Optimizing the Procurement of Aviation Fuels

Austin, Larry M.; Hogan, William W.

doi:10.1287/mnsc.22.5.515

Cited by 16 publications

(4 citation statements)

References 5 publications

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“…Joosung J. Lee [31] suggested the social pressure and public awareness for fuel burn and emission reduction. Austin & Hogan [51] optimized the procurement of aviation fuel for defense industry. D. N. Dewees and L. Waverman [21] studied the recent government policies and fuel taxes for the fuel conservation of airline and transport sector.…”

Section: Socio-economic and Politicalmentioning

confidence: 99%

Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling

Singh¹,

Sharma

2014

Eur. Transp. Res. Rev.

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Introduction The article aims to evolve the base for fuel consumption optimization (FCO) in Indian air transport industry. The objective of this paper is to design the methodology and to develop five facet model of fuel consumption optimization (FCO). Limited researches have been conducted to explore influencing factors for FCO in air transport industry. To fill this gap, this study proposes the model of FCO, and investigates key factors affecting FCO. Methodology The research steps included exploratory factor analysis, confirmatory factor analysis, and testing of structural model. In the first stage exploratory factor analysis (EFA) was used to provide the grouping of variables underline the complete set of item based upon the strong correlation. In the second stage, a confirmatory factor analysis (CFA) was used to specify and estimate of one or more hypothetical models of factor structure, each of which propose a set of latent variables to account for covariance within a set of observed variables. In the third stage, we used the Structural Equation Modeling (SEM) technique and empirically tested the relationships between fuel consumption optimization and aircraft operations Conclusions and future work This study has provided empirical justification for the proposed research framework which describes the relationships between FCO and its dimensions. This has developed an integrated model of FCO, with the purposes of identifying the key factors affecting the FCO. The knowledge of relationship among variables can lead to frame objective function, constraints, and set of equations pertaining situations with regard to Indian scenario. To constitute the equations the data of identified critical factors with regard to Indian scenario can be utilized which will lead to develop optimization based model for fuel consumption that leaves the scope for further study. This study produces the results which represent the base for optimum solution of fuel consumption on which future researchers can target.

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Section: Socio-economic and Politicalmentioning

confidence: 99%

Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling

Singh¹,

Sharma

2014

Eur. Transp. Res. Rev.

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“…Single objective models aim at minimizing a total cost that can be composed of direct costs (purchasing price, price breaks and inventory holding) or some penalties related to quality or shortages, for example. Some of the techniques used to solve such problems are: mixed integer linear programming (Austin and Hogan, 1976; Chaudhry et al , 1993; Ghodsypour and O’Brien, 2001; Crama and Torres, 2004), non-linear programming (Hong and Hayya, 1992; Rosenblatt et al , 1998) and dynamic programming (Kingsman, 1986). Multi-objective models consider the tradeoff between different and often conflicting objectives such as the minimization of costs and the maximization of quality and timely delivery.…”

Section: Research Backgroundmentioning

confidence: 99%

A new weighted fuzzy programming model for supplier selection and order allocation in the food industry

Kaviani

Peykam

Khan

et al. 2020

JM2

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Purpose The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context. Design/methodology/approach First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier. Findings The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions. Originality/value The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

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“…Stanley et al [14], Austin and Hogan [16], Bender et al [17] and Chaudhry et al [18] also used mixed integer programming. Chaudhry et al [18] used linear and mixed integral programming methods to determine the process of cost minimization.…”

Section: Mathematical Programmingmentioning

confidence: 99%

The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation

Kuo

Pai

Lin

et al. 2015

Applied Mathematics and Computation

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Optimizing the Procurement of Aviation Fuels

Cited by 16 publications

References 5 publications

Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling

Evolving base for the fuel consumption optimization in Indian air transport: application of structural equation modeling

A new weighted fuzzy programming model for supplier selection and order allocation in the food industry

The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation

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