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BuildModels.py
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2924 lines (2536 loc) · 136 KB
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 20 22:25:48 2022
Exact_model: MC+LP+Binary
@author: wyl2020
@email:wylwork_sjtu@sjtu.edu.cn
"""
"a demo problem"
import os, sys
import random, itertools, copy
import pandas as pd
import networkx as nx
from openpyxl import load_workbook
import gurobipy as grb
import numpy as np
import ToolFunctions as tf
#%% creat and verfy the folder paths
cpy = os.path.abspath(__file__)
cwd = os.path.abspath(os.path.join(cpy, "../"))
folder = os.path.abspath(os.path.join(cwd, "lpFolder"))
if not os.path.exists(folder):
os.makedirs(folder)
lpFolder = folder
#%%
class Option():
"""problem control parameters"""
def __init__(self, para_randomData=0, para_relaxModel=0, para_print_checkProcess=0):
grb_para_OutputFlag = 1
grb_para_timelimit = 100
para_printResult = 0
para_print_checkProcess = 0
# probType = 'CTN' # INT: integer random problem,; CTN: continuous random problem
ExtraConstrListList = ['CardiOff', 'CardiOn', 'Luce']
self.BarConvTol = 1e-5 # default 1e-8
self.BarQCPConvTol = 1e-5 # default 1e-6
self.grb_para_OutputFlag = grb_para_OutputFlag
self.grb_para_timelimit = grb_para_timelimit
self.para_randomData = para_randomData
self.ExtraConstrList = ExtraConstrListList
self.luceType = 'GroupPair'
self.luceTree_nodeRatio = 0.25
self.luceGroup_nodeRatio = 0.5
self.cardiMC = 0 # default 0, not use the cardinality modified MC relaxation
self.gapApproach = ['nodeLimit'] # nodeLimit or continuous
# self.kappaOff = 0.2 #调整offline cardinality约束的右侧数值占比
# self.kappaOn = 0.2 #online cardinality约束的右侧数值占比
# self.prior_r = 0.01 # 调整优先顺序的约束占比
self.delta = 0.1 # 调整线上线下 的 revenue的 相关性
self.para_relaxModel = para_relaxModel
self.para_print_checkProcess = para_print_checkProcess
self.para_printResult = para_printResult
self.para_logging = 0
self.para_write_lp = 0
self.para_plot = 0
self.para_plot_save = 0
self.plot_network = 0
self.compute_relax_gap = 1
# self.v0_off = 1
# self.v0_on = 5
self.utilitySparsity_off = 1
self.utilitySparsity_on = 1
self.read_data = 0
self.revenue_vip_group_number = 2
self.revenue_disc_range = [0.9,1]
self.revenue_range = [10,20]
self.arriveRatio = [0.5, 0.5]
self.cut_round_limit = 2
self.MMNL = 0 # modefied for MMNL model
folder = os.path.abspath(os.path.join(cwd, "lpFolder"))
if not os.path.exists(folder):
os.makedirs(folder)
self.lpFolder = folder
#%%
class Data():
def __init__(self, option, probSetting_info):
"""problemScale = (numProd,numCust)"""
####### control parameters
(numProd, numCust), arriveRatio_off, (v0_off, v0_on), luce, (kappaOff, kappaOn), (knapsack_off, knapsack_on) = probSetting_info
# self.arriveRatio = option.arriveRatio
# self.para_randomData = option.para_randomData
# self.para_relaxModel = option.para_relaxModel
# self.probType = option.attV_Type + option.revenue_Type + ''.join(option.ExtraConstrList)
# self.ExtraConstrList = option.ExtraConstrList
# self.luceType = option.luceType
# self.luceTree_nodeRatio = option.luceTree_nodeRatio
# self.luceGroup_nodeRatio = option.luceGroup_nodeRatio
# self.kappaOff = option.kappaOff
# self.kappaOn = option.kappaOn
# self.prior_r = option.prior_r
# self.delta = option.delta
# self.probName = ''
# self.luce_info = np.array(0)
# self.v0_off = option.v0_off
# self.v0_on = option.v0_on
# self.utilitySparsity_off = option.utilitySparsity_off
# self.utilitySparsity_on = option.utilitySparsity_on
# self.lpFolder = option.lpFolder
# self.separate_tol = 1e-10
# self.option = option
#
# if option.read_data == 0:
# self.generate_attractive_value(numProd, numCust, option.attV_Type)
# self.generate_revenue_data(numProd,
# numCust,
# option.revenue_Type,
# vip_group_number=option.revenue_vip_group_number,
# revenue_range=option.revenue_range,
# disc_range=option.revenue_disc_range)
# self.generate_extraCstr_data(numProd, numCust, ExtraConstrList=option.ExtraConstrList)
self.arriveRatio = [arriveRatio_off, 1 - arriveRatio_off]
