Spaces:
Sleeping
Sleeping
File size: 9,977 Bytes
e40294e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
from dataclasses import dataclass
from enum import Enum
from random import Random
from typing import List
from .domain import Product, Order, OrderItem, Trolley, TrolleyStep, OrderPickingSolution
from .warehouse import WarehouseLocation, Side, Column, Row, new_shelving_id, Shelving
# Configuration constants - matches Java timefold-quickstarts
TROLLEYS_COUNT = 5
BUCKET_COUNT = 4
BUCKET_CAPACITY = 60 * 40 * 20 # 48000 cm3
ORDERS_COUNT = 8
ORDER_ITEMS_SIZE_MINIMUM = 1
# Start location for all trolleys
START_LOCATION = WarehouseLocation(
shelving_id=new_shelving_id(Column.COL_A, Row.ROW_1),
side=Side.LEFT,
row=0
)
class ProductFamily(Enum):
GENERAL_FOOD = "GENERAL_FOOD"
FRESH_FOOD = "FRESH_FOOD"
MEET_AND_FISH = "MEET_AND_FISH"
FROZEN_PRODUCTS = "FROZEN_PRODUCTS"
FRUITS_AND_VEGETABLES = "FRUITS_AND_VEGETABLES"
HOUSE_CLEANING = "HOUSE_CLEANING"
DRINKS = "DRINKS"
SNACKS = "SNACKS"
PETS = "PETS"
@dataclass
class ProductTemplate:
"""Template for a product before location is assigned."""
id: str
name: str
volume: int # in cm3
family: ProductFamily
# Product templates without locations (locations are assigned randomly)
PRODUCT_TEMPLATES: List[ProductTemplate] = [
# GENERAL_FOOD
ProductTemplate("0", "Kelloggs Cornflakes", 30 * 12 * 35, ProductFamily.GENERAL_FOOD),
ProductTemplate("1", "Cream Crackers", 23 * 7 * 2, ProductFamily.GENERAL_FOOD),
ProductTemplate("2", "Tea Bags 240 packet", 2 * 6 * 15, ProductFamily.GENERAL_FOOD),
ProductTemplate("3", "Tomato Soup Can", 10 * 10 * 10, ProductFamily.GENERAL_FOOD),
ProductTemplate("4", "Baked Beans in Tomato Sauce", 10 * 10 * 10, ProductFamily.GENERAL_FOOD),
ProductTemplate("5", "Classic Mint Sauce", 8 * 10 * 8, ProductFamily.GENERAL_FOOD),
ProductTemplate("6", "Raspberry Conserve", 8 * 10 * 8, ProductFamily.GENERAL_FOOD),
ProductTemplate("7", "Orange Fine Shred Marmalade", 7 * 8 * 7, ProductFamily.GENERAL_FOOD),
# FRESH_FOOD
ProductTemplate("8", "Free Range Eggs 6 Pack", 15 * 10 * 8, ProductFamily.FRESH_FOOD),
ProductTemplate("9", "Mature Cheddar 400G", 10 * 9 * 5, ProductFamily.FRESH_FOOD),
ProductTemplate("10", "Butter Packet", 12 * 5 * 5, ProductFamily.FRESH_FOOD),
# FRUITS_AND_VEGETABLES
ProductTemplate("11", "Iceberg Lettuce Each", 2500, ProductFamily.FRUITS_AND_VEGETABLES),
ProductTemplate("12", "Carrots 1Kg", 1000, ProductFamily.FRUITS_AND_VEGETABLES),
ProductTemplate("13", "Organic Fair Trade Bananas 5 Pack", 1800, ProductFamily.FRUITS_AND_VEGETABLES),
ProductTemplate("14", "Gala Apple Minimum 5 Pack", 25 * 20 * 10, ProductFamily.FRUITS_AND_VEGETABLES),
ProductTemplate("15", "Orange Bag 3kg", 29 * 20 * 15, ProductFamily.FRUITS_AND_VEGETABLES),
# HOUSE_CLEANING
ProductTemplate("16", "Fairy Non Biological Laundry Liquid 4.55L", 5000, ProductFamily.HOUSE_CLEANING),
