class NaiveOptimiser:
def __init__(self):
input_reader = InputReader()
self.state = input_reader.read(0)
self.output_writer = OutputWriter('naive')
self.num_rows = self.state['numRows']
self.num_cols = self.state['numCols']
self.num_drones = self.state['numDrones']
self.num_turns = self.state['numTurns']
self.max_payload = self.state['maxPayload']
self.num_product_types = self.state['numProductTypes']
self.product_weights = self.state['productWeights']
self.num_warehouses = self.state['numWarehouses']
# warehouse: liste auf dicts: x, y, items -> itemstock
self.warehouse_data = self.state['warehousesData']
# order data: liste auf dicts: x, y, numorders, items
self.order_data = self.state['orderData']
# get list of items
self.item_lists = numpy.zeros((len(self.order_data), len(self.product_weights)), dtype=int)
self.turns_used = numpy.zeros(self.num_drones)
self.drone_location = numpy.zeros((self.num_drones, 2))
for order_idx in numpy.arange(len(self.order_data)):
for item in self.order_data[order_idx]['items']:
self.item_lists[order_idx][item] += 1
for n in numpy.arange(self.num_drones):
self.drone_location[n] = [0, 0]
def optimise(self):
while True:
print self.turns_used[:20]
# Do smallest orders first
# list of num orders
num_orders = [order['numOrders'] for order in self.order_data]
idx_smallest_orders = num_orders.index(min(num_orders))
idx_order = idx_smallest_orders
print self.item_lists[idx_smallest_orders]
coordinates_smallest = (self.order_data[idx_order]['x'], self.order_data[idx_order]['y'])
items_order = self.order_data[idx_order]['items']
# get warehouses with items closest to destination that have at least one of the items stocked
smallest_dist = 10000
smallest_idx = -1
# get warehouses that stock suitable items
suitable_warehouses = []
for warehouse in self.warehouse_data:
warehouse_stock = warehouse['items']
for item in items_order:
if warehouse_stock[item] > 0:
suitable_warehouses.append(warehouse)
coordinates_warehouse = (0, 0)
# get nearest one to customer
for idx, warehouse in enumerate(suitable_warehouses):
coordinates_warehouse = (warehouse['x'], warehouse['y'])
dist = math.ceil(math.sqrt((coordinates_smallest[0] - coordinates_warehouse[0]) ** 2 + (
coordinates_smallest[1] - coordinates_warehouse[1]) ** 2))
if dist < smallest_dist:
smallest_dist = dist
smallest_idx = idx
cur_warehouse = self.warehouse_data[smallest_idx]
# assign next drone which hasn't done much so for
next_drone_idx = numpy.argmin(self.turns_used)
# which stock?
warehouse_stock = cur_warehouse['items']
for idx, item in enumerate(self.item_lists[idx_order]):
if item > 0:
print 'here'
if warehouse_stock[item] > 0:
# next drone fetch that stock!
# how many can it fetch?
num_possible = int(self.max_payload[next_drone_idx] * 1. / self.product_weights[item])
num_taken = min([num_possible, warehouse_stock[item]])
# calculate number of turns used
# add delivery
turn_count = smallest_dist + 1
load_dist = math.ceil(
numpy.linalg.norm(self.drone_location[next_drone_idx] - coordinates_warehouse))
