AIFeb 4, 2014

Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources

arXiv:1402.0582v127 citations
Originality Incremental advance
AI Analysis

This work addresses a domain-specific problem for military aircraft fleet management, offering incremental improvements in scheduling efficiency.

The paper tackles the dynamic repair shop scheduling problem for military aircraft fleets by proposing a logic-based Benders decomposition method to solve static sub-problems and rescheduling policies, resulting in a 10% increase in long-term aircraft availability and a fourfold speedup in finding optimal solutions.

We address a dynamic repair shop scheduling problem in the context of military aircraft fleet management where the goal is to maintain a full complement of aircraft over the long-term. A number of flights, each with a requirement for a specific number and type of aircraft, are already scheduled over a long horizon. We need to assign aircraft to flights and schedule repair activities while considering the flights requirements, repair capacity, and aircraft failures. The number of aircraft awaiting repair dynamically changes over time due to failures and it is therefore necessary to rebuild the repair schedule online. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter time periods. We propose a complete approach based on the logic-based Benders decomposition to solve the static sub-problems, and design different rescheduling policies to schedule the dynamic repair shop. Computational experiments demonstrate that the Benders model is able to find and prove optimal solutions on average four times faster than a mixed integer programming model. The rescheduling approach having both aspects of scheduling over a longer horizon and quickly adjusting the schedule increases aircraft available in the long term by 10% compared to the approaches having either one of the aspects alone.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes