Open Access

ARTICLE

A Two-Stage Scenario-Based Robust Optimization Model and a Column-Row Generation Method for Integrated Aircraft Maintenance-Routing and Crew Rostering

by Khalilallah Memarzadeh¹, Hamed Kazemipoor^1,*, Mohammad Fallah¹, Babak Farhang Moghaddam²

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, 1477893855, Iran
2 Institute for Management and Planning Studies, Tehran, 1978914153, Iran

* Corresponding Author: Hamed Kazemipoor. Email:

(This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)

Computer Modeling in Engineering & Sciences 2024, 141(2), 1275-1304. https://doi.org/10.32604/cmes.2024.050306

Received 02 February 2024; Accepted 09 July 2024; Issue published 27 September 2024

Abstract

Motivated by a critical issue of airline planning process, this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions. Following the route network scheme and generated flight timetables, aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management. This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints, rules, and regulations. Considering multiple locations for airline maintenance and crew bases, we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering (AMRCR) to achieve the minimum airline cost. One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights. Due to the fact that disruption scenarios are expressed discretely with a specified probability, and we provide adjustable decisions under disruption to deal with this disruption risk, we provide a Two-Stage Scenario-Based Robust Optimization (TSRO) model. In this model, here-and-now or first-stage variables are the initial resource assignment. Furthermore, to adapt itself to different disruption scenarios, the model considers some adjustable variables, such as the decision to cancel the flight in case of disruption, as wait-and-see or second-stage variables. Considering the complexity of integrated models, and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance, we apply the column and row generation (CRG) method that iteratively considers the disruption scenarios. The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability. To evaluate the proposed TSRO model, which solves the AMRCR problem in an integrated and robust manner, five Key Performance Indicators (KPIs) like Number of delayed/canceled flights, Average delay time, and Average profit are taken into account. As key results driven by conducting a case study, we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models. The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems. However, for large-scale instances the proposed TSRO model falls short in terms of computational efficiency. Conversely, the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.

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