')
elif isinstance(method, (list, tuple)):
for mthd in method:
assert mthd in ('GBB', 'RLT', 'BigM')
methods = tuple(method)
elif isinstance(method, str):
assert method in ('GBB', 'RLT', 'BigM')
methods = (method,)
else:
raise ValueError
for mthd in methods:
def mpath(archive):
return 'models_{}/nodedata/{}.dat'.format(mthd, archive)
for idx, k in enumerate(self.SCENARIOS):
file_name = 'Scenario{}Node'.format(idx + 1)
with open(mpath(file_name), 'w') as f:
f.write('# Demand data for Scenario {}\n\n'.format(idx + 1))
f.write('param d_spt :=\n')
spt = itertools.product(self.STORES, self.PRODUCTS, self.TIMES)
for s, p, t in spt:
info = (' '.join([s, p, t]), self.DEMAND[s, p, t, k])
f.write('{} {}\n'.format(*info))
f.write(';\n\n')
# Create reference files for checks
if mthd == 'GBB':
filenames = [mpath('RootNodeBase'), mpath('Tech0Node')]
cat(mpath('RootNode'), filenames)
else:
cat(mpath('RootNode'), (mpath('RootNodeBase')))
filenames = [mpath('RootNode'), mpath('Scenario1Node')]
cat(mpath('ReferenceModel'), filenames)
# Create the base of the ScenarioStructure file
with open(mpath('ScenarioStructureBase'), 'w') as f:
stages = ['FirstStage', 'SecondStage']
scenarionodes = []
p = {'RootNode': 1}
NodeStage = {'RootNode': 'FirstStage'}
for idx, k in enumerate(self.SCENARIOS, 1):
scenario_node = 'Scenario{}Node'.format(idx)
scenarionodes.append(scenario_node)
p[scenario_node] = self.prob[k]
NodeStage[scenario_node] = 'SecondStage'
nodes = ['RootNode'] + scenarionodes
StageCostVariable = {stage: '{}Cost'.format(stage) for stage in stages}
ScenarioLeafNode = dict(zip(self.SCENARIOS, scenarionodes))
FirstStageVariables = ['alpha_put',
'alpha_pick']
if mthd in ('RLT', 'BigM'):
FirstStageVariables += ['theta_put[*]',
'theta_pick[*]']
if mthd == 'RLT':
FirstStageVariables += ['zeta_put[*]',
'zeta_pick[*]']
SecondStageVariables = ['beta_put[*]',
'beta_pick[*]',
'n_vt[*,*]',
'n_st[*,*]',
'x_vpt[*,*,*]',
'y_spt[*,*,*]',
'z_pt[*,*]',
'z_spt[*,*,*]',
'r_spt[*,*,*]']
if mthd == 'RLT':
SecondStageVariables = ['xi_put[*,*]',
'xi_pick[*,*]']
pdat.set_dat(f, 'Stages', stages)
pdat.set_dat(f, 'Nodes', nodes)
pdat.param_dat(f, 'NodeStage', NodeStage, nodes)
pdat.set_dat(f, 'Children[RootNode]', scenarionodes)
pdat.param_dat(f, 'ConditionalProbability', p, nodes)
pdat.set_dat(f, 'Scenarios', self.SCENARIOS)
pdat.param_dat(f, 'ScenarioLeafNode', ScenarioLeafNode, self.SCENARIOS)
pdat.param_dat(f, 'StageCostVariable', StageCostVariable, stages)
pdat.param_dat(f, 'ScenarioBasedData', False)
f.write('set {} := \t{};\n'.format('StageVariables[FirstStage]',
'\n\t\t'.join(FirstStageVariables)))
# Defining the Second Stage differently as to allow for faster PH runs
with open(mpath('ScenarioStructureEF'), 'w') as f:
f.write('set {} := {};\n\n'.format('StageVariables[SecondStage]',
'\n\t'.join(SecondStageVariables)))
with open(mpath('ScenarioStructurePH'), 'w') as f:
f.write('set {} := ;\n\n'.format('StageVariables[SecondStage]'))
# Create reference files for check
filenames = [mpath('ScenarioStructureBase'), mpath('ScenarioStructureEF')]
cat(mpath('ScenarioStructure'), filenames)
# Create WW config file
with open('config/wwph.suffixes', 'w') as f:
FirstStageVariables = ['alpha_put', 'alpha_pick']
Costs = [self.C_ALPHA, self.C_BETA]
for idx, fsv in enumerate(FirstStageVariables):
f.write('{} {} {}\n'.format(fsv, 'CostForRho', Costs[idx]))