def mkp_mst(D, W):
best_approx = DeliveryPath()
for count in range(1, len(D)):
continue_ = False
for haul in combinations(D, count):
shortest_path = DeliveryPath(haul)
if shortest_path.weight > W:
continue
continue_ = True
approx = DeliveryPath(haul)
if approx > best_approx:
best_approx = approx
if not continue_:
break
return best_approx
if __name__ == '__main__':
deliveries = Delivery.generate(20)
weight_limit = 250
path = mkp_mst(D=deliveries, W=weight_limit)
path.plot(all_deliveries=deliveries)
print(f"best profit: ${path.profit:.2f}")
print(f"capacity used: {path.weight / weight_limit:.1%}")
def compare_mkp_solutions():
print("Comparing up to brute force limits (n=8)")
results = results_init()
for i in range(10):
deliveries = Delivery.generate(8)
weight_limit = 250
for algo in algorithms:
t0 = perf_counter()
path = algo(D=deliveries, W=weight_limit)
results[algo.__name__]['time'] += perf_counter() - t0
results[algo.__name__]['profit'] += path.profit
print(f"Round {i+1}/10")
pprint(results, width=50)
print('Compared to brute force:')
bf_res = results[algorithms[0].__name__]
for algo in algorithms[1:]:
n = algo.__name__
print(f"{n}: "
f"{results[n]['profit'] / bf_res['profit']:.1%} profit, "
f"{bf_res['time'] / results[n]['time']:.0f}x faster")
del algorithms[0]
print("\nComparing up to combinatorial mst limits (n=16)")
results = results_init()
for i in range(10):
deliveries = Delivery.generate(16)
weight_limit = 250
for algo in algorithms:
t0 = perf_counter()
path = algo(D=deliveries, W=weight_limit)
results[algo.__name__]['time'] += perf_counter() - t0
results[algo.__name__]['profit'] += path.profit
print(f"Round {i+1}/10")
pprint(results, width=50)
print('Compared to combinatorial mst:')
cmst_res = results[algorithms[0].__name__]
for algo in algorithms[1:]:
n = algo.__name__
print(f"{n}: "
f"{results[n]['profit'] / cmst_res['profit']:.1%} profit, "
f"{cmst_res['time'] / results[n]['time']:.0f}x faster")
del algorithms[0]
print("\nComparing the two fast approximation algorithms (n=200)")
results = results_init()
for i in range(10):
deliveries = Delivery.generate(250)
weight_limit = 2000
for algo in algorithms:
t0 = perf_counter()
path = algo(D=deliveries, W=weight_limit)
results[algo.__name__]['time'] += perf_counter() - t0
results[algo.__name__]['profit'] += path.profit
print(f"Round {i+1}/10")
pprint(results, width=50)
print('Comparing:')
for index in (0, 1):
n1 = algorithms[index].__name__
n2 = algorithms[not index].__name__
print(f"{n1}: "
f"{results[n1]['profit'] / results[n2]['profit']:.1%} profit, "
f"{results[n2]['time'] / results[n1]['time']:.2f}x faster")
def brute_force_mkp(D, W):
best_path = DeliveryPath()
for count in range(len(D)):
for haul in combinations(D, count + 1):
new_path = DeliveryPath(haul, make_mst=False)
if new_path.weight > W:
continue
for path in permutations(haul):
path = DeliveryPath(path, make_mst=False)
if path > new_path:
new_path = path
if new_path.profit > best_path.profit:
best_path = new_path
return best_path
if __name__ == '__main__':
deliveries = Delivery.generate(10)
weight_limit = 250
path = brute_force_mkp(D=deliveries, W=weight_limit)
path.plot(all_deliveries=deliveries)
print(f"best profit: ${path.profit:.2f}")
print(f"capacity used: {path.weight / weight_limit:.1%}")