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Commit 6267c90

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fixes initial solution generation, project structure modified, the start of the bee colony dev
1 parent 7ddbef7 commit 6267c90

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7 files changed

+444
-204
lines changed

7 files changed

+444
-204
lines changed

‎VehicleRoutingProblem.ipynb‎

Lines changed: 164 additions & 53 deletions
Large diffs are not rendered by default.

‎algorithm/bee_colony.py‎

Lines changed: 23 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,23 @@
1+
import random
2+
import numpy as np
3+
from copy import copy
4+
from utils import tools
5+
from random import randint
6+
from itertools import combinations
7+
from tqdm import tqdm, tqdm_notebook
8+
9+
from utils import tools
10+
from algorithm.base import Algorithm
11+
12+
13+
class BeeColony(Algorithm):
14+
15+
def __init__(self, problem):
16+
self.problem = problem
17+
18+
@property
19+
def name(self):
20+
return 'ArtificalBeeColony'
21+
22+
def solve(self):
23+
pass

‎algorithm/local_search.py‎

Lines changed: 8 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -1,11 +1,11 @@
1+
import random
12
import numpy as np
23
from copy import copy
3-
from utils import tools
44
from random import randint
55
from itertools import combinations
66
from tqdm import tqdm, tqdm_notebook
77

8-
from utils import tools
8+
from utils import tools, common
99
from algorithm.base import Algorithm
1010

1111

@@ -25,21 +25,19 @@ def set_params(self, solution, n_iter, **params):
2525
self.params = params
2626

2727

28-
def solve(self, verbose=False, k=2):
29-
self.cur_cost = tools.compute_solution(self.problem, self.solution)
28+
def solve(self, verbose=False, k=3):
29+
self.cur_cost = common.compute_solution(self.problem, self.solution)
3030
if verbose:
3131
print('Start cost: {}'.format(self.cur_cost))
3232
for comb in tqdm(combinations(np.arange(1, len(self.solution)-k,
33-
dtype=np.int32),
34-
2)
35-
):
33+
dtype=np.int32), 2)):
3634
tmp_sol = copy(self.solution)
3735
i, j = comb
38-
if abs(i-j) > 1:
36+
if abs(i-j) > k:
3937
tmp_sol[i:i+k], tmp_sol[j:j+k] = copy(tmp_sol[j:j+k]), copy(tmp_sol[i:i+k])
4038
else:
4139
continue
42-
cost = tools.compute_solution(self.problem, tmp_sol)
40+
cost = common.compute_solution(self.problem, tmp_sol)
4341
if self.cur_cost > cost:
4442
self.cur_cost = cost
4543
self.solution = tmp_sol
@@ -48,4 +46,5 @@ def solve(self, verbose=False, k=2):
4846
break
4947
self.n_iter -= 1
5048

49+
5150
return self.solution, self.cur_cost

‎algorithm/neighbor_operator.py‎

Lines changed: 28 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,28 @@
1+
import random
2+
import numpy as np
3+
from copy import copy
4+
from utils import tools
5+
from random import randint
6+
from itertools import combinations
7+
8+
9+
class NeighborOperator:
10+
11+
def __init__(self):
12+
operators =
13+
{0: self.random_swap}
14+
pass
15+
16+
@staticmethod
17+
def random_swap(_solution, patience = 100):
18+
solution = copy(_solution)
19+
sol_len = len(solution)
20+
while patience > 0:
21+
i, j = random.sample(np.arange(1, sol_len-1), 2)
22+
if i != j and solution[i] != 0 and solution[j] != 0:
23+
solution[i],
24+
solution[j] = copy(solution[j]),
25+
copy(solution[i])
26+
break
27+
patience -= 1
28+
return solution

