def solve(self, show_iter=False):
supply = self.table[1:-1, -1]
demand = self.table[-1, 1:-1]
n = len(supply)
m = len(demand)
#compute Rij and Rji
Rij, Rji = np.zeros((2, n, m), dtype=object)
for i, s in enumerate(supply):
for j, d in enumerate(demand):
Rij[i, j] = d / s
Rji[i, j] = s / d
#solve for WCD and WCS
min_cost = np.inf
for R, title in zip([Rij, Rji], ["WCD", "WCS"]):
if show_iter:
print("{} SOLUSTION\n".format(title))
#make a copy of table then multiply with Rij/Rji (WCD/WCS)
cost = self.table[1:-1, 1:-1] * R
supply = self.table[1:-1, -1]
demand = self.table[-1, 1:-1]
trans = Transportation(cost, supply, demand)
trans.setup_table()
ks = KaragulSahin(trans)
alloc = ks.solve_part(show_iter=show_iter)
total_cost = self.find_cost(alloc, self.table)
if show_iter:
print("{} TOTAL COST = {}\n".format(title, total_cost))
#save allocation if it has minimum cost
if total_cost < min_cost:
min_cost = total_cost
self.alloc = alloc[:]
return np.array(self.alloc, dtype=object)
cost = np.array([[11, 13, 17, 14], [16, 18, 14, 10], [21, 24, 13, 10]])
supply = np.array([250, 300, 400])
demand = np.array([200, 225, 275, 250])
#example 2 unbalance problem
cost = np.array([[2, 7, 14], [3, 3, 1], [5, 4, 7], [1, 6, 2]])
supply = np.array([5, 8, 7, 15])
demand = np.array([7, 9, 18])
#initialize transportation problem
trans = Transportation(cost, supply, demand)
#setup transportation table.
#minimize=True for minimization problem, change to False for maximization, default=True.
#ignore this if problem is minimization and already balance
trans.setup_table(minimize=True)
#initialize ASM method with table that has been prepared before.
ASM = AssigningShortestMinimax(trans)
#solve problem and return allocation lists which consist n of (Ri, Cj, v)
#Ri and Cj is table index where cost is allocated and v it's allocated value.
#(R0, C1, 3) means 3 cost is allocated at Row 0 and Column 1.
#show_iter=True will showing table changes per iteration, default=False.
#revision=True will using ASM Revision algorithm for unbalance problem, default=False.
allocation = ASM.solve(show_iter=False, revision=False)
#print out allocation table in the form of pandas DataFrame.
#(doesn't work well if problem has large dimension).
trans.print_table(allocation)