Accurate fuel monitoring and tracking are essential for effective fuel management. By installing fuel monitoring systems, organizations can keep a close eye on fuel levels, identify anomalies, and detect instances of fuel theft. Furthermore, integrating fuel tracking systems with GPS technology enables companies to monitor vehicle locations and routes, helping optimize logistics and reduce unnecessary fuel consumption.

Fleet management plays a crucial role in fuel efficiency optimization. It involves utilizing sophisticated software solutions to streamline operations, plan efficient routes, and manage vehicle maintenance schedules.

By employing fuel management software, organizations can centralize data, automate processes, and gain valuable insights to make data-driven decisions that enhance fuel efficiency and reduce costs. Implementing fuel efficiency tips and best practices can significantly impact fuel consumption.

These practices include regular vehicle maintenance, monitoring tire pressure, using lightweight materials, reducing idling time, and promoting eco-driving techniques.

By educating drivers about these practices, organizations can foster a culture of fuel efficiency and improve overall performance. While fuel management systems and fuel efficiency optimization offer substantial benefits, there are challenges and tradeoffs to consider. Implementing advanced technologies and systems requires an initial investment, and maintenance costs should also be taken into account.

Moreover, balancing fuel efficiency with operational requirements, such as meeting customer demands and time constraints, can sometimes present challenges. Organizations must carefully evaluate these tradeoffs and strike a balance that aligns with their specific needs.

Air Traffic Management ATM plays an important role in reducing the environmental impacts of air transportation by reducing the inefficiencies during the operations of an aircraft [ ].

Ryerson et al. In addition the better terminal design can also reduce the fuel consumption. There are a number of ways that airports, airlines and ATM providers can improve the air transportation system to minimize fuel burn and emissions.

These include improving the use of the airspace, air traffic control and operations and further improving the use of airspace and air traffic control includes the flexible use of airspace, route redesign, using the new tools and programmes to find most effective route, and reduced separation between the aircraft.

Salah [ ] developed the model of optimal flight paths taking into consideration jet noise, fuel consumption, constraints and extreme operational limits of the aircraft on approach.

The results of this study showed that, the environmental impacts and fuel consumption are reduced by the use of aircraft trajectory optimization during arrivals.

Beside this there are some constraints to the improved ATM which includes the air traffic controller ATC. ATC prevents the ideal trajectory of the aircraft to be flown due to a number of reasons such as safe separation, congested airspace, restricted airspace, delay management and weather avoidance etc.

The priorities of controller are also taken into the account. For air traffic controller the safety comes first thereafter the performance. Therefore, by optimizing the aviation infrastructure, there is the potential to reduce fuel consumption.

Aviation is the fastest growing sector of the economy. It provides the number of socioeconomic benefits. If these factors are carefully managed then a significant amount of fuel can be saved.

Also the social awareness levels of the society, regarding the impact of the aviation emission on climate change plays a key role in fuel consumption reduction. The strong social pressure sends the signal to the government and the government takes the necessary action after scientifically confirming the problem.

As in the cases of the automobile emission and aircraft noise significant technological and operational improvements have been reported, because the general public was well aware of the health damages caused by these [ 3 ].

Also, the education and awareness are very important social measure in air transport and there will be many airline customers who have never thought of aviation emission as an environmental problem.

Information should be widely available regarding the impact of flying, so that airlines have the background information they need to understand the changing circumstances of aviation. Informed choice is a key component of the transport demand and environmental policy implication.

In addition, the economic and policy measures should be introduced in an incremental fashion to give the air transport and consumers time to adjust to the changes.

Aviation alternative fuels can also play an important for the optimization of aviation fuel consumption. Since the energy crises of the s, all the aircraft companies, aviation sectors, engine companies, and other government organization are working for practicality of using alternative fuel in aircraft.

A viable alternative aviation fuel can stabilize fuel price fluctuation and reduce the reliance from the crude oil. The replacement for current alternative fuels need no aircraft modifications and can be used with the current aviation system, encompassing existing distribution and refueling infrastructure [ ].

