RosettaCodeData/Task/Vogels-approximation-method/Python/vogels-approximation-method.py

58 lines
1.7 KiB
Python

from collections import defaultdict
costs = {'W': {'A': 16, 'B': 16, 'C': 13, 'D': 22, 'E': 17},
'X': {'A': 14, 'B': 14, 'C': 13, 'D': 19, 'E': 15},
'Y': {'A': 19, 'B': 19, 'C': 20, 'D': 23, 'E': 50},
'Z': {'A': 50, 'B': 12, 'C': 50, 'D': 15, 'E': 11}}
demand = {'A': 30, 'B': 20, 'C': 70, 'D': 30, 'E': 60}
cols = sorted(demand.iterkeys())
supply = {'W': 50, 'X': 60, 'Y': 50, 'Z': 50}
res = dict((k, defaultdict(int)) for k in costs)
g = {}
for x in supply:
g[x] = sorted(costs[x].iterkeys(), key=lambda g: costs[x][g])
for x in demand:
g[x] = sorted(costs.iterkeys(), key=lambda g: costs[g][x])
while g:
d = {}
for x in demand:
d[x] = (costs[g[x][1]][x] - costs[g[x][0]][x]) if len(g[x]) > 1 else costs[g[x][0]][x]
s = {}
for x in supply:
s[x] = (costs[x][g[x][1]] - costs[x][g[x][0]]) if len(g[x]) > 1 else costs[x][g[x][0]]
f = max(d, key=lambda n: d[n])
t = max(s, key=lambda n: s[n])
t, f = (f, g[f][0]) if d[f] > s[t] else (g[t][0], t)
v = min(supply[f], demand[t])
res[f][t] += v
demand[t] -= v
if demand[t] == 0:
for k, n in supply.iteritems():
if n != 0:
g[k].remove(t)
del g[t]
del demand[t]
supply[f] -= v
if supply[f] == 0:
for k, n in demand.iteritems():
if n != 0:
g[k].remove(f)
del g[f]
del supply[f]
for n in cols:
print "\t", n,
print
cost = 0
for g in sorted(costs):
print g, "\t",
for n in cols:
y = res[g][n]
if y != 0:
print y,
cost += y * costs[g][n]
print "\t",
print
print "\n\nTotal Cost = ", cost