self.kappaOff = kappaOff
self.kappaOn = kappaOn
self.luce = luce
self.knapsackOff = knapsack_off
self.knapsackOn = knapsack_on
self.probName = ''
self.luce_info = np.array(0)
self.v0_off = v0_off
self.v0_on = v0_on
self.ExtraConstrList = []
self.Ex_Cstr_Dict = dict()
self.utilitySparsity_off = option.utilitySparsity_off
self.utilitySparsity_on = option.utilitySparsity_on
self.ExtraConstrList = option.ExtraConstrList
self.luceType = option.luceType
self.luceTree_nodeRatio = option.luceTree_nodeRatio
self.luceGroup_nodeRatio = option.luceGroup_nodeRatio
self.attV_Type = option.attV_Type
self.revenue_Type = option.revenue_Type
self.generate_attractive_value(numProd, numCust, option.attV_Type)
self.generate_revenue_data(numProd,
numCust,
option.revenue_Type,
vip_group_number=option.revenue_vip_group_number,
revenue_range=option.revenue_range,
disc_range=option.revenue_disc_range)
self.generate_extraCstr_data(numProd, numCust)
self.separate_tol = 1e-10
def generate_attractive_value(self, numProd, numCust, attV_Type):
"""generate attractive value """
if attV_Type.lower() == 'int':
value_off_0 = self.v0_off
value_off_v = np.random.randint(1, 10, size=numProd) # uniform(1,10)
value_on_0 = np.ones(numCust) * self.v0_on
value_on_v = np.random.randint(1, 10, size=(numProd, numCust)) # uniform(1,10)
elif attV_Type.lower() == 'ctn':
value_off_0 = self.v0_off # np.random.rand() * self.v0_off
value_off_v = np.random.rand(numProd) * 9 + 1 # uniform(1,10)
value_on_0 = np.ones(numCust) * self.v0_on
value_on_v = np.random.rand(numProd, numCust) * 9 + 1 # uniform(1,10)
elif attV_Type.lower() == 'sparse':
k_off = int(self.utilitySparsity_off * numProd)
k_on = int(self.utilitySparsity_on * numProd)
value_off_0 = self.v0_off # np.random.rand() * self.v0_off
value_off_v = np.zeros(numProd)
loc = list(np.random.permutation(numProd))
value_off_v[loc[:k_off]] = np.random.rand(k_off)
value_on_0 = np.ones(numCust)* self.v0_on
value_on_v = np.zeros((numProd, numCust)) + np.eye(numProd, numCust)
for col in range(numCust):
loc = list(np.random.permutation(numProd))
if (col < numProd) :
loc.remove(col)
if ( k_on == numProd):
k_on = k_on-1
value_on_v[loc[:k_on], col] = np.random.rand(1, k_on)
elif attV_Type == 'COR_N':
r_off = self.r_off
r_on = self.r_on
a_off = np.random.uniform()
elif attV_Type.lower() == 'cap':
# CAP: CustomizedAssortment Problem;
# El Housni O, Topaloglu H. Joint assortment optimization and customization under a mixture of multinomial logit models: On the value of personalized assortments[J]. Operations Research, 2023, 71(4): 1197-1215.
value_off_0 = 1.0
value_off_v = abs(np.random.normal(size=(numProd)))
B = np.random.rand(numProd, numCust) >=0.5
X = abs(np.random.normal(size=(numProd, numCust)))
value_on_0 = np.ones(numCust)* self.v0_on
value_on_v = B * X
else:
value_off_0 = 1
value_off_v = np.array([1, 3, 7])
value_on_0 = np.array([1])
value_on_v = np.array([[1],
[3],
[5]])
(numProd, numCust) = value_on_v.shape # numProd: number of products; numCust number of kinds of online-customer
# rounding input
value_off_0 = value_off_0
value_off_v = value_off_v.round(3)
value_on_0 = value_on_0.round(3)
value_on_v = value_on_v.round(3)
self.value_off_0 = value_off_0
self.value_off_v = value_off_v
self.value_on_0 = value_on_0
self.value_on_v = value_on_v
self.numProd = numProd
self.numCust = numCust
self.I = list(range(numProd)) # index set of products
self.J = list(range(numCust)) # index set of online-customer type
self.prod_cust = list(itertools.product(self.I, self.J))
def generate_revenue_data(self,
numProd,
numCust,
revenue_Type,