In addition the study explored the potential of the alternative aviation fuels: conventional jet fuel from petroleum resources, synthetic jet fuels, biodiesel and bio-kerosene, ethanol and butanol, liquefied natural gas and hydrogen and highlighted the technical feasibility parameters: high energy density, high specific energy, high flash point, low freezing point and vapor pressure, high thermal stability, adequate lubricity, and sufficient aromatic compound content.

Janic [ 17 ]; Pereira et al. the payload were studied. The result of the study shown that, only the hydrocarbons, matched the range vs. payload of Jet-A1 and the limonene was found to fulfill the required specification.

Therefore a suitable alternative fuel can be selected on the basis of a variety of criteria, societal priorities, economic viability, and sustainability considerations, which will further reduce the aviation fuel consumption.

As clearly evident from the literature these dimensions are closely related to each other so care has been taken that, a single decision variable cannot be repeated more than one time under the two different dimensions. Table 1 shows the decision variables based on the identified dimensions and the reviewed literature.

Table 2 shows the number of decision variables of respective dimension and their percentage. From Table 2 it is clear that the A had the highest percentage of decision variables Further, they are classified into three subcategories of each major classification, i.

analytical-conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies. Furthermore analytical- mathematical techniques include the linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms.

Analytical research uses the deductive methods while the empirical research uses the induction method to arrive at conclusions. Analytical-research consists the logical, mathematical, and statistical methods [ ].

Table 3 shows the research methodologies FCO in air transport. In this study, the analytical research includes the case studies for conceptualization, intro-respective research, and conceptual modeling for fuel consumption research in air transport [ 1 , 3 , 4 , 11 , 13 , 14 , 16 , 19 , 21 , 32 , 34 , 38 , 40 , 41 , 43 — 45 , 49 , 50 , 52 — 54 , 57 , 59 , 61 , 73 , 79 — 83 , 87 , 92 , 94 , 96 , 97 , , , , , , , , , , , , , , , , , , , , , , , , , , — , , , , , , , ].

Analytical mathematical research develops the new mathematical relationships between closely defined concepts and uses the simulated data to draw the conclusions [ , ]. Here the analytical-mathematical research for fuel consumption in aviation includes the; fuel burn and emission prediction and forecast for future scenario studies which primarily consist of logical and descriptive modeling [ 2 , 22 , 25 , 29 , 35 , 37 , 42 , 46 — 48 , 55 , 63 — 65 , 67 , 69 , 70 , 72 , 75 — 78 , 84 , 85 , 88 — 91 , 93 , 95 , 98 , 99 , , , , — , , , , , , , , , , , — , , , , , , , ].

Additionally the analytical-mathematical techniques can further be classified into the linear programming [ 24 , 28 , 39 , 62 , , , — , , , , , , , , , , , , , , , , , , ], mixed integer programming [ 12 , , , , ], dynamic programming [ 17 , 75 , 76 , , , , , , , , ], gradient based methods [ 26 , 27 , 30 , 31 , 36 , 51 , 56 , 60 , 71 ], simulation modeling [ 15 , , , , , ], and nature based algorithms [ 10 , 58 , 66 , 68 , , , ].

These techniques mainly deal with the FCO models that are the main thematic area of this study. Each of these techniques has its own strengths and weaknesses and can be helpful in solving certain types of FCO problems. Mathematical programming models have been demonstrated to be useful analytical tools in optimizing decision-making problems such as those encountered in air transport fuel consumption.

Linear programming LP models consist of a linear fuel consumption function which is to be minimized subject to a certain number of constraints [ , ]. Mixed integer programming MIP is applicable when some or all of the variables are restricted to be integers [ ]. Dynamic programming is used when sub problems are not independent and we solve the problem by dividing them into sub problem [ ].

As the aircraft fuel consumption during its operation is not always linear in nature, therefore complex mathematical relationships are used for the FCO.

The mathematical techniques, i. linear programming and MIP may not be very effective in solving real world FCO problems, because of the large number of variables and constraints involved. These are only suitable for solving the FCO problems with limited variables and constraints and also LP require high computer memory and long CPU time in order to process complex mathematical algorithms [ ].

