Data commit

Task/Vogels-approximation-method/11l/vogels-approximation-method.11l

@@ -0,0 +1,55 @@
+V 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]]
+V demand = [‘A’ = 30, ‘B’ = 20, ‘C’ = 70, ‘D’ = 30, ‘E’ = 60]
+V cols = sorted(demand.keys())
+V supply = [‘W’ = 50, ‘X’ = 60, ‘Y’ = 50, ‘Z’ = 50]
+V res = Dict(costs.keys().map(k -> (k, DefaultDict[Char, Int]())))
+[Char = [Char]] g
+L(x) supply.keys()
+   g[x] = sorted(costs[x].keys(), key' g -> :costs[@x][g])
+L(x) demand.keys()
+   g[x] = sorted(costs.keys(), key' g -> :costs[g][@x])
+
+L !g.empty
+   [Char = Int] d
+   L(x) demand.keys()
+      d[x] = I g[x].len > 1 {(costs[g[x][1]][x] - costs[g[x][0]][x])} E costs[g[x][0]][x]
+   [Char = Int] s
+   L(x) supply.keys()
+      s[x] = I g[x].len > 1 {(costs[x][g[x][1]] - costs[x][g[x][0]])} E costs[x][g[x][0]]
+   V f = max(d.keys(), key' n -> @d[n])
+   V t = max(s.keys(), key' n -> @s[n])
+   (t, f) = I d[f] > s[t] {(f, g[f][0])} E (g[t][0], t)
+   V v = min(supply[f], demand[t])
+   res[f][t] += v
+   demand[t] -= v
+   I demand[t] == 0
+      L(k, n) supply
+         I n != 0
+            g[k].remove(t)
+      g.pop(t)
+      demand.pop(t)
+   supply[f] -= v
+   I supply[f] == 0
+      L(k, n) demand
+         I n != 0
+            g[k].remove(f)
+      g.pop(f)
+      supply.pop(f)
+
+L(n) cols
+   print("\t "n, end' ‘ ’)
+print()
+V cost = 0
+L(g) sorted(costs.keys())
+   print(g" \t", end' ‘ ’)
+   L(n) cols
+      V y = res[g][n]
+      I y != 0
+         print(y, end' ‘ ’)
+      cost += y * costs[g][n]
+      print("\t", end' ‘ ’)
+   print()
+print("\n\nTotal Cost =  "cost)