A bi-objective dynamic programming approach for airline green fleet planning

Khoo, Hooi Ling; Teoh, Lay Eng

doi:10.1016/j.trd.2014.06.003

Cited by 27 publications

(17 citation statements)

References 30 publications

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“…Such emissions can influence the air quality and contribute to the greenhouse effect. At present, CO 2 emissions from air transport account for 2.0%-2.5% of total global CO 2 emissions by humans [24]. Damage assessment of carbon emissions in air transport, especially in the surface operating environments of airports, has attracted wide attention [25].…”

Section: Multi-objective Optimization Methods Of Taxiing Time Fuel Comentioning

confidence: 99%

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

et al. 2019

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High-efficiency taxiing for safe operations is needed by all types of aircraft in busy airports to reduce congestion and lessen fuel consumption and carbon emissions. This task is a challenge in the operation and control of the airport’s surface. Previous studies on the optimization of aircraft taxiing on airport surfaces have rarely integrated waiting constraints on the taxiway into the multi-objective optimization of taxiing time and fuel emissions. Such studies also rarely combine changes to the airport’s environment (such as airport elevation, field pressure, temperature, etc.) with the multi-objective optimization of aircraft surface taxiing. In this study, a multi-objective optimization method for aircraft taxiing on an airport surface based on the airport’s environment and traffic conflicts is proposed. This study aims to achieve a Pareto optimized taxiing scheme in terms of taxiing time, fuel consumption, and pollutant emissions. This research has the following contents: (1) Previous calculations of aircraft taxiing pathways on the airport’s surface have been based on unimpeded aircraft taxiing. Waiting on the taxiway is excluded from the multi-objective optimization of taxiing time and fuel emissions. In this study, the waiting points were selected, and the speed curve was optimized. A multi-objective optimization scheme under aircraft taxiing obstacles was thus established. (2) On this basis, the fuel flow of different aircraft engines was modified with consideration to the aforementioned environmental airport differences, and a multi-objective optimization scheme for aircraft taxiing under different operating environments was also established. (3) A multi-objective optimization of the taxiing time and fuel consumption of different aircraft types was realized by acquiring their parameters and fuel consumption indexes. A case study based on the Shanghai Pudong International Airport was also performed in the present study. The taxiway from the 35R runway to the 551# stand in the Shanghai Pudong International Airport was optimized by the non-dominant sorting genetic algorithm II (NSGA-II). The taxiing time, fuel consumption, and pollutant emissions at this airport were compared with those of the Kunming Changshui International Airport and Lhasa Gonggar International Airport, which have different airport environments. Our research conclusions will provide the operations and control departments of airports a reference to determine optimal taxiing schemes.

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Section: Multi-objective Optimization Methods Of Taxiing Time Fuel Comentioning

confidence: 99%

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

et al. 2019

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“…A demand constraint was noted in a study by Khoo & Teoh (2014), to ensure that travelers' demand could be met satisfactorily. The demand constraint could be expressed as…”

Section: Review Of Literaturementioning

confidence: 99%

“…A dynamic programming model was adopted to solve as simpler, smaller, sub-problems for each operating period, and determine the optimal solution. When stochastic demand is considered, the probabilistic component captures demand uncertainty, providing a more accurate solution (Khoo and Teoh, 2014).…”

Section: Review Of Literaturementioning

confidence: 99%

An Optimal Airline Revenue Management Seat Pricing Plan Model

Koursaris¹,

Marion²

2018

IJAAA

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“…The study analyzed the choice and introduction mode of aircraft. Khoo and Teoh constructed a bi‐objective dynamic programming approach by incorporating the concept of green fleet as an objective into fleet planning problem.…”

Section: Literature Reviewmentioning

confidence: 99%

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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A bi-objective dynamic programming approach for airline green fleet planning

Cited by 27 publications

References 30 publications

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment

An Optimal Airline Revenue Management Seat Pricing Plan Model

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

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