An efficient resource utilization and scheduling strategy for in-service aircraft maintenance and operations

Abstract

Optimal scheduling of maintenance activities requires the solution of combinatorial optimization problems that need to be efficiently modeled and solved with optimization techniques. Maintenance scheduling and operations-associated problems in the aviation industry can efficiently enhance competitiveness. In the maintenance and scheduling problem, aircrafts need to undergo tasks for both line (A check) and base (C check) maintenance at various hangers at MRO (Maintenance, Repair and Operations) based on resource availability (both human and material). The determination of the optimal maintenance plan, in terms of allocating the resources to the aircraft, and resource movement from one aircraft to another based on availability and licensed skills in the presence of multiple tasks and capacity constraints so as to obtain maximum utilization of resources at maintenance site and minimize the turnaround time is a complex combinatorial optimization problem. To the best of our knowledge, this work is the first CP (Constraint Programming) based mathematical solution that jointly integrates zone, task precedence, technician-pool sharing, and multi-shift continuity for large-scale aircraft maintenance scheduling. In this article, we proposed an efficient optimization strategy that overcomes many of the drawbacks of the formulation/strategies available in literature and helps in determining efficient execution of maintenance work packages. The proposed strategy is generic, encompassing multi-aircraft, multi-skill and multi-shift scheduling capabilities and is validated on two real scenario business case studies, one each for line maintenance (A check) tasks and base maintenance (C check) tasks, as well as six large-scale synthetic scenarios with up to 20,000 tasks, demonstrating feasibility and scalable performance. The proposed strategy is demonstrated on MRO scheduling and it shows an improvement of up to 30.68% in the turn-around time by incorporating the proposed optimization strategy.