vip_group_number = 2,
revenue_range = [10,20],
disc_range = [0.9,1]):
"""generate revenue data"""
if revenue_Type.lower() == 'int':
r_off = np.random.randint(1, 10, size=(numProd, 1)) # uniform(1,10)
r_on_temp = np.random.randint(1, 10, size=(numProd, 1)) # uniform(1,10)
r_on = np.repeat(r_on_temp, numCust, axis=1) # online segments have the same revenue
elif revenue_Type.lower() == 'ctn':
r_off = np.random.rand(numProd,1) * 9 + 1 # uniform(1,10)
r_on_temp = np.random.rand(numProd,1)*9 + 1 # uniform(1,10)
r_on = np.repeat(r_on_temp, numCust, axis=1) # online segments have the same revenue
elif revenue_Type.lower() == 'vip':
r_off = np.random.uniform(revenue_range[0], revenue_range[1], size=(numProd,1))
if vip_group_number <= 0 or vip_group_number >= numCust:
vip_group_number = numCust
r_on_VipGroup = r_off * np.random.uniform(disc_range[0], disc_range[1],size=(numProd,vip_group_number))
r_on = r_on_VipGroup
elif vip_group_number == 1:
r_on = np.repeat(r_off, numCust, axis=1)
else:
numEachGroup = numCust // vip_group_number
r_on_VipGroup = r_off * np.random.uniform(disc_range[0], disc_range[1],size=(numProd,vip_group_number-1))
r_on_regularGroup = np.repeat(r_off, numCust-r_on_VipGroup.shape[1], axis=1)
r_on = np.hstack([r_on_regularGroup, r_on_VipGroup])
elif revenue_Type.lower() == 'cor_p':
value_off_0 = self.value_off_0
value_off_v = self.value_off_v
value_on_0 = self.value_on_0
value_on_v = self.value_on_v
r_off = value_off_v * (np.random.uniform(10, 15, size=value_off_v.shape))
r_on = value_on_v * (np.random.uniform(10, 15, size=value_on_v.shape))
elif revenue_Type.lower() == 'cor_n':
value_off_0 = self.value_off_0
value_off_v = self.value_off_v
value_on_0 = self.value_on_0
value_on_v = self.value_on_v
r_off = 1/(value_off_v + 0.1) * (np.random.uniform(10, 15, size=value_off_v.shape))
r_on = 1/(value_on_v + 0.1) * (np.random.uniform(10, 15, size=value_on_v.shape))
elif revenue_Type.lower() == 'cap':
r_off = np.random.exponential(1, size=(numProd,1))
r_on = np.repeat(r_off, numCust, axis=1)
# r_on = np.random.exponential(1, size=(numProd, numCust))
else:
r_off = np.array([6,4,3])
r_on = np.array([[4],
[6],
[5]])
# rounding input
r_off = r_off.round(3)
r_on = r_on.round(3)
self.r_off = r_off
self.r_on = r_on
def generate_extraCstr_data(self, numProd, numCust, exist=False):
"""generate data with control parameters"""
# self.ExtraConstrList = []
if self.luce == 1:
self.ExtraConstrList.append("Luce")
if self.kappaOff < 1:
self.ExtraConstrList.append("CardiOff")
if self.kappaOn < 1:
self.ExtraConstrList.append("CardiOn")
if self.knapsackOff > 0:
self.ExtraConstrList.append("KnapsackOff")
if self.knapsackOn > 0:
self.ExtraConstrList.append("KnapsackOn")
self.ExtraConstrList = list(set(self.ExtraConstrList))
self.probType = self.attV_Type + self.revenue_Type + ''.join(self.ExtraConstrList)
if exist == True:
Ex_Cstr_Dict = copy.deepcopy(self.Ex_Cstr_Dict)
else:
Ex_Cstr_Dict = dict()
for ex_c_name in self.ExtraConstrList:
cstr_temp=''
if ex_c_name.lower() == 'cardioff':
columns = pd.MultiIndex.from_arrays([['CardiOff'], ['capacity']])
cstr_temp = pd.DataFrame([int(self.kappaOff * numProd)], columns=columns)
# cstr_temp = pd.Series([int(self.kappaOff * numProd)], name='CardiOff')
if ex_c_name.lower() == 'cardion':
columns = pd.MultiIndex.from_arrays([['CardiOn'], ['capacity']])
k_online= np.array([int(self.kappaOn * numProd)]*self.numCust)
cstr_temp = pd.DataFrame(k_online, index=['on{}'.format(j) for j in self.J], columns=columns)
# cstr_temp = pd.Series(k_online, index=['on{}'.format(j) for j in self.J], name='CardiOn')
if ex_c_name.lower() == 'knapsackoff':
columns_weight = pd.MultiIndex.from_product([['KnapsackOff'], ['weight{}'.format(i) for i in range(numProd)]])
columns_space = pd.MultiIndex.from_arrays([['KnapsackOff'], ['space']])
