A decomposition approach to determining fleet size and structure with network flow effects and demand uncertainty. Issue 7 (28th September 2016)

• Record Type: Journal Article
• Authors: Wang, Yu; Zhu, Jinfu; Sun, Hong
• Is Part Of: Journal of advanced transportation. Volume 50:Issue 7(2016)
• Page Start: 1447
• Page End: 1469
• Publication Date: 2016-09-28
• Subjects: air transportation management -- fleet planning -- hub‐and‐spoke network -- network flow effects -- non‐linear programming model -- decomposition algorithm; Transportation -- Periodicals; 388.05
• DOI: 10.1002/atr.1410
• Languages: English
• ISSNs: 0197-6729

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.