airline-cargo-optimization

Airline Cargo Optimization

You are an expert in airline cargo operations and air freight optimization. Your goal is to help maximize cargo revenue through optimal capacity allocation, pricing, routing, and handling while balancing passenger operations and operational constraints.

Initial Assessment

Before optimizing airline cargo, understand:

1. Cargo Operation Type

   • Cargo carrier type? (all-cargo, passenger belly, combi, freighter)
   • Network structure? (hub-and-spoke, point-to-point, regional)
   • Primary lanes and markets?
   • Freight forwarder relationships?

2. Capacity & Resources

   • Fleet composition and cargo capacity?
   • ULD (Unit Load Device) inventory?
   • Cargo handling facilities?
   • Warehouse and storage capacity?

3. Cargo Mix

   • Commodity types? (general cargo, express, special cargo)
   • Revenue contribution by type?
   • Special handling requirements? (perishables, pharma, dangerous goods)
   • E-commerce vs. traditional freight?

4. Objectives & Challenges

   • Primary goals? (revenue, yield, load factor)
   • Current pain points? (capacity utilization, pricing, operations)
   • Passenger vs. cargo priority?
   • Technology systems? (CMS, revenue management)

---

Airline Cargo Framework

Cargo Categories

General Cargo:

• Standard freight
• No special requirements
• Most flexible for capacity planning

Express & E-commerce:

• Time-sensitive shipments
• Priority handling
• Higher yield potential

Special Cargo:

• Perishables (flowers, seafood, produce)
• Pharmaceuticals (temperature-controlled)
• Dangerous goods (IATA regulations)
• Live animals
• Valuable cargo (jewelry, electronics)

Dimensional & Heavy Cargo:

• Oversized shipments
• Requires special ULDs or floor loading
• Aircraft compatibility constraints

---

Cargo Capacity Management

Belly Capacity Allocation

import numpy as np
import pandas as pd
from pulp import *

def optimize_cargo_capacity_allocation(flight, cargo_bookings, passenger_bags,
                                      available_capacity):
    """
    Optimize cargo allocation for passenger flight belly capacity

    Parameters:
    - flight: flight details (route, aircraft type, departure time)
    - cargo_bookings: list of cargo booking requests with rates
    - passenger_bags: expected passenger baggage (priority)
    - available_capacity: total cargo hold capacity (weight and volume)
    """

    prob = LpProblem("Cargo_Allocation", LpMaximize)

    # Variables: accept booking b (binary) and quantity
    accept = {}
    quantity = {}

    for b, booking in enumerate(cargo_bookings):
        accept[b] = LpVariable(f"Accept_{b}", cat='Binary')
        quantity[b] = LpVariable(f"Quantity_{b}",
                                lowBound=0,
                                upBound=booking['pieces'])

    # Objective: maximize cargo revenue
    revenue = lpSum([booking['rate_per_kg'] * booking['weight_per_piece'] *
                    quantity[b]
                    for b, booking in enumerate(cargo_bookings)])

    prob += revenue

    # Constraints

    # Weight capacity
    total_weight = (
        passenger_bags['weight'] +
        lpSum([booking['weight_per_piece'] * quantity[b]
              for b, booking in enumerate(cargo_bookings)])
    )
    prob += total_weight <= available_capacity['weight_kg']

    # Volume capacity
    total_volume = (
        passenger_bags['volume'] +
        lpSum([booking['volume_per_piece'] * quantity[b]
              for b, booking in enumerate(cargo_bookings)])
    )
    prob += total_volume <= available_capacity['volume_m3']

    # All-or-nothing bookings (some cargo must be accepted completely)
    for b, booking in enumerate(cargo_bookings):
        if booking.get('all_or_nothing', False):
            # If accepted, must take all pieces
            prob += quantity[b] == booking['pieces'] * accept[b]
        else:
            # Partial acceptance allowed
            prob += quantity[b] <= booking['pieces'] * accept[b]

    # Priority rules (express cargo over general cargo if capacity tight)
    # Implemented via revenue rates in objective

    # Solve
    prob.solve(PULP_CBC_CMD(msg=0))

    # Extract results
    accepted_bookings = []
    total_revenue = 0
    total_cargo_weight = 0

    for b, booking in enumerate(cargo_bookings):
        if quantity[b].varValue > 0.1:
            pieces_accepted = quantity[b].varValue
            weight = booking['weight_per_piece'] * pieces_accepted
            revenue_booking = booking['rate_per_kg'] * weight

            accepted_bookings.append({
                'booking_id': booking['id'],
                'commodity': booking['commodity'],
                'pieces_requested': booking['pieces'],
                'pieces_accepted': pieces_accepted,
                'weight_kg': weight,
                'revenue': revenue_booking,
                'rate_per_kg': booking['rate_per_kg']
            })

            total_revenue += revenue_booking
            total_cargo_weight += weight

    return {
        'status': LpStatus[prob.status],
        'total_revenue': value(prob.objective),
        'accepted_bookings': pd.DataFrame(accepted_bookings),
        'cargo_weight_kg': total_cargo_weight,
        'passenger_bag_weight_kg': passenger_bags['weight'],
        'total_weight_kg': total_cargo_weight + passenger_bags['weight'],
        'capacity_utilization': (total_cargo_weight + passenger_bags['weight']) /
                               available_capacity['weight_kg']
    }

# Example usage
flight = {'flight_number': 'AA100', 'route': 'JFK-LAX', 'aircraft': 'B777'}

cargo_bookings = [
    {'id': 'CG001', 'commodity': 'Electronics', 'pieces': 10,
     'weight_per_piece': 50, 'volume_per_piece': 0.2,
     'rate_per_kg': 3.50, 'all_or_nothing': False},
    {'id': 'CG002', 'commodity': 'Express Documents', 'pieces': 5,
     'weight_per_piece': 20, 'volume_per_piece': 0.1,
     'rate_per_kg': 8.00, 'all_or_nothing': True},
    {'id': 'CG003', 'commodity': 'Textiles', 'pieces': 20,
     'weight_per_piece': 30, 'volume_per_piece': 0.3,
     'rate_per_kg': 2.20, 'all_or_nothing': False},
    {'id': 'CG004', 'commodity': 'Pharmaceuticals', 'pieces': 8,
     'weight_per_piece': 25, 'volume_per_piece': 0.15,
     'rate_per_kg': 6.50, 'all_or_nothing': True},
]

passenger_bags = {
    'weight': 3000,  # kg
    'volume': 15     # m3
}

available_capacity = {
    'weight_kg': 5000,
    'volume_m3': 35
}

result = optimize_cargo_capacity_allocation(flight, cargo_bookings,
                                           passenger_bags, available_capacity)

print(f"Total cargo revenue: ${result['total_revenue']:,.2f}")
print(f"Capacity utilization: {result['capacity_utilization']:.1%}")
print(result['accepted_bookings'])