Three-stage airline fleet planning model

Dožić, Slavica; Kalić, Milica

doi:10.1016/j.jairtraman.2015.03.011

Cited by 45 publications

(15 citation statements)

References 13 publications

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“…These two criteria are chosen because they are subject to changes and they will be varied in the sensitivity analysis. The highest local weights with respect to seat capacity is given to aircraft types ATRs and CRJ 700, which is expected bearing in mind that their capacities meet the estimated number of passengers per flight in the best way (Dožić and Kalić, 2015). The values of local weights are equal to 0.227 (Table 2).…”

Section: Analytic Hierarchy Processmentioning

confidence: 95%

“…The airline's route network consists of 27 routes. According to the estimated demand and route characteristics (Dožić and Kalić, 2015), 8 routes should be covered by small aircraft with capacity up to 100 seats, while the 19 routes should be covered by medium size aircraft with the capacity of 101-200 seats. In this paper, the focus is on the set of routes covered by small aircraft.…”

Section: Model and Datamentioning

confidence: 99%

“…In the AHP, the sensitivity of alternative ratings with respect to different pairwise comparisons of the alternatives is analysed (Dožić and Kalić (2014) analyzed solution sensitivity with respect to different pairwise comparisons of the criteria), while in the even swaps method the objective ranking across alternatives is varied in order to learn solution sensitivity. Dožić and Kalić (2015) developed the three stages airline fleet planning model encompassing approximate fleet mix, fleet sizing and aircraft selection, respectively. Passenger demand and distance are the inputs to the first stage to get an approximate fleet mix in terms of aircraft size.…”

Section: Introductionmentioning

confidence: 99%

“…Using the results obtained from the second stage, the third step is to determine which aircraft type meets the market requirements in the best way. The third stage of the model developed by Dožić and Kalić (2015) is analyzed in this paper, applying two different MCDM methodologies for to the aircraft selection problem.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Two MCDM Methodologies in Aircraft Type Selection Problem

Dožić

Kalić

2015

Transportation Research Procedia

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In order to provide a satisfactory choice while dealing with multiple criteria, planners apply some of the multiple criteria decision making (MCDM) methods as an aid to decision making. As the aircraft type selection embodies inherent multi-criteria decision making, two MCDM methods, the Analytic Hierarchy Process (AHP) and the Even Swaps Method (ESM), are applied and illustrated with the case study of a hypothetical regional airline. These methods are compared, as well as solutions they arrived at. Considering the difference between the AHP and ESM, a sensitivity analysis is carried out in different ways. In the AHP, the sensitivity of alternative ratings in respect to different pairwise comparisons of the alternatives is analysed, showing that the AHP is sensitive to this kind of changing. In the even swaps method, the objective ranking across alternatives is varied showing that the ESM is not sensitive at all. The final priority weight for specific aircraft type decreases with the decrease of the domination measure of one aircraft over another with respect to payment conditions, for all aircraft types in the solutions obtained by AHP.

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Section: Analytic Hierarchy Processmentioning

confidence: 95%

Section: Model and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Two MCDM Methodologies in Aircraft Type Selection Problem

Dožić

Kalić

2015

Transportation Research Procedia

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“…Travel demand and service frequency are found to be two influential factors in fleet planning [5][6][7][8]. In other words, there is a close relationship between the type of aircraft, aircraft quantity, travel demand of passengers, and the service frequency in fleet planning.…”

Section: Introductionmentioning

confidence: 97%

Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase

Teoh

Khoo²

2016

Journal of Optimization

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Essentially, strategic fleet planning is vital for airlines to yield a higher profit margin while providing a desired service frequency to meet stochastic demand. In contrast to most studies that did not consider slot purchase which would affect the service frequency determination of airlines, this paper proposes a novel approach to solve the fleet planning problem subject to various operational constraints. A two-stage fleet planning model is formulated in which the first stage selects the individual operating route that requires slot purchase for network expansions while the second stage, in the form of probabilistic dynamic programming model, determines the quantity and type of aircraft (with the corresponding service frequency) to meet the demand profitably. By analyzing an illustrative case study (with 38 international routes), the results show that the incorporation of slot purchase in fleet planning is beneficial to airlines in achieving economic and social sustainability. The developed model is practically viable for airlines not only to provide a better service quality (via a higher service frequency) to meet more demand but also to obtain a higher revenue and profit margin, by making an optimal slot purchase and fleet planning decision throughout the long-term planning horizon.

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

Wang

Zhu

2016

J of Advced Transportation

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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.

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Three-stage airline fleet planning model

Cited by 45 publications

References 13 publications

Comparison of Two MCDM Methodologies in Aircraft Type Selection Problem

Comparison of Two MCDM Methodologies in Aircraft Type Selection Problem

Fleet Planning Decision-Making: Two-Stage Optimization with Slot Purchase

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

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