问约束优化:只适用于继续选择的第二项的条件？

EN

# Instantiate solver
solver = pywraplp.Solver.CreateSolver('SCIP')

# Get data
data = get_model_data()

### VARIABLES ###

# whether a courier can ship a parcel (T or F depending on whether the courier is eligible to ship the parcel)
parcel_by_courier = {}
for parcel_num in data['parcel_range']:
    for courier_num in data['courier_range']:
        parcel_by_courier[parcel_num, courier_num] = solver.BoolVar(f"parcel_{parcel_num}_courier_{courier_num}")

# the total number of parcels shipped by a courier
all_courier_sums = {}
for courier_num in data['courier_range']:
    all_courier_sums[courier_num] = solver.IntVar(0, len(data['parcel_range']), f"{data['courier_names'][courier_num]}_total_parcels_shipped")

print('Number of variables =', solver.NumVariables(), '\n')

### CONSTRAINTS ###

# add constraint that all parcels have to be shipped
for parcel_num in data['parcel_range']:
    solver.Add(sum(parcel_by_courier[parcel_num, courier_num] for courier_num in data['courier_range']) == 1)

# add weight & dimension constraints
for parcel_num in data['parcel_range']:
    for courier_num in data['courier_range']:
        solver.Add(parcel_by_courier[parcel_num, courier_num] * data['parcel_weights'][parcel_num] <= parcel_by_courier[parcel_num, courier_num] * data['courier_weights'][courier_num])
        solver.Add(parcel_by_courier[parcel_num, courier_num] * data['parcel_largest_dimensions'][parcel_num] <= parcel_by_courier[parcel_num, courier_num] * data['courier_largest_dimensions'][courier_num])

# add a constraint that couriers can only ship >1 parcel IF "further_item_allowed" = True
for courier_num in data['courier_range']:
    solver.Add(sum(parcel_by_courier[parcel_num, courier_num] for parcel_num in data['parcel_range']) <= 1 if data['courier_further_item_allowed'][courier_num] is False else sum(parcel_by_courier[parcel_num, courier_num] for parcel_num in data['parcel_range']) >= 0)

print('Number of constraints =', solver.NumConstraints(), '\n')

### OBJECTIVE ###

# Calculate costs for the objective and run the solver

total_base_item_cost = []

for courier_num in data['courier_range']:
    for parcel_num in data['parcel_range']:
        if all_courier_sums[courier_num] >= 2:
            if ???:
                total_base_item_cost.append(data['courier_first_item_pence'][courier_num] * parcel_by_courier[parcel_num, courier_num])
            else:
                total_base_item_cost.append(data['courier_further_item_pence'][courier_num] * parcel_by_courier[parcel_num, courier_num])
        else:
            total_base_item_cost.append(data['courier_first_item_pence'][courier_num] * parcel_by_courier[parcel_num, courier_num])

total_cost = sum(total_base_item_cost)

solver.Minimize(total_cost)
status = solver.Solve()