from gurobipy import *
import Read_Data
import Static_Model
#nitems, planningHorizon, MJC, DPP, DAVG, HLC, MNC, CNT_WEIGHT, CNT_VALUE, REQ_WEIGHT, REQ_VALUE, MIN_WEIGHT, MIN_VALUE = \
nitems, planningHorizon, S, D, DAVG, h, s, w, v, weightREQ, valueREQ, minWeight, minValue = \
Read_Data.read_Data ('setA-002.txt')
zs, ys, Bs = Static_Model.computeInitialSolution()
# creat periods and items to iterate through
periods = range(planningHorizon)
items = range(nitems)
M = {}
for i in items:
M[i] = 50000
warehouseC = 0
budgetC = 0
minC = 0
truckC = 0
#%% Creat patterns
import itertools
# Initialization
patterns = []
combinations = []
elements = [0] * nitems
patterns.append(tuple(elements))
#fill patterns with only initial solution pattern
for t in periods:
if zs[t] == 1:
from gurobipy import *
import Read_Data
import numpy as np
import time
import DJRP_20190704_V1
#filenumbers = ['01','02','03','04','05','06','07','08','09','10']
filenumbers = ['01', '02', '03', '04', '05']
for number in filenumbers:
folder = "_Daten_und_Loesungen\\E\\"
filename = "setE-0" + number + ".txt"
#nitems, planningHorizon, MJC, DPP, DAVG, HLC, MNC, CNT_WEIGHT, CNT_VALUE, REQ_WEIGHT, REQ_VALUE, MIN_WEIGHT, MIN_VALUE = \
nitems, planningHorizon, S, D, DAVG, h, s_minor, w, v, weightREQ, valueREQ, minWeight, minValue = \
Read_Data.read_Data (folder+filename)
# creat periods and items to iterate through
periods = range(planningHorizon)
items = range(nitems)
maxIter = 100
minAmountItems = np.linspace(0.85, 0.99, num=maxIter)
# Big M with sum over Demand
M = {}
for i in items:
M[i] = 0
for t in periods:
M[i] = M[i] + D[i, t]
start = time.time()
DJRP_Model, DJRP_z, DJRP_y, DJRP_B, DJRP_I = \
DJRP_20190704_V1.DJRP_run (nitems, planningHorizon, S, D, DAVG, h, s_minor, w, v, weightREQ, valueREQ, minWeight, minValue)
#%% Creat patterns
# Creat subpatterns and initialize for every period a pattern that can order everything
def computeInitialSolution():
# http://apmonitor.com/che263/index.php/Main/PythonOptimization
#import statistics
#nitems, planningHorizon, MJC, DPP, DAVG, HLC, MNC, CNT_WEIGHT, CNT_VALUE, REQ_WEIGHT, REQ_VALUE, MIN_WEIGHT, MIN_VALUE = \
nitems, planningHorizon, S, D, DAVG, h, s, w, v, weightREQ, valueREQ, minWeight, minValue = \
Read_Data.read_Data ('setA-002.txt')
items = range(nitems)
periods = range(planningHorizon)
Dm = {}
ss = {}
Soc = S[0]
for i in items:
Dm[i] = 0
ss[i] = s[i, 0]
for t in periods:
Dm[i] = Dm[i] + D[i, t]
Dm[i] = Dm[i] / planningHorizon
# Modelling optimization function
def objective(x):
T = x[nitems]
Total_holding_cost = 0
Total_setup_cost = 0
for i in items:
Total_holding_cost = Total_holding_cost + x[i] * T * 0.5 * Dm[
i] * h[i]
Total_setup_cost = Total_setup_cost + (1 / T) * (ss[i] / x[i])
Total_setup_cost = Total_setup_cost + (Soc / T)
return Total_holding_cost + Total_setup_cost
#def objective(x):
# return sum((x[0:items] * x[items] * Dm[0:items] * 0.5 * h[0:items]) \
# + (1 / x[items]) * (ss[0:items] / x[0:items])) + (1 / x[items]) * Soc
def constraint_value(x):
c_v = 0
T = x[nitems]
if (valueREQ == 1):
for i in items:
c_v = c_v + (x[i] * T * Dm[i] * v[i])
c_v = c_v - minValue
return c_v
def constraint_weight(x):
c_w = 0
T = x[nitems]
if (weightREQ == 1):
for i in items:
c_w = c_w + (x[i] * T * Dm[i] * w[i])
c_w = c_w - minWeight
return c_w
# Define decision variables and initialize parameters
x0 = np.zeros(
nitems + 1
) # the number of integer multipliers of T that a replensishment of item i will last
for i in items:
x0[i] = 1.0