ProductTemplate("17", "Toilet Tissue 8 Roll White", 50 * 20 * 20, ProductFamily.HOUSE_CLEANING),
ProductTemplate("18", "Kitchen Roll 200 Sheets x 2", 30 * 30 * 15, ProductFamily.HOUSE_CLEANING),
ProductTemplate("19", "Stainless Steel Cleaner 500Ml", 500, ProductFamily.HOUSE_CLEANING),
ProductTemplate("20", "Antibacterial Surface Spray", 12 * 4 * 25, ProductFamily.HOUSE_CLEANING),
# MEET_AND_FISH
ProductTemplate("21", "Beef Lean Steak Mince 500g", 500, ProductFamily.MEET_AND_FISH),
ProductTemplate("22", "Smoked Salmon 120G", 150, ProductFamily.MEET_AND_FISH),
ProductTemplate("23", "Steak Burgers 454G", 450, ProductFamily.MEET_AND_FISH),
ProductTemplate("24", "Pork Cooked Ham 125G", 125, ProductFamily.MEET_AND_FISH),
ProductTemplate("25", "Chicken Breast Fillets 300G", 300, ProductFamily.MEET_AND_FISH),
# DRINKS
ProductTemplate("26", "6 Milk Bricks Pack", 22 * 16 * 21, ProductFamily.DRINKS),
ProductTemplate("27", "Milk Brick", 1232, ProductFamily.DRINKS),
ProductTemplate("28", "Skimmed Milk 2.5L", 2500, ProductFamily.DRINKS),
ProductTemplate("29", "3L Orange Juice", 3 * 1000, ProductFamily.DRINKS),
ProductTemplate("30", "Alcohol Free Beer 4 Pack", 30 * 15 * 30, ProductFamily.DRINKS),
ProductTemplate("31", "Pepsi Regular Bottle", 1000, ProductFamily.DRINKS),
ProductTemplate("32", "Pepsi Diet 6 x 330ml", 35 * 12 * 12, ProductFamily.DRINKS),
ProductTemplate("33", "Schweppes Lemonade 2L", 2000, ProductFamily.DRINKS),
ProductTemplate("34", "Coke Zero 8 x 330ml", 40 * 12 * 12, ProductFamily.DRINKS),
ProductTemplate("35", "Natural Mineral Water Still 6 X 1.5Ltr", 6 * 1500, ProductFamily.DRINKS),
# SNACKS
ProductTemplate("36", "Cocktail Crisps 6 Pack", 20 * 10 * 10, ProductFamily.SNACKS),
]
# Shelving assignments per product family
SHELVINGS_PER_FAMILY = {
ProductFamily.FRUITS_AND_VEGETABLES: [
new_shelving_id(Column.COL_A, Row.ROW_1),
new_shelving_id(Column.COL_A, Row.ROW_2),
],
ProductFamily.FRESH_FOOD: [
new_shelving_id(Column.COL_A, Row.ROW_3),
],
ProductFamily.MEET_AND_FISH: [
new_shelving_id(Column.COL_B, Row.ROW_2),
new_shelving_id(Column.COL_B, Row.ROW_3),
],
ProductFamily.FROZEN_PRODUCTS: [
new_shelving_id(Column.COL_B, Row.ROW_2),
new_shelving_id(Column.COL_B, Row.ROW_1),
],
ProductFamily.DRINKS: [
new_shelving_id(Column.COL_D, Row.ROW_1),
],
ProductFamily.SNACKS: [
new_shelving_id(Column.COL_D, Row.ROW_2),
],
ProductFamily.GENERAL_FOOD: [
new_shelving_id(Column.COL_B, Row.ROW_2),
new_shelving_id(Column.COL_C, Row.ROW_3),
new_shelving_id(Column.COL_D, Row.ROW_2),
new_shelving_id(Column.COL_D, Row.ROW_3),
],
ProductFamily.HOUSE_CLEANING: [
new_shelving_id(Column.COL_E, Row.ROW_2),
new_shelving_id(Column.COL_E, Row.ROW_1),
],
ProductFamily.PETS: [
new_shelving_id(Column.COL_E, Row.ROW_3),
],
}
def get_max_product_size() -> int:
"""Get the maximum product volume."""