Linear programming has shown to be incapable of describing the actual complexity of realism of FCO models. Also the dynamic programming has the limitations: lack of general algorithms and dimensionality [ ]. Gradient based methods are mainly used for aerodynamic design optimization of aircraft and they minimize the convex differential functions.

Gradient-based methods provide a clear convergence criterion. The limitations of gradient-based methods are; high development cost, noisy objective function spaces, inaccurate gradients, categorical variables, and topology optimization [ ].

This limits their use for global FCO. Simulation modeling in the area of the FCO is used to observe how an aircraft performs, diagnose problems and predict the effect of changes in the aircraft system, evaluates fuel consumption, and suggest possible solutions for improvements.

Simulation techniques can be ideal for reproducing the behaviors of a complex design system of the aircraft. Many previous studies have analyzed the capability of simulation modeling in fuel consumption modeling and optimization [ 15 , , , , , ].

One of the major limitations of simulation techniques is its inability to guarantee optimality of the developed solution. Also the simulation technique is very expansive. The nature based algorithms can be based on swarm intelligence, biological systems, physical and chemical systems [ ].

The researchers have learned from biological systems, physical and chemical systems to design and develop a number of different kinds of optimization algorithms that have been widely used in both theoretical study and practical applications.

Since the nature is the main source of inspiration of these algorithms, so they are called nature based algorithms [ , ]. In FCO problems the nature based algorithms are classified into the genetic algorithm GA , particle swarm optimization PSO , simulated annealing, and immune algorithm.

GA is an evolutionary based stochastic optimization algorithm with general-purpose search methods which simulate the processes in a natural evolution system [ ]. GA is an efficient algorithm with flexibility to search the complex spaces such as the solution space for the global air transport fuel consumption.

GA algorithms are well suited to multi-objective optimization problems because they can handle large populations of solutions [ 58 ].

The advantages of using GA techniques for solving large optimization problems are its ability to solve multidimensional, non-differential, non-continuous, and even nonparametric problems [ ]. Moreover, it solves the problem with multi solutions. GAs has been proven to be a highly effective and efficient tool in solving complex aircraft design, and some of their successful applications in the optimization of fuel consumption models have been proposed in the literature [ 58 , 68 , ].

There are, however, a number of challenges when designing a customized GA procedure to solve a certain FCO problem. The first difficulty is the construction of customized genetic operators to perform the mating process on the chromosomes.

Secondly, designing a constraint handling mechanism is generally a complicated task in order to ensure the effective implementation of the model constraints. In addition, when populations have a lot of subjects, there is no absolute assurance that a genetic algorithm will find a global optimum [ ].

PSO has been extensively used to many engineering optimization areas due to its simple conceptual framework, unique searching mechanism, computational efficiency, and easy implementation [ ]. In order to find the optimal solution, the PSO algorithm simulates the movement of a set of particles in the search space under predetermined rules [ ].

The particles use the experience accumulated during the evolution, for finding the global maximum or minimum of a function [ ]. The PSO algorithm does not require sorting of fitness values of solutions in any process and this might be a significant computational advantage over GA, especially when the population size is large [ ].

Simulated annealing SA is a one of the most common meta-heuristics techniques, and has been successfully applied to solve several types of combinational optimization problems [ ].

The main advantages of SA are; it deals with arbitrary systems and cost functions, relatively easy to code, even for complex problems. But its main disadvantage is that, it cannot tell whether it has found an optimal solution, it requires some complimentary bound [ ].

Pant, R. In case of aircraft fuel consumption, the objective function was found to be highly nonlinear and discontinuous, with several combinations of design variables not having a feasible solution. Hence, gradient-based optimization methods could not be applied to obtain the optimal solution, and the SA approach was adopted [ 66 ].

Ravizza, S. Immune Algorithms are related to the Artificial Immune Systems field of study concerned with computational methods. Immune Algorithms are inspired by the process and mechanisms of the biological immune system. The main advantages of the algorithm are dynamically adjustable population size, combination of local with global search, defined convergence criterion, and the capability of maintaining stable local optimum solutions [ ].

