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Disruption Management in Airline Operations: A Solver-based Approach using Time-Space Network Optimization

J. Rodrigues, F. Turoboś, M. Lenartowicz, Z. Puchała, M. Klimek, K. Hendzel, P. Gepner

TL;DR

This work addresses day-of airline disruption management by proposing AIRS, a solver-based system that jointly optimizes aircraft and crew recovery within a Time-Space Network (TSN) MILP framework, followed by a passenger reallocation module PaxR. AIRS.ACR builds an integrated Aircraft & Crew Recovery, while PaxR reassigns passengers to recovered schedules, balancing feasibility and disruption costs. The approach demonstrates scalable performance on realistic-sized problems and reduces secondary delays, cancellations, and operational costs, offering a rigorous, implementable tool for AOCCs. The framework's modularity and constraint incorporation support practical deployment and pave the way for real-time data-driven enhancement and deeper passenger-centric optimization.

Abstract

This paper presents AIRS, a day-of-operations disruption-recovery system. AIRS.ACR models integrated aircraft-crew recovery on a Time-Space Network (TSN) and solves a mixed-integer linear program (MILP) that enforces rotation continuity, crew legality, maintenance windows, slot capacities, and multi-leg integrity via flow-balance constraints; disruption-aware search-space construction and warm starts control combinatorial growth. A companion module, AIRS.PaxR, performs rapid passenger re-accommodation using greedy assignment and lightweight evolutionary search while preserving aircraft-crew feasibility. Across realistic evaluations, AIRS meets operational decision windows and reduces recovery costs relative to manual or sequential methods, providing a scalable, extensible decision-support capability for operations control centers.

Disruption Management in Airline Operations: A Solver-based Approach using Time-Space Network Optimization

TL;DR

This work addresses day-of airline disruption management by proposing AIRS, a solver-based system that jointly optimizes aircraft and crew recovery within a Time-Space Network (TSN) MILP framework, followed by a passenger reallocation module PaxR. AIRS.ACR builds an integrated Aircraft & Crew Recovery, while PaxR reassigns passengers to recovered schedules, balancing feasibility and disruption costs. The approach demonstrates scalable performance on realistic-sized problems and reduces secondary delays, cancellations, and operational costs, offering a rigorous, implementable tool for AOCCs. The framework's modularity and constraint incorporation support practical deployment and pave the way for real-time data-driven enhancement and deeper passenger-centric optimization.

Abstract

This paper presents AIRS, a day-of-operations disruption-recovery system. AIRS.ACR models integrated aircraft-crew recovery on a Time-Space Network (TSN) and solves a mixed-integer linear program (MILP) that enforces rotation continuity, crew legality, maintenance windows, slot capacities, and multi-leg integrity via flow-balance constraints; disruption-aware search-space construction and warm starts control combinatorial growth. A companion module, AIRS.PaxR, performs rapid passenger re-accommodation using greedy assignment and lightweight evolutionary search while preserving aircraft-crew feasibility. Across realistic evaluations, AIRS meets operational decision windows and reduces recovery costs relative to manual or sequential methods, providing a scalable, extensible decision-support capability for operations control centers.

Paper Structure

This paper contains 25 sections, 1 equation, 12 figures, 5 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Executive Summary
  3. Background and Motivation
  4. The Resilient Yet Fragile Airline Industry
  5. Current Disruption Management Practices and Limitations
  6. Literature review
  7. Aircraft Recovery
  8. Crew Recovery
  9. Passenger Recovery
  10. Integrated Recovery Approaches
  11. Research gap
  12. Motivation for a Solver-Based Optimization Approach
  13. System Overview: AIRS
  14. Problem Scenario
  15. The AIRS method
  16. ...and 10 more sections

Figures (12)

  • Figure 1: Approximate problem timeline, such that "C.T." is Current Time, "R.S." is Recovery Start, and "R.F." is Recovery Finish. Not in scale.
  • Figure 2: Flow diagram: top level view of solution steps in the AIRS solver.
  • Figure 3: Required Arcs for representing the Option of scheduling a flight in the aircraft network.
  • Figure 4: Required Arcs for representing the Option of scheduling a flight in the crew network.
  • Figure 5: Example scheme of Arcs for one 2-leg flight and one single-leg flight.
  • ...and 7 more figures