Testing a simpler version of plant_location.py

Author: tuliotoffolo

examples/plant_location.py

@@ -18,7 +18,6 @@
     mpl.use('Agg')
     del mpl
 
-import matplotlib.pyplot as plt
 from math import sqrt, log
 from itertools import product
 from mip import Model, xsum, minimize, OptimizationStatus
@@ -42,20 +41,6 @@
 # demands
 d = {1: 302, 2: 273, 3: 275, 4: 266, 5: 287, 6: 296, 7: 297, 8: 310, 9: 302, 10: 309}
 
-# plotting possible plant locations
-for i, p in pf.items():
-    plt.scatter((p[0]), (p[1]), marker="^", color="purple", s=50)
-    plt.text((p[0]), (p[1]), "$f_%d$" % i)
-
-# plotting location of clients
-for i, p in pc.items():
-    plt.scatter((p[0]), (p[1]), marker="o", color="black", s=15)
-    plt.text((p[0]), (p[1]), "$c_{%d}$" % i)
-
-plt.text((20), (78), "Region 1")
-plt.text((70), (78), "Region 2")
-plt.plot((50, 50), (0, 80))
-
 dist = {(f, c): round(sqrt((pf[f][0] - pc[c][0]) ** 2 + (pf[f][1] - pc[c][1]) ** 2), 1)
         for (f, c) in product(F, C) }
 
@@ -102,21 +87,11 @@
 
 m.optimize()
 
-plt.savefig("location.pdf")
-
 if m.num_solutions:
     print("Solution with cost {} found.".format(m.objective_value))
     print("Facilities capacities: {} ".format([z[f].x for f in F]))
     print("Facilities cost: {}".format([y[f].x for f in F]))
-
-    # plotting allocations
-    for (i, j) in [(i, j) for (i, j) in product(F, C) if x[(i, j)].x >= 1e-6]:
-        plt.plot(
-            (pf[i][0], pc[j][0]), (pf[i][1], pc[j][1]), linestyle="--", color="darkgray"
-        )
-
-    plt.savefig("location-sol.pdf")
-
+    
 # sanity checks
 opt = 99733.94905406
 if m.status == OptimizationStatus.OPTIMAL: