Minimum transportation cost

# Minimum transportation cost using the Northwest corner rule import numpy as np def min_transportation_cost(transportation_costs, supply_by_factory, demand_by_warehouse): if np.sum(supply_by_factory) != np.sum(demand_by_warehouse): return "Sums of supply and demand must be equal" xshape, yshape = transportation_costs.shape matrix_type = supply_by_factory.dtype steps = np.zeros((xshape, yshape), dtype=matrix_type) length = len(supply_by_factory) row, col, lst_idx, i = 0, 0, 0, 0 steps[row, col] = demand_by_warehouse[0] while (row, col) != (length-1, length-1): start_cell = steps[row, col] if i % 2 == 0: col += 1 array_name = supply_by_factory else: row += 1 array_name = demand_by_warehouse lst_idx += 1 steps[row, col] = array_name[lst_idx] - start_cell i += 1 return np.sum(transportation_costs * steps) transportation_costs = np.array([ [4, 5, 6, 7], [5,10,12,6], [6,12,8,10], [8,11,3,7] ]) supply_by_factory = np.array([330, 350, 320, 220]) demand_by_warehouse = np.array([250, 260, 310, 400]) print(min_transportation_cost(transportation_costs, supply_by_factory, demand_by_warehouse)) # Initial basic feasible solution: 9700