File size: 7,030 Bytes
a241478 ff58990 a241478 ff58990 a241478 ff58990 a241478 ff58990 a241478 ff58990 a241478 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | # -*- coding: utf-8 -*-
import json
import numpy as np
import warnings
def totuple(a):
try:
return tuple(totuple(i) for i in a)
except TypeError:
return a
def generate_json_file(time_horizon,
n_items,
n_machines,
initial_setup,
machine_production,
max_inventory_level,
initial_inventory_level,
holding_costs,
lost_sales_costs,
demand_distribution,
setup_costs,
setup_loss,
file_name):
file_dict = {
"time_horizon": time_horizon,
"n_items": n_items,
"n_machines": n_machines,
"initial_setup": initial_setup,
"machine_production": machine_production,
"max_inventory_level": max_inventory_level,
"initial_inventory": initial_inventory_level,
"holding_costs": holding_costs,
"lost_sales_costs": lost_sales_costs,
"demand_distribution": demand_distribution,
"setup_costs": setup_costs,
"setup_loss": setup_loss
}
# Serializing json
json_object = json.dumps(file_dict, indent = 4)
print(json_object)
with open(f"{file_name}.json", "w") as outfile:
outfile.write(json_object)
def generate_vector_min_bias(n_elements = 10,max_value = 10,min_value= 0 ,max_prob = 0.6):
if max_value == min_value:
return np.ones(n_elements,dtype = int)*max_value
else:
choices = np.arange(min_value,max_value+1)
probs = np.zeros(len(choices))
probs[0] = max_prob
if len(choices)<2:
leftprob = 1-max_prob
else:
leftprob = (1-max_prob)/(len(choices)-1)
probs[1:] = leftprob
return np.random.choice(choices,n_elements,p = probs.tolist())
def generate_matrix(n_lines,n_columns,max_value,min_value,max_prob):
final_matrix = []
for i in range(n_lines):
final_matrix.append(generate_vector_min_bias(n_columns,max_value,min_value,max_prob))
if n_lines <2:
return final_matrix[0].tolist()
else:
final_matrix = [l.tolist() for l in final_matrix]
return final_matrix
def check_all_zeros(matrix):
for i in range(len(matrix)):
if sum(np.array(matrix)[:,i]) == 0:
return warnings.warn("The generated matrix contains columns with zeros only")
time_horizon = 100
n_items = 25
n_machines = 15
#initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
machine_production = generate_matrix(n_machines,n_items,40,0,0.4)
check_all_zeros(machine_production)
max_inventory_level = generate_matrix(1,n_items,100,100,0.5)
initial_inventory_level = generate_matrix(1,n_items,50,0,0.05)
holding_costs = generate_matrix(1,n_items,0.01,0.01,0.5)
lost_sales_costs = generate_matrix(1,n_items,0.3,0.1,0.5)
demand_distribution = {
"name": "binomial",
"n": 20,
"p": 0.4
}
setup_costs = generate_matrix(n_machines,n_items,30,15,0.2)
setup_loss = generate_matrix(n_machines,n_items,3,1,0.7)
file_name = f'setting_{n_items}items_{n_machines}machines_t{time_horizon}_i{max_inventory_level[0]}'
generate_json_file( time_horizon,
n_items,
n_machines,
initial_setup,
machine_production,
max_inventory_level,
initial_inventory_level,
holding_costs,
lost_sales_costs,
demand_distribution,
setup_costs,
setup_loss,
file_name)
# time_horizon = 100
# n_items = 25
# n_machines = 10
# #initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
# initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
# machine_production = generate_matrix(n_machines,n_items,40,0,0.4)
# check_all_zeros(machine_production)
# max_inventory_level = generate_matrix(1,n_items,100,100,0.5)
# initial_inventory_level = generate_matrix(1,n_items,0,0,0.05)
# holding_costs = generate_matrix(1,n_items,0.01,0.01,0.5)
# lost_sales_costs = generate_matrix(1,n_items,0.2,0.2,0.5)
# demand_distribution = {
# "name": "binomial",
# "n": 20,
# "p": 0.4
# }
# setup_costs = generate_matrix(n_machines,n_items,2,1,0.2)
# setup_loss = generate_matrix(n_machines,n_items,1,1,0.7)
# file_name = f'setting_{n_items}items_{n_machines}machines'
# generate_json_file( time_horizon,
# n_items,
# n_machines,
# initial_setup,
# machine_production,
# max_inventory_level,
# initial_inventory_level,
# holding_costs,
# lost_sales_costs,
# demand_distribution,
# setup_costs,
# setup_loss,
# file_name)
# for m in [2,5,10]:
# for n in [10,20,30,40,50,60,70,80,90,100]:
# time_horizon = 5
# n_items = n
# n_machines = m
# #initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
# initial_setup = generate_matrix(1,n_machines,n_items,0,0.4)
# machine_production = generate_matrix(n_machines,n_items,4,0,0.2)
# check_all_zeros(machine_production)
# max_inventory_level = generate_matrix(1,n_items,10,10,0.5)
# initial_inventory_level = generate_matrix(1,n_items,0,0,0.05)
# holding_costs = generate_matrix(1,n_items,0.1,0.1,0.5)
# lost_sales_costs = generate_matrix(1,n_items,3,1,0.5)
# demand_distribution = {
# "name": "binomial",
# "n": 4,
# "p": 0.4
# }
# setup_costs = generate_matrix(n_machines,n_items,2,1,0.2)
# setup_loss = generate_matrix(n_machines,n_items,1,1,0.7)
# file_name = f'setting_{n_items}items_{n_machines}machines_t{time_horizon}'
# generate_json_file( time_horizon,
# n_items,
# n_machines,
# initial_setup,
# machine_production,
# max_inventory_level,
# initial_inventory_level,
# holding_costs,
# lost_sales_costs,
# demand_distribution,
# setup_costs,
# setup_loss,
# file_name) |