‎utils/common.py‎

Lines changed: 176 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,176 @@
1+
import random
2+
import numpy as np
3+
import pandas as pd
4+
5+
from copy import copy
6+
from datetime import datetime
7+
from itertools import combinations
8+
from tqdm import tqdm, tqdm_notebook
9+
10+
11+
def generate_solution(problem,
12+
patience=10,
13+
verbose=False) -> np.ndarray:
14+
15+
dists = problem['dists']
16+
demands = problem['demands']
17+
i_loc = [i for i in range(1, problem['n_locations'])]
18+
routes = [[0] for _ in range(problem['n_trucks'])]
19+
20+
for i in range(len(i_loc)):
21+
route_dists = []
22+
random_loc = random.choice(i_loc)
23+
for route in routes:
24+
dist_to_loc = dists[route[-1]][random_loc]
25+
route_demand = sum([demands[i] for i in route])
26+
alpha = (route_demand + 0.5*dist_to_loc)
27+
route_dists.append(alpha)
28+
routes[np.argmin(route_dists)].append(random_loc)
29+
i_loc.remove(random_loc)
30+
31+
solution = [loc for route in routes for loc in route]
32+
solution.append(0)
33+
solution = np.array(solution, dtype=np.int32)
34+
35+
return solution
36+
37+
38+
def compute_solution(problem, solution) -> np.float32:
39+
# compute solution cost
40+
# cost = Sum DijXij
41+
n = problem['n_locations']
42+
x = np.zeros((n, n), dtype=np.int32)
43+
for i, loc in enumerate(solution[:-1]):
44+
x[solution[i], solution[i+1]] = 1
45+
cost = problem['dists'][x == 1].sum()
46+
return cost
47+
48+
49+
def check_solution(problem,
50+
solution,
51+
x=None,
52+
verbose=False) -> bool:
53+
# sanity check No1
54+
# len(solution) == n_locations + n_trucks
55+
sol_len = len(solution)
56+
plan_len = problem['n_trucks'] + problem['n_locations']
57+
if not sol_len == plan_len:
58+
if verbose:
59+
print('Solution len {} but should be {}' \
60+
.format(sol_len, plan_len))
61+
return False
62+
63+
# sanity check No2
64+
# The end and the start of the solution should be depot
65+
depots = list(filter(lambda i: solution[i]==0, range(sol_len)))
66+
if depots[0] != 0 or depots[-1] != sol_len-1:
67+
if verbose:
68+
print('The end and the start of the solution should be depots')
69+
print(depots)
70+
return False
71+
72+
# sanity check No3
73+
# there shouldn`t be several depots in a row for example [0, 0,.. ]
74+
for i in range(len(depots)-1):
75+
if depots[i+1] - depots[i] <= 1:
76+
if verbose:
77+
print('Several depots in a row: {}'.format(depots))
78+
return False
79+
80+
if not isinstance(x, np.ndarray):
81+
n = problem['n_locations']
82+
x = np.zeros((n, n), dtype=np.int32)
83+
for i, loc in enumerate(solution[:-1]):
84+
x[solution[i], solution[i+1]] = 1
85+
86+
# cruteria check No1
87+
# Sum Xi0 = M For all i in V
88+
# Sum X0j = M For all j in V and
89+
# where M is the number of trucks
90+
if not check_M_criteria(problem,
91+
solution,
92+
x=x,
93+
verbose=verbose):
94+
return False
95+
96+
# criteria check No2
97+
# Sum Xij = 1 For all j in V\{0} and
98+
# Sum Xij = 1 For all i in V\{0}
99+
if not check_One_criteria(problem,
100+
solution,
101+
x=x,
102+
verbose=verbose):
103+
return False
104+
105+
# criteria check No3
106+
# route demand <= truck capacity
107+
if not check_capacity_criteria(problem,
108+
solution,
109+
verbose=verbose):
110+
return False
111+
112+
return True
113+
114+
115+
def check_One_criteria(problem,
116+
solution,
117+
x=None,
118+
verbose=False) -> bool:
119+
if not ((x.sum(axis=1)[1: ].sum() == problem['n_locations'] - 1) and
120+
(x.sum(axis=0)[1: ].sum() == problem['n_locations'] - 1)):
121+
if verbose:
122+
print('Sum Xij for j = ', x.sum(axis=1)[1: ])
123+
print('Sum Xij for j = ', x.sum(axis=0)[1: ])
124+
return False
125+
126+
return True
127+
128+
129+
def check_M_criteria(problem,
130+
solution,
131+
x=None,
132+
verbose=False) -> bool:
133+
134+
if not isinstance(x, np.ndarray):
135+
n = problem['n_locations']
136+
x = np.zeros((n, n), dtype=np.int32)
137+
for i, loc in enumerate(solution[:-1]):
138+
x[solution[i], solution[i+1]] = 1
139+
140+
if not ((x[0, :].sum() == problem['n_trucks']) and
141+
x[:, 0].sum() == problem['n_trucks']):
142+
if verbose:
143+
print('n_trucks =', problem['n_trucks'])
144+
print('Sum Xi0 = ', x[:, 0].sum())
145+
print('Sum X0j = ', x[0, :].sum())
146+
print(solution)
147+
return False
148+
149+
return True
150+
151+
152+
def check_capacity_criteria(problem,
153+
solution,
154+
verbose=False) -> bool:
155+
capacity = problem['capacity']
156+
routes_demand = get_routes_demand(problem, solution)
157+
for route_demand in routes_demand:
158+
if route_demand > capacity:
159+
if verbose:
160+
print('Route demand {} exeeds capacity {}' \
161+
.format(route_demand, capacity))
162+
print('Route ', routes_demand)
163+
return False
164+
return True
165+
166+
167+
def get_routes_demand(problem, solution):
168+
sol_len = len(solution)
169+
demands = problem['demands']
170+
depots = list(filter(lambda i: solution[i]==0, range(sol_len)))
171+
routes = []
172+
for i, d in enumerate(depots[:-1]):
173+
route = solution[depots[i]+1:depots[i+1]]
174+
route_demand = np.sum([demands[place] for place in route])
175+
routes.append(route_demand)
176+
return routes

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