gistfile1.txt

from pulp import LpVariable, LpProblem, LpMaximize, LpStatus, value, LpInteger

# abstract simple problem
prob = LpProblem("Flow Model", LpMaximize)
# decision variables

buy_d1 = LpVariable("buy_d1", 0, 60_000)
buy_d2 = LpVariable("buy_d2", 0, 60_000)
store_d1_d2 = LpVariable("store_d1_d2", 0, 50_000)
store_d2_d3 = LpVariable("store_d2_d3", 0, 50_000)
sell_d2 = LpVariable("sell_d2", 0)
sell_d3 = LpVariable("sell_d3", 0)

prob += buy_d1 == store_d1_d2
prob += buy_d2 + store_d1_d2 == sell_d2 + store_d2_d3
prob += store_d2_d3 == sell_d3
prob += buy_d1 + buy_d2 <= 90_000

# sell_d2 mod 7000 == 0
number_of_trucks_d2 = LpVariable("number_of_trucks_d2", 0, 100, cat=LpInteger)
number_of_trucks_d3 = LpVariable("number_of_trucks_d3", 0, 100, cat=LpInteger)
prob += number_of_trucks_d2 * 7_000 == sell_d2
prob += number_of_trucks_d3 * 7_000 == sell_d3


# Maximize
sales = sell_d2 * 0.51 + sell_d3 * 0.58
storage_cost = (store_d2_d3 + store_d1_d2) * 0.01
purchase_cost = buy_d1 * 0.50 + buy_d2 * 0.52
prob += sales - purchase_cost - storage_cost

status = prob.solve()

print(LpStatus[status])

for p in prob.variables():
    print(p.name, value(p))