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print_solution
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# Solve | |
status = solver.Solve() | |
print('==== Assignment Result: ====') | |
if status == pywraplp.Solver.OPTIMAL or status == pywraplp.Solver.FEASIBLE: | |
print('Total cost = ', solver.Objective().Value(), '\n') | |
prods = generate_items_dict(data_model['products']) | |
locations = generate_items_dict(data_model['locations']) | |
for i in range(num_products): | |
product = data_model['products'][i] | |
for j in range(num_locations): | |
location = data_model['locations'][j] | |
if x[i, j].solution_value() > 0.5: | |
prods[product] += x[i, j].solution_value() | |
locations[location] += x[i, j].solution_value() | |
cost_product = data_model['costs'][i][j].product | |
cost_product_name = data_model['costs'][i][j].product.name | |
cost_location_name = data_model['costs'][i][j].location.name | |
quantity = x[i, j].solution_value() | |
res = 'Product %s assigned to Shelf %s. Quantity = %d Cost = %f ' | |
print(res % ( cost_product_name, cost_location_name , quantity, | |
quantity * (data_model['costs'][i][j].value - | |
data_model['allocated_unallocated_prod_relationship'][i][j].weight) )) | |
print() | |
print('==== Weights w[i, j, k, j] ====') | |
total_weight = 0 | |
for upr in data_model['unallocated_prod_relationship']: | |
i, k = data_model['products'].index(upr.product1), data_model['products'].index(upr.product2) | |
for j in range(num_locations): | |
res = 'Product %s and Product %s at Location %s: Weight = %d' | |
print(res % (upr.product1.name, upr.product2.name, data_model['locations'][j].name, w[i, j, k, j].solution_value() )) | |
total_weight += w[i, j, k, j].solution_value() | |
print('Total Weight = %d' % (total_weight)) | |
if status == pywraplp.Solver.OPTIMAL: | |
print('OPTIMAL') | |
elif status == pywraplp.Solver.FEASIBLE: | |
print('FEASIBLE') | |
else: | |
print('No Solution Found') |
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