return max(p.volume for p in PRODUCT_TEMPLATES)
def validate_bucket_capacity(bucket_capacity: int) -> None:
"""Ensure bucket capacity can hold the largest product."""
max_size = get_max_product_size()
if bucket_capacity < max_size:
raise ValueError(
f"The selected bucketCapacity: {bucket_capacity}, is lower than the "
f"maximum product size: {max_size}. Please use a higher value."
)
def build_products(random: Random) -> List[Product]:
"""Build products with random warehouse locations based on their family."""
products = []
for template in PRODUCT_TEMPLATES:
shelving_ids = SHELVINGS_PER_FAMILY[template.family]
shelving_id = random.choice(shelving_ids)
side = random.choice(list(Side))
row = random.randint(1, Shelving.ROWS_SIZE)
location = WarehouseLocation(
shelving_id=shelving_id,
side=side,
row=row
)
products.append(Product(
id=template.id,
name=template.name,
volume=template.volume,
location=location
))
return products
def build_trolleys(
count: int,
bucket_count: int,
bucket_capacity: int,
start_location: WarehouseLocation
) -> List[Trolley]:
"""Build trolleys at the start location."""
return [
Trolley(
id=str(i),
bucket_count=bucket_count,
bucket_capacity=bucket_capacity,
location=start_location
)
for i in range(1, count + 1)
]
def build_orders(count: int, products: List[Product], random: Random) -> List[Order]:
"""Build orders with random products - matches Java implementation."""
orders = []
for order_num in range(1, count + 1):
# Java: ORDER_ITEMS_SIZE_MINIMUM + random.nextInt(products.size() - ORDER_ITEMS_SIZE_MINIMUM)
order_items_size = ORDER_ITEMS_SIZE_MINIMUM + random.randint(0, len(products) - ORDER_ITEMS_SIZE_MINIMUM - 1)
order_items = []
order_product_ids = set()
order = Order(id=str(order_num), items=order_items)
item_num = 1
for _ in range(order_items_size):
product_index = random.randint(0, len(products) - 1)
product = products[product_index]
# Avoid duplicate products in the same order
if product.id not in order_product_ids:
order_items.append(OrderItem(
id=str(item_num),
order=order,
product=product
))
order_product_ids.add(product.id)
item_num += 1
orders.append(order)
return orders
def build_trolley_steps(orders: List[Order]) -> List[TrolleyStep]:
"""Build trolley steps from order items."""
steps = []
for order in orders:
for idx, item in enumerate(order.items):
steps.append(TrolleyStep(
id=f"{order.id}-{idx}",
order_item=item
))
return steps
def generate_demo_data() -> OrderPickingSolution:
"""Generate the complete demo data set."""
random = Random(37) # Fixed seed for reproducibility
validate_bucket_capacity(BUCKET_CAPACITY)
products = build_products(random)
trolleys = build_trolleys(TROLLEYS_COUNT, BUCKET_COUNT, BUCKET_CAPACITY, START_LOCATION)
orders = build_orders(ORDERS_COUNT, products, random)
trolley_steps = build_trolley_steps(orders)
# Pre-assign steps evenly across trolleys so we have paths to visualize immediately
# The solver will optimize the distribution
if trolleys:
for i, step in enumerate(trolley_steps):
trolley = trolleys[i % len(trolleys)]
trolley.steps.append(step)
step.trolley = trolley
return OrderPickingSolution(
trolleys=trolleys,
trolley_steps=trolley_steps
)
|