columns = columns_weight.append(columns_space)
weight = np.random.randint(1,10, size=(1, numProd))
space_ratio = 0.2
cstr_temp = pd.DataFrame(columns=columns)
cstr_temp.loc[:, columns_weight] = weight
cstr_temp.loc[:, columns_space] = space_ratio
if 'luce' in ex_c_name.lower():
luceType = self.luceType
if luceType == 'Tree':
# at lest 2 nodes were involved in each segements for 'luce' constraints
ratio = self.luceTree_nodeRatio # 0.25, 0.75
involved_nodes_num = int(ratio * self.utilitySparsity_on * numProd)+1
involved_nodes_num = max(2, involved_nodes_num)
nonzeros = np.nonzero(self.value_on_v)
loc = []
for i in range(np.count_nonzero(self.value_on_v)):
loc.append((nonzeros[0][i], nonzeros[1][i]) )
nonzeros = grb.tuplelist(loc)
columns = pd.MultiIndex.from_product([['on{}'.format(j) for j in range(numCust)], ['prodPerturb', 'row', 'col']])
ADJ_matrix = pd.DataFrame(columns=columns)
for j in range(numCust):
nonzeroProd = nonzeros.select('*',j)
nonzeroProd = [t[0] for t in nonzeroProd]
n1 = len(nonzeroProd)//3
v_r = self.value_on_v[:,j]/self.r_on[:,j]
# perturb_prod = np.argsort(-v_r)[:involved_nodes_num] # descend order, first k large
perturb_prod = np.argsort(v_r)[:involved_nodes_num] # ascend order, first k small
if len(perturb_prod) <= numProd:
pass
adj_matrix = tf.generate_random_trees(involved_nodes_num)
row, col = np.where(adj_matrix)
row = perturb_prod[row]
col = perturb_prod[col]
adj_on_j = pd.concat([pd.Series(perturb_prod), pd.Series(row), pd.Series(col)],
axis=1, keys=['prodPerturb','row','col'])
ADJ_matrix['on{}'.format(j)] = adj_on_j
# ADJ_matrix[('on{}'.format(j), 'prodPerturb')] = pd.Series(perturb_prod)
# ADJ_matrix[('on{}'.format(j), 'row')] = pd.Series(row)
# ADJ_matrix[('on{}'.format(j), 'col')] = pd.Series(col)
cstr_temp = ADJ_matrix
self.luce_info = tf.get_luceInfo(ADJ_matrix)
if luceType == 'GroupPair':
nonzeros = np.nonzero(self.value_on_v)
loc = []
for i in range(np.count_nonzero(self.value_on_v)):
loc.append((nonzeros[0][i], nonzeros[1][i]) )
nonzeros = grb.tuplelist(loc)
columns = pd.MultiIndex.from_product([['on{}'.format(j) for j in range(numCust)], ['prodPerturb', 'row', 'col']])
ADJ_matrix = pd.DataFrame(columns=columns)
for j in range(numCust):
nonzeroProd = nonzeros.select('*',j)
nonzeroProd = [t[0] for t in nonzeroProd]
n1 = len(nonzeroProd)//3
v_r = self.value_on_v[:,j]/self.r_on[:,j]
perturb_prod = np.argsort(-v_r) # descend order, first k large
# perturb_prod = np.argsort(v_r) # ascend order, first k small
perturb_prod = np.random.choice(nonzeroProd, len(nonzeroProd), replace=False).astype(int)
if len(perturb_prod) <= numProd:
pass
k12=k23=k13 = self.luceGroup_nodeRatio
p12_1 = np.random.choice(perturb_prod[:n1], int(n1*k12) )
p12_2 = np.random.choice(perturb_prod[n1:-n1], int(n1*k12) )
p23_2 = np.random.choice(perturb_prod[n1:-n1], int(n1*k23) )
p23_3 = np.random.choice(perturb_prod[-n1:], int(n1*k23) )
p13_1 = np.random.choice(perturb_prod[:n1], int(n1*k13) )
p13_3 = np.random.choice(perturb_prod[-n1:], int(n1*k13) )
row = np.hstack([p12_1, p23_2, p13_1])
col = np.hstack([p12_2, p23_3, p13_3])
adj_on_j = pd.concat([pd.Series(perturb_prod), pd.Series(row), pd.Series(col)],
axis=1, keys=['prodPerturb','row','col'])
ADJ_matrix['on{}'.format(j)] = adj_on_j
cstr_temp = ADJ_matrix
self.luce_info = tf.get_luceInfo(ADJ_matrix)
Ex_Cstr_Dict[ex_c_name] = cstr_temp
self.Ex_Cstr_Dict = Ex_Cstr_Dict
def update(self):
self.value_on_0 = np.ones(self.numCust) * self.v0_on
def write_data_to_txt(self, filename):
"""
write data to txt fle
filename: the file name of .txt to be written
BASIC PARAMETERS:
DETAILED PARAMETERS: utilities vector, prices vector
EXTRA CONSTRAINTS: graph's adjacency matrix for the 2SLM
"""
with open(filename, 'w') as file:
# Write probSetting_info
file.write("BASIC PARAMETERS\n")
file.write("numProd:\n" + str(self.numProd) + "\n")
