import itertools
import sys
import copy
import math
from src.load_input import load_input
from src.util import distance, find_closest_nonempty_warehouses
from src.output import Output
if len(sys.argv) != 3:
print("invalid number of params")
print("run as: ./script input_file.in output_file.out")
sys.exit(1)
world, initial_state = load_input(sys.argv[1])
# this is the function which is called anytime drone is idle and
# it decides what the drone will do next
#
# the idea behind this implementation is to find closest non-empty
# warehouse to drone which can deliver something for any order
# and find order which can be ideally fully
# delivered from this warehouse or order for which the drone can
# be fully loaded
def find_drone_action(state, d):
if len(state.warehouses) == 1:
# optimalization for scenario when there is just one warehouse
closest_warehouses = state.warehouses[:]
else:
closest_warehouses = find_closest_nonempty_warehouses(state, d)
#!/usr/bin/env python3
import itertools
import sys
import copy
from src.load_input import load_input
from src.util import distance
world, initial_state = load_input("inputs/busy_day.in")
def o_fulfilment_cost(o_fulfilment):
cost = 0
o, o_fulfilment = o_fulfilment
# give me all the warehouse IDs from which this fulfilment would
# get the product
w_ids = [o_item_fulfilment[1] for o_item_fulfilment in o_fulfilment]
for w_id in w_ids:
cost += 2 * distance(initial_state.warehouses[w_id].pos, o.pos)
return cost
def strategy_cost(strategy):
cost = 0
for oi, o_fulfilment in enumerate(strategy):
o = initial_state.open_orders[oi]
cost += o_fulfilment_cost((o, o_fulfilment))
