A decomposition approach to determining fleet size and structure with network flow effects and demand uncertainty

Abstract

Summary This paper presents a new methodology to determine fleet size and structure for those airlines operating on hub‐and‐spoke networks. The methodology highlights the impact of stochastic traffic network flow effects on fleet planning process and is employed to construct an enhanced revenue model by incorporating the expected revenue optimization model into fleet planning process. The objective of the model is to find a feasible allocation of aircraft fleet types to route legs using minimum fleet purchasing cost, thus ensuring that the expected fleet profit is maximized subject to several critical resource constraints. By using a linear approximation to the total network revenue function, the fleet planning model with enhanced revenue modeling is decomposed into the nonlinear aspects of expected revenue optimization and the linear aspects of determining fleet size and structure by optimal allocation of aircraft fleet types to route legs. To illustrate this methodology and its economic benefits, an example consisting of 6 chosen aircraft fleet types, 12 route legs, and 57 path‐specific origin‐destination markets is presented and compared with the results found using revenue prorated fleet planning formulation. The results show that the fleet size and structure of the methodology proposed in this paper gain 211.4% improvement in fleet profit over the use of the revenue prorated fleet planning approach. In addition, comparison with the deterministic model reveals that the fleet size and structure of this proposed methodology are more adaptable to the fluctuations of passenger demands. Copyright © 2016 John Wiley & Sons, Ltd.

Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time

“…Wang et al included feet size and allocation of various types of aircraft to air routes in an HS network. For the objective function, the net beneft of the feet was maximized by accounting for the least feet purchasing cost of six types of aircraft [24]. Hsu and Wen investigated the efect of various aircraft types of diverse capacities on maximizing the benefts of code-share alliance agreements for airlines using an interactive biobjective model [7].…”

“…Yang and Chiu [15] Air trafc congestion in hub airports Mohri et al [16] Te real capacity of hub airports using envelope curves Teymourian et al [18] Predetermining an alternative hub in the disruption situation Passengers Carmona-Benítez et al [19] Estimation of passenger demand using an econometric dynamic model Kawasaki [20] Scheduling efect on the demand side and the number of passengers Yang [21] Hub location and fight route planning under seasonal demand variations Airlines Martin and Román [5] Airline's HLP through a spatial competition game in two phases Eiselt and Marianov [22] Te efect of competition between two airlines in an HLP Wen and Hsu [23] Te efect of code-share alliance agreements on designing HS networks Wang et al [24] Maximizing the net benefts through feet size and feet assignment Hsu and Wen [7] Maximizing the benefts through code-share alliance agreements for airlines Wei and Hansen [25] Minimizing airline operational costs through feet planning and airline service frequency Sina Mohri et al [26] Fleet planning to locate optimal global hubs 4…”

Optimization Model and Algorithm Design for Airline Fleet Planning in a Multiairline Competitive Environment

“…holds that an airline's choice of aircraft size and flight frequency largely depends on the network-wide distribution of the airline's passenger demand. However, few studies [7][8][9] inversely consider that an airline's choice of aircraft size and flight frequency in a competitive environment is also greatly affecting the airline capability of capturing passenger demand. 𝑆 curve presented by Simpson [19] in the last century is considered as one of most famous functions around the world for evaluating airline market share, which is defined as a ratio of captured demand for an airline to the total demand in the market.…”

“…Symbol 𝑜 denotes the total number of airlines on the route. Previous route-based approaches must predict an airline's demand in advance and then use the proper number of flights provided by different types of aircraft to determine the airline's fleet size and structure while satisfying its predicted demand [7][8][9]. This process obviously neglects the impact of the choice of aircraft size and flight frequency on the airline captured passenger demand.…”

“…Sun et al applied a similar model to analyze the Chinese market [8]. Wang et al presented a new fleet planning approach for those airlines operating in Hub and Spoke network, in which network effects are highlighted [9]. Wei and Hansen discussed the competitive relationship between aircraft size and flight frequency using game theory.…”