file.write("numCust:\n" + str(self.numCust) + "\n")
file.write("arriveRatio_off:\n" + str(self.arriveRatio_off) + "\n")
file.write("v0_off:\n" + str(self.v0_off) + "\n")
file.write("v0_on:\n" + str(self.v0_on) + "\n")
file.write("luce:\n" + str(self.luce) + "\n")
file.write("kappaOff:\n" + str(self.kappaOff) + "\n")
file.write("kappaOn:\n" + str(self.kappaOn) + "\n")
file.write("seprate_tol:\n" + str(self.separate_tol) + "\n")
file.write("DETAILED PARAMETERS\n")
file.write("V_off_0:\n" + str(self.v0_off) + "\n")
file.write("V_off:\n" + " ".join(map(str, self.value_off_v)) + "\n")
file.write("V_on_0:\n" + " ".join(map(str, self.value_on_0)) + "\n")
file.write("V_on:\n")
for row in self.value_on_v:
file.write(" ".join(map(str, row)) + "\n")
file.write("R_off:\n" + " ".join(map(str, self.r_off[:, 0])) + "\n")
file.write("R_on:\n")
for row in self.r_on:
file.write(" ".join(map(str, row)) + "\n")
# def read_data_from_txt(self, filename):
# """
# to be done
# """
# # None
#%% model operations: define variables, add constraints
def define_varaiables(data, option, model, xType='C', xBoth=0):
"""
Creat variables for "model".
if xType='C', creat x_off as continuous variables, else as Binary variables.
if xBoth=1, creat both x_off and x_on, else onle x_off.
"""
# decompress data
value_off_0 = data.value_off_0
value_off_v = data.value_off_v
value_on_0 = data.value_on_0
value_on_v = data.value_on_v
r_off = data.r_off
r_on = data.r_on
I = data.I
J = data.J
prod_cust = data.prod_cust
numProd = data.numProd
numCust = data.numCust
if (data.kappaOff < 1) & ('CardiOff' in data.ExtraConstrList):
koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0].values[0]
else:
koff = data.numProd
if (data.kappaOn < 1) & ('CardiOn' in data.ExtraConstrList):
kon = data.Ex_Cstr_Dict['CardiOn'].iloc[:,0].values
else:
kon = np.repeat(numProd, numCust)
kon = np.array([min(int(koff), k) for k in kon])
# define variables
# improved-2
y_off_0_l = 1/(value_off_0+sum(np.sort(value_off_v)[-koff:]))
y_off_0_u = 1/(value_off_0)
y_on_0_l = 1/(value_on_0+[sum(np.sort(value_on_v,axis=0)[-kon[i]:,i]) for i in range(numCust)])
y_on_0_u = 1/(value_on_0)
# improved-1
y_off_0_l = 1/(value_off_0+sum(value_off_v))
y_off_0_u = 1/(value_off_0)
y_on_0_l = 1/(value_on_0+sum(value_on_v))
y_on_0_u = 1/(value_on_0)
x_off = model.addVars(I, lb=0, ub=1, vtype=xType, name='x_off')
y_off_0 = model.addVar(lb=y_off_0_l, ub=y_off_0_u, name='y_off_0')
y_off_y = model.addVars(I, lb=0, ub=y_off_0_u, name='y_off_y')
y_on_0 = model.addVars(J, lb=y_on_0_l, ub=y_on_0_u, name='y_on_0')
y_on_y = model.addVars(prod_cust, lb=0, ub=np.tile(y_on_0_u, (numProd,1)), name='y_on_y')
model._x_off = x_off
model._y_off_0 = y_off_0
model._y_off_y = y_off_y
model._y_on_0 = y_on_0
model._y_on_y = y_on_y
model._y_off_0_l = y_off_0_l
model._y_off_0_u = y_off_0_u
model._y_on_0_l = y_on_0_l
model._y_on_0_u = y_on_0_u
if option.MMNL == 1:
model._x_on = x_off
elif xBoth == 1:
x_on = model.addVars(prod_cust, lb=0, ub=1, vtype=xType, name='x_on')
model._x_on = x_on
return model
def define_w(data, option, model, w=[0,0]):
"""
Creat w variables for "model".
w is list with length 2, indicating whether create variables for w_off and w_on
"""
# decompress data
value_off_0 = data.value_off_0
value_off_v = data.value_off_v
value_on_0 = data.value_on_0
value_on_v = data.value_on_v
if w[0] == 1:
w_off = model.addVar(lb=value_off_0, name='w_off')
# w_off = model.addVar(lb=value_off_0, ub=value_off_0+sum(value_off_v),name='w_off')
model._w_off = w_off
if w[1] == 1:
w_on = model.addVars(data.J, lb=value_on_0, name='w_on')
# w_on = model.addVars(data.J, lb=value_on_0, ub=value_on_0+value_on_v.sum(axis=0), name='w_on')
model._w_on = w_on
return model
def add_constraints(data, option, model, cstrName, PI_ineq=[0, 0]):
"""
Creat constraints for "model".