More knowledge about the fuel-based objective function is needed to formulate the combined FCO function. Table 2 shows the list of analytical statistical studies [ 20 , 23 , , , , , , , , ]. Summarily, the main objective of analytical statistical research is to provide, the more cohesive model for empirical statistical testing [ ].

The empirical research methodology uses data from external organizations or businesses to test if relationships hold in the external world [ ].

The empirical-experimental research examines the relationships by manipulating controlled treatments to determine the exact effect on specific dependent variables [ , ]. The main advantage of using the empirical-experimental research is, it may understand and respond more appropriately to dynamics of situations of fuel consumption.

The main purpose of empirical statistical research methodologies is to empirically verify theoretical relationships in larger populations from actual practices for reducing the number of relationships for future application [ , ]. Literature reports the two empirical statistical analyses [ 18 , ], in which fuel consumption models are tested for their reliability and validity.

Lastly, the empirical case study examines the organizations across time and provides the dynamic dimension to theory for promoting the theoretical concepts [ ]. Moreover, the empirical case studies provide new conceptual insights by empirically investigating individual cases of complex fuel consumption relations of the real world.

In general, the nature of data available in the studies reviewed determines the type of meta-analytic method that can be applied. In this paper, we perform summary counts of the determinants of the article studied, fuel prices, and evolution of fuel efficiency trends.

Though this simple meta-analysis provides only descriptive information with no statistics, it is expected to shed greater understanding of the development and evolution of FCO research trends in the air transport industry and to identify potential research areas for further research and for improvement.

Accordingly, we analyzed articles related to FCO research in air transport by 1 Yearly distribution of articles, and evolution of fuel prices and fuel efficiency trends 2 Distribution of research methodologies 3 Journal wise Discipline distribution.

Progresses in literature related to fuel consumption have been started since after —74 Arab oil embargoes. After that, the oil crises fuel conservation and efficiency became the main focus of the aviation industry. Table 4 Yearly distributions of research articles, fuel prices, and evolution of fuel efficiency trends of air transport from to [ ].

The major growth in optimum use of fuel occurred after the Arab oil embargo. During the period —, the oil prices increased sharply and U. economy had focused the need for more fuel efficient transportation [ 98 ]. The first oil shock was in — and the second one in — [ 16 ].

During the period —, the oil prices increased sharply as shown in Table 4 , while the airline jet fuel prices stabilized in compared to sharply rising prices in the three preceding years.

The jet fuel price in rose to about 2. Also increased air travel volume was one more main reason behind the rising fuel prices, because the passengers were relatively unconcerned to the ticket price because the benefits of faster travel and this was a very interesting trend in that period [ 16 ].

Table 4 shows the distribution of research articles during the period — Total number of articles from to were 38 and most of the studies have been found in i. It is clear from the Table 4 , that the numbers of the articles during the first oil shock — were 9 and after first oil shock and second oil shock, they have been increased to Figure 3 shows the yearly distribution of a number of articles and fuel prices.

In the early s, the non OPEC countries had also started production of oil therefore oil consuming counties decreased their oil demand from OPEC countries.

As a result the OPEC production declined after and in response to declining production. Furthermore, Iran and Iraq war, and ceasing of oil production by Saudi Arabia were the main reasons for fuel price decline [ ].

During the period — the US airline jet fuel prices declined from 7. The spike was mainly attributable to the first Gulf War, but the price spike was only for shorter periods [ ]. It is clear from the Fig. The total numbers of articles during this period were Most of the studies have been found in i.

During the period —, the number of articles also decreased as compared to — In the period — the jet fuel prices remained relatively low and stable. During the period —, the fuel prices continuously decreased and they fell from 5.

In oil prices were affected by the Asian financial crisis. But, the Asian economies recovering from the financial crisis, prices increased during The total number of articles from to were 61 and most of the studies have been found in i. The numbers of the articles were more than the last two decades.