x0[items] = planningHorizon # T
# show initial objective
print('Initial Objective: ' + str(objective(x0)))
# Constraints
# Constraints
bnds = []
for i in range(nitems + 1):
bnds.append([1.0, 5.0])
con1 = {
'type': 'ineq',
'fun': constraint_value
} # for instance of the formula, a - b >= 0
con2 = {'type': 'ineq', 'fun': constraint_weight}
cons = ([con1, con2])
##
start = time.time()
# SLSQP only solver can solve nonlinear
solution = minimize(objective,x0,method='SLSQP',\
bounds=bnds, constraints=cons)
x = solution.x
end = time.time()
print('conputational time = {}'.format(end - start))
# show final objective
print('Final Objective: ' + str(objective(x)))
# print solution
print('Solution')
for i in items:
# print(str(x[i]))
print('x{} = '.format(i) + str(x[i]))
print('T = ' + str(x[nitems]))
#Round values from the static solution
Ts = int(round(x[nitems]))
k = {}
for i in items:
k[i] = int(round(x[i]))
TC_rounded = 0
for i in items:
TC_rounded = TC_rounded + k[i] * Ts * Dm[i] * 0.5 * h[i] + (1 / Ts) * (
ss[i] / k[i])
TC_rounded = (TC_rounded + Soc / Ts) * planningHorizon
#Initialize z and y
z = {}
y = {}
B = {}
I = {}
for i in items:
for t in range(planningHorizon):
z[t] = 0
y[i, t] = 0
B[i, t] = 0
#Conversion from the static to the dynamic
for i in items:
Dm[i] = round(Dm[i])
for t in range(0, planningHorizon, Ts * k[i]):
B[i, t] = Dm[i] * Ts * k[i]
z[t] = 1
y[i, t] = 1
for i in items:
for t in range(planningHorizon):
if t != 0:
I[i, t] = I[i, t - 1] + B[i, t] - D[i, t]
else:
I[i, t] = B[i, t] - D[i, t]
#Total cost with the dynamic objective function with the static solution.
newTC = 0
for t in range(planningHorizon):
for i in items:
newTC = newTC + s[i, t] * y[i, t] + h[i] * I[i, t]
newTC = newTC + S[t] * z[t]
print(TC_rounded)
print(newTC)
return z, y, B
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 25 09:14:27 2019
@author: LeaGrahn
"""
from gurobipy import *
import Read_Data
#nitems, planningHorizon, MJC, DPP, DAVG, HLC, MNC, CNT_WEIGHT, CNT_VALUE, REQ_WEIGHT, REQ_VALUE, MIN_WEIGHT, MIN_VALUE = \
nitems, planningHorizon, S, D, DAVG, h, s, w, v, weightREQ, valueREQ, minWeight, minValue = \
Read_Data.read_Data ('setB-001.txt')
# creat periods and items to iterate through
periods = range(planningHorizon)
items = range(nitems)
#%% Creat patterns
import itertools
# Initialization
patterns = []
elements = []
combinations = []
#
## Fill patterns with one-item-patterns
#demand={}
#maxweight={}
#maxvalue={}
from gurobipy import *
import Read_Data
import Static_Model
import matplotlib.pyplot as plt
import numpy as np
import time
#nitems, planningHorizon, MJC, DPP, DAVG, HLC, MNC, CNT_WEIGHT, CNT_VALUE, REQ_WEIGHT, REQ_VALUE, MIN_WEIGHT, MIN_VALUE = \
inst_set = 'setB-002'
instance = inst_set + '.txt'
nitems, planningHorizon, S, D, DAVG, h, s, w, v, weightREQ, valueREQ, minWeight, minValue = \
Read_Data.read_Data (instance)
# Periods and items to iterate through
periods = range(planningHorizon)
items = range(nitems)
#Start modeling
m = Model("DJRP_model")
#Introduction of variables
z = {} #An order is placed at period t
y = {} #An order is placed at period t for item i
B = {} #Order quantity of item i arriving at the beginning of period t
I = {} #Level of inventory of item i at the end of period t
for t in periods:
z[t] = m.addVar(vtype=GRB.BINARY, name="Z_%s" % str(t))
for i in items:
y[i, t] = m.addVar(vtype=GRB.BINARY, name="Y_%s%s" % (str(i), str(t)))
B[i, t] = m.addVar(vtype=GRB.INTEGER,
name="B_%s%s" % (str(i), str(t)),