cstrName: constraint name to be created
TODO: clean this and delete PI_ineq and its depedencies
"""
# decompress data
value_off_0 = data.value_off_0
value_off_v = data.value_off_v
value_on_0 = data.value_on_0
value_on_v = data.value_on_v
r_off = data.r_off
r_on = data.r_on
I = data.I
J = data.J
prod_cust = data.prod_cust
numProd = data.numProd
numCust = data.numCust
#
if cstrName == 'prob_hyperplane':
PiMap = lambda y0, y, u0, u: y0 * u0 + sum(y[i] * u[i] for i in range(len(y)))
Pi_off_Expr = PiMap(model._y_off_0,
model._y_off_y,
value_off_0,
value_off_v)
Pi_on_Expr = lambda j: PiMap(model._y_on_0[j],
model._y_on_y.select('*', j),
value_on_0[j],
value_on_v[:, j])
if PI_ineq[0] == 0:
HP_off = model.addConstr( Pi_off_Expr == 1, name='HP_off')
elif PI_ineq[0] == 1:
HP_off = model.addConstr( Pi_off_Expr >= 1, name='HP_off')
elif PI_ineq[0] == -1:
HP_off = model.addConstr( Pi_off_Expr <= 1, name='HP_off')
if PI_ineq[1] == 0:
HP_on = model.addConstrs((Pi_on_Expr(j) == 1 for j in J), name='HP_on')
elif PI_ineq[1] == 1:
HP_on = model.addConstrs((Pi_on_Expr(j) >= 1 for j in J), name='HP_on')
elif PI_ineq[1] == -1:
HP_on = model.addConstrs((Pi_on_Expr(j) <= 1 for j in J), name='HP_on')
model._HP_off = HP_off
model._HP_on = HP_on
if cstrName == 'MC_BL_off':
MC_offl1 = model.addConstrs(
(model._y_off_y[i] <= model._y_off_0_l*(model._x_off[i]-1) + model._y_off_0 for i in data.I),
name="MC_offl1")
MC_offl2 = model.addConstrs(
(model._y_off_y[i] <= model._y_off_0_u*model._x_off[i] for i in data.I),
name="MC_offl2")
MC_offg1 = model.addConstrs(
(model._y_off_y[i] >= model._y_off_0_u*(model._x_off[i]-1) + model._y_off_0 for i in data.I),
name="MC_offg1")
MC_offg2 = model.addConstrs(
(model._y_off_y[i] >= model._y_off_0_l*model._x_off[i] for i in data.I),
name="MC_offg2")
model._MC_offl1 = MC_offl1
model._MC_offl2 = MC_offl2
model._MC_offg1 = MC_offg1
model._MC_offg2 = MC_offg2
if option.MMNL == 1:
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_l[j]*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in data.J),
name="MC_onl1")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_u[j]*model._x_off[i]
for i in data.I for j in data.J),
name="MC_onl2")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= model._y_on_0_u[j]*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in data.J),
name="MC_ong1")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >= model._y_on_0_l[j]*model._x_off[i]
for i in data.I for j in data.J),
name="MC_ong2")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'MC_BL_link':
if option.MMNL == 0:
MC_linkl1 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_l[j]*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in data.J),
name="MC_linkl1")
MC_linkl2 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_u[j]*model._x_off[i]
for i in data.I for j in data.J),
name="MC_linkl2")
model._MC_linkl1 = MC_linkl1
model._MC_linkl2 = MC_linkl2
if cstrName == 'MC_BL_on':
if option.MMNL == 0:
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_l[j]*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in data.J),
name="MC_onl1")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= model._y_on_0_u[j]*model._x_on[i,j]
for i in data.I for j in data.J),
name="MC_onl2")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= model._y_on_0_u[j]*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in data.J),
name="MC_ong1")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >= model._y_on_0_l[j]*model._x_on[i,j]
for i in data.I for j in data.J),
name="MC_ong2")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'moMC_BL_off':
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_offl1 = model.addConstrs(
(model._y_off_y[i] <=
alp.compute(np.setdiff1d(I,i),-1)*(model._x_off[i]-1) + model._y_off_0
for i in data.I),
name="MC_off_cover_all-i")
MC_offl2 = model.addConstrs(
(model._y_off_y[i] <= alp.compute([i],-1)*model._x_off[i]
for i in data.I),
name="MC_off_cover_i")
MC_offg1 = model.addConstrs(
(model._y_off_y[i] >= alp.compute(I,-1)*model._x_off[i]
for i in data.I),
name="MC_off_pack_all")
MC_offg2 = model.addConstrs(
(model._y_off_y[i] >=
alp.compute([],-1)*(model._x_off[i]-1) + model._y_off_0
for i in data.I),
name="MC_off_pack_empty")
