def plot(self, name):
"""
Plots results, optimizes model first if not done yet
"""
if self.m.Status == 1:
print("Model not yet optimized, now optimizing")
self.optimize(verbose=True)
plot(self.location, self.tours, name)
def plot(self, name):
"""
Plots results, optimizes model first if not done yet
"""
if not len(self.visits) == self.n:
print("Model not yet optimized, now optimizing")
self.optimize(verbose=True)
plot(self.location, [self.tour], name)
def plot(self):
if not test:
vs.plot(Array.full_array)
from visualizer import plot, plot_tsp #Example of how to use visualizer.py #Print a VRP solution. Locations are pairs of x/y coordinates, #routes is a 2-d list representing the routes. The variable #name is used as the title of the graph, and is also the name of the #output file. locations = [(1, 2), (2, 3), (5, 0), (4, 8), (5, 9)] routes = [[0, 1, 2, 0], [0, 3, 4, 0]] name = "VRP" plot(locations, routes, name) #Print a TSP solution. Locations are pairs of x/y coordinates, #route is a list representing the TSP tour. The variable #name is used as the title of the graph, and is also the name of the #output file. # locations=[(1,2),(2,3),(5,0),(4,8),(5,9)] # route=[0,1,2,3,4,0] # name="TSP" # plot_tsp(locations,route,name)
Q = 80
r = 4
f = 101
# To hold results, initialize as (list) : list[k] => (ObjValK, xvarsK)
K_optimals = []
# Iterate through loop, initialize model
for K in range(1, 10):
model = CVRPModel(Q, r, K, f, dist, demand)
model.optimize()
K_optimals.append({
"K": K,
"Obj": model.m.ObjVal,
"Tour": model.get_tours()
})
[result["Tour"] for result in K_optimals]
x = [r["K"] for r in K_optimals]
y = [r["Obj"] for r in K_optimals]
plt.figure(dpi=150), plt.bar(x, y), plt.title(
"VRP - Vehicle Scaling"), plt.xlabel("K number of vehicles"), plt.ylabel(
"Profit (revenue, transport and fixed costs)")
# Get best result for all K's
BEST_RESULT = max(K_optimals, key=lambda res: res["Obj"])
print(BEST_RESULT["Obj"])
plot(location, BEST_RESULT["Tour"], "CVRP")
