Optimize flight and cargo bookings for profit

OptiCargo: make the booking algorithm profitable

You are given two streams/lists:

• Flights you may attempt to book. Each flight has:
  • flight_id
  • depart_time and arrive_time
  • max_weight (capacity)
  • cost (paid only if you successfully book the flight)
• Cargo jobs you may book. Each cargo has:
  • cargo_id
  • weight
  • latest_arrival_time (deadline)
  • revenue (earned if delivered by the deadline)

A cargo job can be assigned to at most one booked flight. A flight can carry multiple cargo jobs as long as total assigned weight ≤ max_weight. A cargo job is deliverable on a flight if arrive_time ≤ latest_arrival_time.

Task A (batch / fix existing code)

The existing implementation is unprofitable because it books all flights regardless of whether there is profitable cargo to carry.

Design/implement changes so that the algorithm chooses which flights to book and which cargo to assign to maximize total profit:

$\text{profit} = \sum(\text{revenue of delivered cargo}) - \sum(\text{cost of booked flights})$

You may output either:

• the maximum profit value, or
• the set of booked flights and cargo-to-flight assignments.

Task B (incremental / streaming class)

Implement a class that processes events as they arrive:

• onFlight(flight) adds a new available flight.
• onCargo(cargo) adds a new available cargo job.
• Optionally, decide() (or similar) updates the plan.

At any time, the class should be able to report the current planned profit and/or current bookings/assignments.

Realism constraint (booking may fail)

When you “apply” to book a flight, the booking can fail (e.g., another party booked it first). Your design should handle failed bookings gracefully (e.g., retry, re-plan, or treat it as removed from availability).