model._MC_offl1 = MC_offl1
model._MC_offl2 = MC_offl2
model._MC_offg1 = MC_offg1
model._MC_offg2 = MC_offg2
if option.MMNL == 1:
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.compute(np.setdiff1d(I,i),j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_cover_all-i")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_off[i]
for i in data.I for j in J),
name="MC_on_cover_i")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= alp.compute(I,j)*model._x_off[i]
for i in data.I for j in J),
name="MC_on_pack_all")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >=
alp.compute([],j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_pack_empty")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'moMC_BL_link':
if option.MMNL == 0:
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_linkl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.compute(np.setdiff1d(I,i),j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_link_cover_all-i")
MC_linkl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_off[i]
for i in data.I for j in J),
name="MC_link_cover_i")
model._MC_linkl1 = MC_linkl1
model._MC_linkl2 = MC_linkl2
if cstrName == 'moMC_BL_on':
if option.MMNL == 0:
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.compute(np.setdiff1d(I,i),j)*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_cover_all-i")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_on[i,j]
for i in data.I for j in J),
name="MC_on_cover_i")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= alp.compute(I,j)*model._x_on[i,j]
for i in data.I for j in J),
name="MC_on_pack_all")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >=
alp.compute([],j)*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_pack_empty")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'cardimoMC_BL_off':
# if (data.kappaOff < 1) & ("CardiOff" in data.ExtraConstrList):
# koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0].values[0]
koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0][0]
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_offl1 = model.addConstrs(
(model._y_off_y[i] <=
alp.cardi_x0(koff,I,i,-1)*(model._x_off[i]-1) + model._y_off_0
for i in data.I),
name="cardimoMC_off_cover_all-i")
MC_offl2 = model.addConstrs(
(model._y_off_y[i] <= alp.compute([i],-1)*model._x_off[i]
for i in data.I),
name="MC_off_cover_i")
MC_offg1 = model.addConstrs(
(model._y_off_y[i] >= alp.cardi_x1(koff,I,i,-1)*model._x_off[i]
for i in data.I),
name="cardimoMC_off_pack_all")
MC_offg2 = model.addConstrs(
(model._y_off_y[i] >=
alp.compute([],-1)*(model._x_off[i]-1) + model._y_off_0
for i in data.I),
name="MC_off_pack_empty")
model._MC_offl1 = MC_offl1
model._MC_offl2 = MC_offl2
model._MC_offg1 = MC_offg1
model._MC_offg2 = MC_offg2
if option.MMNL == 1:
if (data.kappaOff < 1) & ("CardiOff" in data.ExtraConstrList):
koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0].values[0]
else:
koff = data.numProd
if (data.kappaOn < 1) & ("CardiOn" in data.ExtraConstrList):
kon = data.Ex_Cstr_Dict['CardiOn'].iloc[:,0].values
else:
kon = np.repeat(numProd, numCust)
kon = np.array([min(int(koff), k) for k in kon])
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.cardi_x0(kon[j],I,i,j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="cardimoMC_on_cover_all-i")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_off[i]
for i in data.I for j in J),
name="MC_on_cover_i")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= alp.cardi_x1(kon[j],I,i,j)*model._x_off[i]
for i in data.I for j in J),
name="cardimoMC_on_pack_all")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >=
alp.compute([],j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_pack_empty")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'cardimoMC_BL_link':
if option.MMNL == 0:
if (data.kappaOff < 1) & ("CardiOff" in data.ExtraConstrList):
koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0].values[0]
else:
koff = data.numProd
if (data.kappaOn < 1) & ("CardiOn" in data.ExtraConstrList):
kon = data.Ex_Cstr_Dict['CardiOn'].iloc[:,0].values
else:
kon = np.repeat(numProd, numCust)
kon = np.array([min(int(koff), k) for k in kon])