During — world aviation fuel consumption and its production increased to a greater extent. The rising demands of countries such as China and India, and political instability in Venezuela, Nigeria, Russia and particularly Middle East have troubled oil supplies and raising prices [ ].

From Fig. In jet fuel prices reached levels more than three times those of While in fuel prices fell from their high, and it all most reached half of fuel prices. This spike and decline in jet fuel prices have demonstrated uncertainty in the magnitude of future fuel prices.

Again, in the jet fuel prices rose by 6. During the period — the numbers of research studies have also been increased. Table 4 shows, the total numbers of articles from to were , which is more than the number of articles than from to Figure 3 shows the increasing trend of number of articles from to and during the same period the oil prices had also increased.

From Table 4 it is observed that the number of published articles between the period and is less 90 articles , than that of the period — Historically, jet fuel prices have been the main driver for improvements in aircraft fuel efficiency [ 16 ].

Table 4 also shows the yearly evolution of the fuel efficiency trends from to Evolution of fuel efficiency trends has explored the four factors.

But, the alternative fuels have only shown the future potential options for fuel efficiency improvement, because of concerns regarding their economic cost of production and the current lack of feedstock availability limits their near term availability of aviation [ ].

So, only the improvement in aircraft fuel efficiency from to were mainly due to technological factors, operational factors, load factors, and aircraft size.

The various trends evolved in the Table 4 are also grouped under these four factors. From Table 4 it is clear that the entire fuel efficiency factor have been evolved continuously from to , as compared to other time span.

According to Grote, [ 14 ] average fuel-efficiency improvement between and was 1. Lee et al. Figure 4 shows the evolution of fuel efficiency trends in US domestic and international aviation from to It is clear from Fig.

Improvements were particularly rapidly during the s, when wide body aircraft came into the service and in the early to mids, when mid-range aircraft like turbofan 2nd and third generation entered into the service.

The flattening slope of the fuel burns curve in Fig. Evolution of fuel efficiency trends in US domestic and international aviation from to [ ]. Table 5 shows the percentage distribution of research methodologies for the FCO research in air transport.

Also, the near about haft of the research methodologies are from the analytical- conceptual, logical, and descriptive modeling. These studies mainly include the; fuel burn and emission calculation, prediction and forecast for future scenario. It is also observed from Table 5 that the optimization modeling research techniques, i.

The journal wise the number of FCO research articles in international journals is computed and the same is shown in Table 6. Since the numbers of articles against many journals are few to get some simple inferences, the research journals reviewed are grouped with respect to disciple wise, i.

Accordingly, distribution of articles of journals discipline wise is computed and shown in Fig. It is observed that the Transportation TP related journals have far the most articles i. This indicates that TP is a major important field affecting the FCO in air transport.

Followed to TP related journals, the Aerospace sciences AS are having more articles on FCO research. This could be due to the fact most of the articles reported in TP are also related to road and rail transport and those were not included in the study.

This could be due to the low correlation between the objectives of various studies reported on FCO research in air transport and scope of the respective journals. It is known that the history of the FCO research is not long compared with other industries.

To the best of our knowledge, so far, no attempt has been made to classify and analyze the literature dealing with FCO with air transport research. Thus, in this paper we have attempted to review and classify the FCO research.

Accordingly, an extensive literature review has been attempted from various journals and web based articles that are possible outlets for this research. This resulted in the identification of articles from 69 journals by year of publication, journal, and topic area based on the two classification schemes related to FCO research, published between, to December- Also, this study have explained the six categories of FCO research methodologies analytical - conceptual, mathematical, statistical, and empirical-experimental, statistical, and case studies and optimization techniques linear programming, mixed integer programming, dynamic programming, gradient based algorithms, simulation modeling, and nature based algorithms.

From the matching of published articles according to our proposed classification schemes and according to performance metric, it seems there are considerable untouched research problems in FCO research. Over the last four decades, the significance of FCO at tactical and operational levels has been recognized by academics and practitioners as a competitive advantage for the better performance of airlines.

