Deterministic Modeling of Collaborative Decision-Making in the Process of Preflight AIrcraft Maintenance

In accordance with the latest ICAO requirements, the implementation of collaborative decision-making (CDM) models is becoming increasingly relevant for improving the air transport system efficiency. The Airport-CDM concept brings together the interests of various airport stakeholders – aircraft and airport operators, engineering companies, ground handling agents, and air traffic control services – to create a coordinated information space where all parties receive accurate, up to date information providing a single view of the situation on the ground and in the airspace. Airport-CDM is based on openness and information sharing, and its implementation is aimed at optimizing air traffic and airport resource management by reducing delays, improving the predictability of operations, and rationalizing the use of infrastructure. It is determined that, despite a significant amount of research, interaction level between aviation specialists in teams remains insufficient for effective joint formation and optimal decision-making. Preflight preparation of aircraft is carried out by maintenance personnel consisting of two to three people as part of line maintenance and covers critical systems, including engines, landing gear, hydraulics, and avionics. If the team of specialists finds any discrepancies, they are immediately eliminated to ensure the safety of passengers and crew. A methodology for deterministic modeling of individual and collaborative decision-making (CDM) by maintenance personnel during aircraft preflight preparation based on the Critical Path Method (CPM) is presented. An example of CDM by maintenance personnel (aircraft airframe and engine specialist, aviation and radio-electronic equipment specialist, and ground operator) during the preflight preparation of the An-26 is considered. Structural time tables and network graphs of maintenance personnel actions during preflight preparation of the An-26 are constructed in accordance with the regulations before and after optimization. The calculations are performed using the MS Excel spreadsheet and the Python language in the COLAB programming environment, and identical results are obtained. After optimization, preflight preparation of the An-26 with the ground operator participation will take place 22.42% faster than with the ground operator participation before optimization, and 48.81% faster without the ground operator participation before optimization. The developed deterministic CDM models will optimize the operational interaction between the maintenance personnel during preflight preparation of aircraft and minimize the time required to perform scheduled routine maintenance.