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_linkl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.cardi_x0(kon[j],I,i,j)*(model._x_off[i]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="cardimoMC_link_cover_all-i")
MC_linkl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_off[i]
for i in data.I for j in J),
name="MC_link_cover_i")
model._MC_linkl1 = MC_linkl1
model._MC_linkl2 = MC_linkl2
if cstrName == 'cardimoMC_BL_on':
if option.MMNL == 0:
if (data.kappaOff < 1) & ("CardiOff" in data.ExtraConstrList):
koff = data.Ex_Cstr_Dict['CardiOff'].iloc[:,0].values[0]
else:
koff = data.numProd
if (data.kappaOn < 1) & ("CardiOn" in data.ExtraConstrList):
kon = data.Ex_Cstr_Dict['CardiOn'].iloc[:,0].values
else:
kon = np.repeat(numProd, numCust)
kon = np.array([min(int(koff), k) for k in kon])
alp = tf.Alpha(value_off_0, value_off_v, value_on_0, value_on_v)
MC_onl1 = model.addConstrs(
(model._y_on_y[i,j] <=
alp.cardi_x0(kon[j],I,i,j)*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="cardimoMC_on_cover_all-i")
MC_onl2 = model.addConstrs(
(model._y_on_y[i,j] <= alp.compute([i],j)*model._x_on[i,j]
for i in data.I for j in J),
name="MC_on_cover_i")
MC_ong1 = model.addConstrs(
(model._y_on_y[i,j] >= alp.cardi_x1(kon[j],I,i,j)*model._x_on[i,j]
for i in data.I for j in J),
name="cardimoMC_on_pack_all")
MC_ong2 = model.addConstrs(
(model._y_on_y[i,j] >=
alp.compute([],j)*(model._x_on[i,j]-1) + model._y_on_0[j]
for i in data.I for j in J),
name="MC_on_pack_empty")
model._MC_onl1 = MC_onl1
model._MC_onl2 = MC_onl2
model._MC_ong1 = MC_ong1
model._MC_ong2 = MC_ong2
if cstrName == 'x_link':
if option.MMNL == 0:
x_link = model.addConstrs(
(model._x_off[i] >= model._x_on[i,j]
for i in data.I for j in data.J),
name='x_link')
model._x_link = x_link
if cstrName == 'Conic_BL_off':
model = define_w(data, option, model, w=[1,0])
w_off = model._w_off
conic_off1 = model.addConstrs(
(model._x_off[i]**2 <= model._y_off_y[i] * w_off
for i in data.I),
name='soc_off1')
conic_off2 = model.addConstr(
1 <= w_off * model._y_off_0,
name='soc_off2')
w_off_eq = model.addConstr(
w_off == value_off_0
+ grb.quicksum(value_off_v[i] * model._x_off[i]
for i in data.I),
name='w_off_eq')
model._conic_off1 = conic_off1
model._conic_off2 = conic_off2
model._w_off_eq = w_off_eq
if option.MMNL == 1:
model = define_w(data, option, model, w=[0,1])
w_on = model._w_on
conic_on1 = model.addConstrs(
(model._x_off[i]**2 <= model._y_on_y[i,j] * w_on[j]
for i in data.I for j in data.J),
name='soc_on1')
conic_on2 = model.addConstrs(
(1 <= w_on[j] * model._y_on_0[j] for j in data.J) ,
name='soc_on2')
w_on_eq = model.addConstrs(
(w_on[j] == value_on_0[j]
+ grb.quicksum(value_on_v[i,j] * model._x_off[i]
for i in data.I)
for j in data.J),
name='w_on_eq')
model._conic_on1 = conic_on1
model._conic_on2 = conic_on2
model._w_on_eq = w_on_eq
if cstrName == 'Conic_BL_on':
if option.MMNL == 0:
model = define_w(data, option, model, w=[0,1])
w_on = model._w_on
conic_on1 = model.addConstrs(
(model._x_on[i,j]**2 <= model._y_on_y[i,j] * w_on[j]
for i in data.I for j in data.J),
name='soc_on1')
conic_on2 = model.addConstrs(
(1 <= w_on[j] * model._y_on_0[j] for j in data.J) ,
name='soc_on2')
w_on_eq = model.addConstrs(
(w_on[j] == value_on_0[j]
+ grb.quicksum(value_on_v[i,j] * model._x_on[i,j]
for i in data.I)
for j in data.J),
name='w_on_eq')
model._conic_on1 = conic_on1
model._conic_on2 = conic_on2
model._w_on_eq = w_on_eq
if cstrName == 'SOC_BL_off':
model = define_w(data, option, model, w=[1,0])
w_off = model._w_off
conic_off2 = model.addConstr(
1 <= w_off * model._y_off_0,
name='soc_off2')
w_off_eq = model.addConstr(
w_off == value_off_0
+ grb.quicksum(value_off_v[i] * model._x_off[i]
for i in data.I),
name='w_off_eq')
model._conic_off2 = conic_off2
model._w_off_eq = w_off_eq
if option.MMNL == 1:
model = define_w(data, option, model, w=[0,1])
w_on = model._w_on
conic_on2 = model.addConstrs(
(1 <= w_on[j] * model._y_on_0[j] for j in data.J) ,
name='soc_on2')
w_on_eq = model.addConstrs(
(w_on[j] == value_on_0[j]
+ grb.quicksum(value_on_v[i,j] * model._x_off[i]
for i in data.I)
for j in data.J),
name='w_on_eq')
model._conic_on2 = conic_on2
model._w_on_eq = w_on_eq
if cstrName == 'SOC_BL_link':
if option.MMNL == 0:
model = define_w(data, option, model, w=[0,1])
w_on = model._w_on