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

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+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