Airline Fleet Assignment Problem Research Articles

Extended flexible capacity planning methodology with flight network contribution to the airline fleet assignment problem

Flexible capacity planning is crucial for the airline industry due to the high fixed cost of assets, notably aircraft ownership, and the fluctuating demand, particularly in response to external shocks like pandemics and political crises. Following the negative consequences of such events on the financial positions of almost all airline companies, there is growing interest in developing more responsive capacity planning structures for cost efficiency in the airline industry. In this study, we present a sequential two-phase methodology for flexible capacity planning and airline fleet assignment considering the factors related to fleeting strategies, passenger demand behavior, and contribution of flights in the network. The first phase of the methodology generates potential scenarios to estimate the number of flights to the destinations, based on the data and the reports provided by two prominent associations in the aviation industry, which incorporate various country-related and airline-related factors, including population, income, routes, airports, and connectivity. The second phase identifies the required number of aircraft for each type within the fleet inventory, taking into account the aircraft utilization rates associated with each scenario. The fleet assignment problem is also reformulated to employ the objective of maximizing total profit of the entire flight network by introducing network contribution and nation/region-specific limitations in this phase. A real case study from Turkey is conducted to illustrate efficiency of the proposed methodology. We also present a comparative study to highlight the impact of network contribution and how fleet size affects the outcomes. We have observed that the inclusion of the network contribution aspect and country constraints has led to a significant improvement in financial indicators and connectivity. The results show that the methodology can effectively tackle challenges in airline transportation problems.

Two-Phase Heuristic Algorithm for Integrated Airline Fleet Assignment and Routing Problem

High profitability and high costs have stiffened competition in the airline industry. The main purpose of the study is to propose a computationally efficient algorithm for integrated fleet assignments and aircraft routing problems for a real-case hub and spoke airline planning problem. The economic concerns of airline operations have led to the need for minimising costs and increasing the ability to meet rising demands. Since fleets are the most limited and valuable assets of airline carriers, the allocation of aircraft to scheduled flights directly affects profitability/market share. The airline fleet assignment problem (AFAP) addresses the assignment of aircraft, each with a different capacity, capability, availability, and requirement, to a given flight schedule. This study proposes a mathematical model and heuristic method for solving a real-life airline fleet assignment and aircraft routing problem. We generate a set of problem instances based on real data and conduct a computational experiment to assess the performance of the proposed algorithm. The numerical study and experimental results indicate that the heuristic algorithm provides optimal solutions for the integrated fleet assignment and aircraft routing problem. Furthermore, a computational study reveals that compared with the heuristic method, solving the mathematical model takes significantly longer to execute.

Airline fleet assignment with internal passenger flow reevaluations

The airline fleet assignment problem consists of assigning an aircraft type to each flight leg of a flight schedule in order to maximize the airline expected profit. Most existing fleet assignment models (FAMs) use an estimation of the revenues per flight leg that neglects the interdependency between the flight legs and poorly approximates the spill and recapture of the passengers. To overcome this difficulty, Dumas et al. (Transp Res Part B 43(4):466–475, 2009 ) have introduced an iterative solution method that solves at each iteration a FAM and a passenger flow model (PFM). A solution to the PFM provides the expected number of passengers on each leg, taking into account spill and recapture. These numbers are then used to better estimate the revenues per flight leg for the next iteration. Compared to solving a FAM once, this method yields better quality solutions but requires much larger computational times (by a factor 10 or more). In this paper, we aim at reducing these computational times while preserving solution quality. To do so, we propose to reevaluate periodically the flight leg revenues via the PFM while solving the FAM with a heuristic branch-and-bound algorithm. Computational results obtained for a large-scale real-life network and various demand levels show that the proposed method can reduce the average computational time by a factor of 2–3 to obtain solutions of similar quality.

Integrated Airline Fleet and Crew Robust Planning

The airline fleet assignment problem involves assigning aircraft to flights to maximize profit. Different fleet assignment solutions cause dramatically different performance in subsequent crew-planning and operational processes. We have developed an integrated fleet and crew robust planning method to provide fleet assignment solutions that are both friendly to crew planning and robust to real-time operations. The three challenges of this work are (1) to understand the influence of fleet assignment on crew scheduling; (2) to address crew scheduling in a tractable way in the integrated model; and (3) to achieve robustness. We address these challenges by developing a new approach that integrates crew connections within the fleet assignment model and imposes station purity by limiting the number of fleet types and crew bases allowed to serve each airport. Computational results demonstrate that the proposed approach can reduce crew-planning cost, improve robustness, and solve industrial-size problems with good computational efficiency.

Airline Fleet Assignment with Enhanced Revenue Modeling

The airline fleet assignment problem addresses the question of how to best assign aircraft fleet types to scheduled flight legs. This paper presents the subnetwork fleet assignment model: a model that employs composite decision variables representing the simultaneous assignment of fleet types to subnetworks of one or more flight legs. The formulation is motivated by the need to better model the revenue side of the objective function. We present a solution method designed to balance revenue approximation and model tractability. Computational results suggest that the approach yields profit improvements over comparable models and that it is computationally tractable for problems of practical size.

Itinerary-Based Airline Fleet Assignment

We consider the airline fleet assignment problem involving the profit maximizing assignment of aircraft types to flight legs. Although several basic formulations have been proposed, important network considerations are insufficiently treated in these formulations and the resulting solutions are often suboptimal. We propose a new formulation and solution approach that captures network effects and generates superior solutions. We quantify the benefits of our proposed approach in a case study using data from a major United States airline.