Expert systems often develop around core industries where planning and/or diagnosing complexities make human resource acquisition and retention a major issue. The aviation industry is a perfect example.

The schedulling flights based on economics, environmental, regulatory requirements and airway traffic parameters is extremely complex. And mistakes extremely costly.

So too in aircraft maintenance, and once you enter the world of military aviation - it just gets down right non-sensible to most humans.

Expert systems typically found in the avaition industry include:

The Aviation Expert System - used to clarify psychological assessment issues in the field of aviation.
GAPATS - General Aviation Pilot Advisor and Training System
AMES - Aircraft Maintenance Expert Systems
Operations Management - Flights Schedulling, Crew Rostering, Airline Gate Allocation Schedulling. See JAL Case Study
CASRAP - Civil Aviation Security Risk Assessment Program
Anti-G control schedule for jet fighter pilots

GAPATS

The General Aviation Pilot Advisor and Training System (GAPATS) is a computerized airborne expert system developed jointly by Knowledge Based Systems, Inc. (KBSI) and Texas A&M University (TAMU).

This system uses AI fuzzy logic to infer the flight mode of an aircraft from:

sensed flight parameters
an embedded knowledge base, and
pilot inputs

This data is then used to assess the pilot’s flying performance and issue recommendations for pilot actions.

GPATS improves safety by enhancing the pilot’s situational awareness and by reducing the cost and time required to achieve and maintain pilot proficiency, without adding to pilot workload. More information on GPATS

Aircraft Maintenance Expert Systems

AMES have been used since the early 1990's. Manual proceduresaround aircraft maintenance are very strenuous and time consuming. Diagnosis of aircraft malfunctions is an ideal candidate for an expert system to assist in the diagnosis of aircraft problems.

Operations Management Decision Suppport

Airline operational planning, scheduling and controlling [OPSC] is one of the most demanding operational scenarios. For details on JAL Scheduling Case Study

Flight operational control decision-making operates within the structured flight schedule planning [long and short run] developed by airline competitive strategy. It must then manage the pre-described flight schedule on a daily basis, in a highly dynamic environment. Affects such as weather, unscheduled equipment maintenance, crew shortages, regulatory factors, and aircraft loading can make the profitable deployment and management of a pre-determined flight schedule very complex.

Airline operational management systems are heuristic, experience-based tools that apply the above factors in a real-time decision support systems [DSS]. To stay economically competitive, airlines need decision-making tools that can provide qualified, if not quantified information rapidly. Examples of such systems include:

Singapore Airlines

Singapore Airlines installed a commercial off the shelf AI based DSS platform to support scheduling, crew, maintenance decisions. In the interim, they have implemented a Crew Management System [ICMS] - a stand alone AI based DSS, with limited integration of other management considerations. Unix based, written in C, and supported by an Oracle database.

Southwest

Southwest developed their own Integrated Flight Tracking System [Swift] in 1995. SWIFT allows 37 dispatchers the ability to track 2,200 daily flights, in just 45 seconds [previously a 15 minute calculation]. Swift provides AI decision-making, removing the need to manually filter through irrelevant information. Maintenance, planning, and other functions have since been integrated to Swift.

Delta Airlines

Delta Airlines commissioned Transquest to develop an AI-DSS system. This AI-DSS automatically determines the solutions to problems such as: Which aircraft in a large holding pattern should land first? Or; Which flight(s) should be canceled or re-routed as a result of ATC flow control?

United Airlines

United Airlines initiated an AI-DSS called System Operations Advisor [SOA] in 1992. During the next 12 month period, the SOA reduced potential delays, on the deployment of over 2,000 flights a day on over five continents, by 27,000 minutes, which apart from customer convenience, translated into savings of approximately $540,000 in related delay costs.

SOA allows the operational manager to use a "solve button" on module programs that integrate and provide real-time decision support for delaying a flight, swapping and canceling. Each module acts as an ES and provides a graphical user interface (GUI) that may be used to set up models for solving specific real-time problems.

SOA uses a mixed nodal hierarchy model, consisting of nodes and arcs.

Nodes represent arrival, ground and departure times.
Arcs indicates the direction of specific aircraft flight flow, a direct route, maintenance, cancellation or the swap of alternate aircraft.

Allocated costs are assigned to each arch created within each model set calculated by SOA. The slope and flow direction of each arc determines the overall affect of the incident on the final solution. After pushing the solve button, SOA provides the optimal cost solution along with other [ranked] alternatives.

Thereafter, United developed additional integrated AI-DSS modules to assist with the management of gates, passenger flow, and overall staffing.

Cathay Pacific

Cathay Pacifics hierarchy for strategic planning was structured as:

Long term strategy - includes analysis of new aircraft type, evaluation of fleet plan, effects of aircraft on deployed routes, and new route studies
Medium and short run planning - consist of the same considerations conducted in long range planning with the addition of seasonal and weekly ad hoc modifications.

For short term planning Cathay developed an AI-DSS platform called 'Interactive Flight Scheduling System' [IFSS].

IFSS is a short term planning tool that employs a rules base and symbolic values.

Rules - include maintenance, ad hoc reports, training flights, charter flights, and many other factors translated into computational values.
Symbolic values - are generated by the IFSS upon the user’s request, based on commands such as: Shift Flight, Best Fit, Exchange Flights, Cancel Flight, Upgrade Flight (larger aircraft) or Downgrade Flight (smaller aircraft).

The IFSS also allows the user to manually Add, Delete, Move, Exchange, and Search various flights.

A utility function on the IA processor allows the airline to pre-define the probabilities for utility of affected operational attributes for each calculated solution. This allows the user to determine the highest probability of economic utility when deciding between solutions that will all solve the defined problem. This allows the operational manager to refine the IFSS selected solution within real-time decision making.

Anti-G Fighter Pilot System

The high maneuverability of modern jet fighters often subjects the pilots to high Gz acceleration. One of the adverse effects of Gz acceleration is loss of consciousness. The Anti-G Fighter Pilot System presents an alternative to the current protection pressure mask and pressurized G-suit. The system used expert knowledge and pilots' anthropometric and physiologic data to generate control schedules of the G-suit and mask pressures of jet fighter pilots.

Civil Aviation Security Risk Assessment

AKELA has developed an expert system program [CASRAP] which enables users to examine and assess the three major elements of risk - threats, vulnerabilities, and assets - and then determine the impact of mitigating measures on overall security risk. The system allows comparison between airports in widely varying operating environments.

The program leads the user through a vulnerability assessment which includes: physical, operational, and technical elements of security in each of the major areas of an airport. Full details on CASRAP [pdf]

NEXT: JAL Scheduling Case Study Using Expert System

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