turn_count += load_dist + 1
# we are over max turns!
if self.turns_used[next_drone_idx] + turn_count > self.num_turns:
self.end()
return
else:
self.turns_used[next_drone_idx] += turn_count
# fetch that
self.output_writer.writeLoad(next_drone_idx, smallest_idx, item, num_taken)
self.output_writer.writeDeliver(next_drone_idx, idx_order, item, num_taken)
# decrease warehouse stock
warehouse_stock[item] -= num_taken
# remove item from list
self.item_lists[idx_order][item] -= num_taken
# update drone location
self.drone_location[next_drone_idx] = coordinates_smallest
print 'here'
break
def end(self):
self.output_writer.endFile()
class NaiveOptimiser:
def __init__(self):
input_reader = InputReader()
self.state = input_reader.read(0)
self.output_writer = OutputWriter('naive')
self.num_rows = self.state['numRows']
self.num_cols = self.state['numCols']
self.num_drones = self.state['numDrones']
self.num_turns = self.state['numTurns']
self.max_payload = self.state['maxPayload']
self.num_product_types = self.state['numProductTypes']
self.product_weights = self.state['productWeights']
self.num_warehouses = self.state['numWarehouses']
# warehouse: liste auf dicts: x, y, items -> itemstock
self.warehouse_data = self.state['warehousesData']
# order data: liste auf dicts: x, y, numorders, items
self.order_data = self.state['orderData']
# get list of items
self.item_lists = numpy.zeros(
(len(self.order_data), len(self.product_weights)), dtype=int)
self.turns_used = numpy.zeros(self.num_drones)
self.drone_location = numpy.zeros((self.num_drones, 2))
for order_idx in numpy.arange(len(self.order_data)):
for item in self.order_data[order_idx]['items']:
self.item_lists[order_idx][item] += 1
for n in numpy.arange(self.num_drones):
self.drone_location[n] = [0, 0]
def optimise(self):
while True:
print self.turns_used[:20]
# Do smallest orders first
# list of num orders
num_orders = [order['numOrders'] for order in self.order_data]
idx_smallest_orders = num_orders.index(min(num_orders))
idx_order = idx_smallest_orders
print self.item_lists[idx_smallest_orders]
coordinates_smallest = (self.order_data[idx_order]['x'],
self.order_data[idx_order]['y'])
items_order = self.order_data[idx_order]['items']
# get warehouses with items closest to destination that have at least one of the items stocked
smallest_dist = 10000
smallest_idx = -1
# get warehouses that stock suitable items
suitable_warehouses = []
for warehouse in self.warehouse_data:
warehouse_stock = warehouse['items']
for item in items_order:
if warehouse_stock[item] > 0:
suitable_warehouses.append(warehouse)
coordinates_warehouse = (0, 0)
# get nearest one to customer
for idx, warehouse in enumerate(suitable_warehouses):
coordinates_warehouse = (warehouse['x'], warehouse['y'])
dist = math.ceil(
math.sqrt((coordinates_smallest[0] -
coordinates_warehouse[0])**2 +
(coordinates_smallest[1] -
coordinates_warehouse[1])**2))
if dist < smallest_dist:
smallest_dist = dist
smallest_idx = idx
cur_warehouse = self.warehouse_data[smallest_idx]
# assign next drone which hasn't done much so for
next_drone_idx = numpy.argmin(self.turns_used)
# which stock?
warehouse_stock = cur_warehouse['items']
for idx, item in enumerate(self.item_lists[idx_order]):
if item > 0:
print 'here'
if warehouse_stock[item] > 0:
# next drone fetch that stock!
# how many can it fetch?
num_possible = int(self.max_payload[next_drone_idx] *
1. / self.product_weights[item])
num_taken = min([num_possible, warehouse_stock[item]])
# calculate number of turns used
# add delivery
turn_count = smallest_dist + 1
load_dist = math.ceil(
numpy.linalg.norm(
self.drone_location[next_drone_idx] -
coordinates_warehouse))
turn_count += load_dist + 1
# we are over max turns!
if self.turns_used[
next_drone_idx] + turn_count > self.num_turns:
self.end()
return
else:
self.turns_used[next_drone_idx] += turn_count
# fetch that
self.output_writer.writeLoad(next_drone_idx,
smallest_idx, item,
num_taken)
self.output_writer.writeDeliver(
next_drone_idx, idx_order, item, num_taken)
# decrease warehouse stock
warehouse_stock[item] -= num_taken
# remove item from list
self.item_lists[idx_order][item] -= num_taken
# update drone location
self.drone_location[
next_drone_idx] = coordinates_smallest
print 'here'
break
def end(self):
self.output_writer.endFile()