This study reviewed the state of the art in optimization modeling of fuel consumption. Our findings have some important conclusions of FCO research and suggest the following directions for future research in the area:. We classified the current literature into four dimensions based on the degree of complexity and identified 98 decision variables affecting the FCO.

This classification of dimensions and their respective decision variables could be of potential value to future researchers in the field and is also capable of further refinements.

These parameters, if addressed, could result in a consistent, and comparable database of the FCO research. We also conclude that the performance measures. Although real FCO models emphasize a variety of performance measures in practice—none of FCO models allow for this variety.

A second classification was also presented in the paper based on the research methodologies and techniques used for tackling the proposed fuel consumption problems. One perpetual concern is the development of appropriate research approaches for tackling large fuel consumption and optimization problems.

Hence, there is a need to further extend the effectiveness of the existing solution techniques to be capable of handling realistic FCO problems with large numbers of variables and constraints.

Heuristics and meta-heuristic techniques are still the dominant solution techniques in the literature of FCO [ 10 , 58 , 66 , 68 , , , ]. Genetic algorithms GAs , particle swarm optimization PSO , simulated annealing SA , and immune algorithm IA , has been recognized by several researchers as the most promising techniques.

There is still a need to further extend the effectiveness of the existing research methodologies and to test the new arrivals such as Ant Colony Optimization ACO , Bee Colony Optimization BCO techniques, and Firefly optimization techniques FA.

As far as the empirical statistical research methodology is concerned, it verifies models for their empirical validity in larger populations to reduce the number of relationships in future research, while the nature based algorithms has been considered the most powerful tool for optimization.

More research could be done on this issue. Therefore, we observed that, the combination of empirical statistical methodology followed by analytical- mathematical nature based algorithms could be of potential research methodologies to future researchers in the field.

It is observed that the number of published articles between the period and is less 90 articles , so we can conclude that there are articles which appeared in various journals and other publication sources in the area of FCO since The prices of jet fuel have significantly increased since the If air transport improves their fuel efficiency in response to increase in jet fuel prices, then some of the increases in the cost of air travel can be reduced.

Technological improvements will take a long time for development, while the operational change is most near-term, could lead to significant reduction in air fares in the face of much higher oil price, but it may not achieve a significant option given the fast increase in air travel demand.

Also the study has evolved the various trends of aircraft technological factors, and operational factors for fuel efficiency improvement. These factors could be potential options for the FCO. Also an important outcome of the analysis of trends in literature output was that we noticed clear parallels between interest in FCO research and global occurrences related to the oil and energy industry, whether social, political or economic, whether scheduled or sudden; whether positive or degrading to the energy sector.

And hence, ultimately, oil prices seem closely related to interest in the FCO. In addition a total of articles were classified according to our classifications. We analyzed the identified articles from the 69 journals by year of publication, journal, and topic area. This particular analysis could provide guidelines for the pursuit of future research on FCO and its applications by explaining the chronological growth of aviation fuel efficiency over the years, the challenging areas of fuel efficiency improvement and application, and the major issues surrounding environmental impact, fuel prices, and competitions among the airlines.

Finally, we acknowledge that this review cannot be claimed to be exhaustive, but it does provide a reasonable insight into the state-of-the-art on FCO research. Future work will concentrate on the development of an appropriate information framework for FCO research in air transport.

After that, this informational framework should be checked for reliability and validity. This leads to the development of a structural model of fuel consumption in the air transport industry and further knowing the relationships among the variables an optimization model will be constructed.

Furthermore, this study will also provide the base for fuel conservation, energy efficiency, and emission reduction As CO 2 emission are proportional to aircraft fuel burn in the aviation sector.

This study might have some limitations. Readers should be cautious in interpreting the result of this study, since the findings are based on the data collected only from the international journal articles.

Only, the 69 journal of these disciplines were included in the study. There might be other academic journal which may be able to provide a more comprehensive picture of the articles related to the application of the FCO in air transport.

Lastly, non-English publications were excluded from this study. We believe research regarding the application of FCO techniques have also been discussed and published in other languages.

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