Good old-fashioned AI (or what is now called Traditional AI) is deterministic in nature, while Generative AI is more probabilistic. Traditional AI relies on explicit rules, logic, and predefined algorithms. Given the same input and conditions, it will always produce the same output. This predictability ensures transparent behavior and decision-making processes.
Generative AI, on the other hand, generates new content after learning from data, expressing outcomes as probabilities. It adapts to different contexts and produces varied outputs even with the same input.
By combining Traditional AI for stability, interpretable decision-making, and well-defined rules in critical tasks, with Generative AI for creativity, adaptability, and handling complex, unstructured data, enterprises can create powerful systems that balance reliability and innovation.
This blog explores how this AI Symphony can be used in the context of an Airline Crew Scheduling System.
The Airline industry is a dynamic sector, where every aspect of its operations demands meticulous planning and execution. One of the core components of these operations is the task of the Crew Scheduling, which has to accommodate a wide range of variables and unforeseen events. The purpose of Crew Scheduling is to define where crew members will be on set dates and times. At the heart of crew scheduling is the Crew Roster. The diagram below brings out some of the key dimensions, if not all, that impact the activity of arriving at an optimal crew schedule. The diagram also outlines some of the key variables that are part of each of the impacting dimensions. For some of the terminologies involved, you can check out Ref 1.
The key dimensions that drive the schedule are:
Business related – Covers variables such as aircrafts, routes, schedules.
Crew related – Covers variables such as availability, roster bids, skills, training schedules, medical checks, license validity.
Disruptions – Covers events like technical failures, weather conditions, crew emergencies.
Legal requirements – Covers constraints like flight time, duty time, minimum rest period.
Labour union agreements – Covers constraints like agreed work hours, scheduling rules, pay and compensation, seniority considerations in roster bidding.
Crew Pairing – Covers requirements like flight pairing to be fulfilled by pairing crews. A flight pairing (also known as a trip or crew rotation) is a sequence of nonstop flights (flight legs) that starts and ends at the same airport. Once flight pairings are established, airlines can then assign crew members (crew pairing) specific tasks based on these designated flights.
Suppose an airline operates flights from New York (JFK) to London (LHR) and back. A pairing could be:
JFK-LHR (outbound leg)
LHR-JFK (return leg)
The crew assigned to this pairing would fly from JFK to LHR, rest, and then fly back to JFK.
Deadhead travels – A deadhead refers to a flight within a trip sequence where a crew member (such as a pilot or flight attendant) is not scheduled to work. Deadheads are necessary when trip continuity fails due to delays or cancellations and crew is required to reach a location to take over the shift. It’s a cost to the airline as a ticket revenue is lost.
Its planning has to accommodate available Deadhead routes, cost of travel on that route, conflict of crew rest period requirements with duration of travel on the route.
The key outcomes of scheduling exercise are:
Crew Roster – Shows assigned duties and responsibilities of each crew member. It ensures that the correct number of crew members are scheduled work at all times and to ensure that they are properly rested.
Crew communications and Notifications – Through SMS, WhatsApp or automated voice calls. The duty assignments, schedule changes, flight update etc. are conveyed to crew.
The future
Digitalization of Crew Scheduling and Roster Management has happened through IT Systems. Some levels of mathematical rules are incorporated in these systems to help the planners carry out the job. Combination of Traditional AI and Generative AI has potential to take this digitalization further to bring down the people intensity involved in creating these digital rosters making it more responsive to unknown events.
In general, Traditional AI is better for rule-based, deterministic tasks, while Generative AI excels in creative content generation and learning from data. Some of the areas covered below will provide appreciation of how these two AI types can work together. Though this is not an exhaustive list of possibilities.
- Crew Assignment Optimization: Crew assignment optimization involves creating efficient schedules for crew members based on predefined rules, multiple variables and constraints. Traditional AI can handle this well, as it relies on deterministic algorithms to find optimal assignments.
- Rostering: Automating the rostering process where all the required impacting elements, except may be disruptions, are accommodated is handled well by using Traditional AI. If the airline wants to implement Dynamic Rostering, which takes care of the learning from historical data and adjusting schedules based on changing conditions (e.g., flight delays, crew last minute demands), then Generative AI is more suitable. Generative AI is able to propose options that were never thought of before to make the roster more dynamic.
- Pairing Optimization: Pairing optimization involves creating efficient sequences of flights (pairings) for crew members. Such requirement is well suited for Traditional AI due to its deterministic behaviour.
- Deadhead crew positioning: In the context of deadhead positioning, having clear rules and protocols is crucial for efficient repositioning. Deadhead positioning requires immediate decisions based on operational needs (e.g., flight delays, crew availability) with stability and predictability. Adherence to legal regulations (e.g., duty time limits, rest requirements) is critical during disruptions. No creativity is acceptable here. It’s a large-scale repositioning exercise that needs to be managed timely with scalability. Hence Traditional AI is suitable here.
- Crew Communications and Notifications: Traditional AI can handle automated crew notifications (e.g., flight changes, duty reminders) based on predefined triggers. So suitable for routine communications. The interaction with the crews can be further enhanced by bringing in Generative AI Chatbots using natural language interactions for crew queries, assist with logistics, roster bids, crew swaps possibilities and so on. The interaction can become more context sensitive, for example in logistics assistance, based on crew’s current location, current weather condition there, current traffic situation there and so on. This also reduces administrative workload.
The above are the traditional scheduling reas. Some other possible areas where Generative AI can complement Traditional AI are as below.
- Scenario Exploration and Contingency Planning: Generative AI can simulate alternative scheduling scenarios based on historical patterns. It can explore “what-if” situations, such as crew shortages, equipment failures, or unexpected events. By creatively generating various scenarios, it helps airlines prepare for contingencies.
- Predictive Crew Sickness and Fatigue Management: Generative AI can analyze crew health data, historical sickness patterns, and fatigue indicators. It can predict potential crew shortages due to sickness or fatigue. Creativity lies in identifying early warning signs and suggesting preventive measures.
This use case brings out the fact that most of the operational ‘AIfication’ use cases in enterprises will be a hybrid approach involving Traditional AI and Generative AI.
References:
- Understanding Cabin Crew Roster https://cabincrewhq.com/cabin-crew-roster/
Note: The diagram was created using “Xmind” mind mapping tool from XMIND LTD.