# Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities

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## Abstract

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## 1. Introduction

## 2. Research Methodology

#### 2.1. Research Protocol

#### 2.2. Implementation of the Research Protocol

#### 2.3. Analysis and Results

## 3. Energy Requirements in Modern Transportation Systems

## 4. Road Transportation

- The first strategy is based on greening the objective function of the well-known vehicle routing problem (VRP) and its many variants [36,37,38]. That is, the traditional objective function related to minimization of distances is turned into an energy-related one. Examples include the pollution routing problem and the energy minimizing VRP. These problems are fundamentally similar because they take into account decision variables related to energy consumption when performing logistics operations. A special case of this strategy is the green VRP, which maintains the traditional distance-based objective function but also incorporates vehicles using alternative sources of energy [39]. Moreover, most of the reviewed papers do not directly include an energy optimization scope. Instead, they usually refer to the minimization of CO${}_{2}$ emissions. These emissions, as well as other greenhouse gases, are released to the environment with fuel burn. Then, minimizing the CO${}_{2}$ emissions is equivalent, in many situations, to optimizing the energy consumption (i.e., fuel burn).
- The second strategy is the improvement of the load factors while maintaining the traditional distance-based objective function. In this broad strategy, back-hauling (including pick-up and delivery applications) is the most illustrative example [40]. The main goal is to avoid empty trucks covering long distances while returning to their depots. If this can be achieved, the process gains efficiency, and energy consumption is reduced.
- The third strategy is based on horizontal cooperation in road freight transportation. Cooperation practices represent an efficient way of promoting environmentally friendly policies and optimizing the energy use for freight mobility [41].
- Finally, the aforementioned strategies can be combined, thus leading to more effective approaches for energy optimization.

#### 4.1. Strategies Based on Greening the Objective Function

#### 4.2. Strategies Based on Improving Load Factors

#### 4.3. Strategies Based on Horizontal Cooperation

#### 4.4. Mixed Strategies

## 5. Rail Transit Systems

#### 5.1. Energy-Efficient Train Driving

- Genetic Algorithms (GAs): GAs constitute the most widely used metaheuristic method according to an analysis conducted by Fernández et al. [83]. Chang and Sim [96] were among the first authors to propose a GA to solve the energy-efficient train driving problem. The goal is to create a coast control lookup table, which would dictate the operation of an ATO system by providing feedback on when to power-up, coast, or brake. The optimization problem minimizes the overall energy consumption by also taking into account the punctuality and rider discomfort as penalty functions. Han et al. [97] considered a similar problem, where the goal is to identify the minimum energy-consuming schedule for a train which is driven in automatic operation mode along a non-constant gradient and with speed limits. The author also proposed a GA and compared it (in terms of energy consumption) to the exact algorithm in [98]. The algorithms were tested on Seoul’s metro subway system. The results show a better performance of the GA in terms of energy consumption. Wong and Ho [99] proposed a GA for a dynamic, real-time, and coasting-control problem. They compared the GA to three exact methods and provided a comprehensive discussion about the pros and cons of each method. Lechelle and Mouneimne [100] developed a GA that minimizes energy consumption while adhering to some additional constraints regarding the train and track characteristics as well as operational constraints. The GA, which was applied to a tramway network of Rouen (France), shows about 7% energy savings compared to the current approach used by the system. Ma et al. [101] also proposed a GA to minimize energy consumption, but with the additional consideration of running time, which is introduced as a penalty cost to the optimization problem. The algorithm was tested on an urban train system, and shown to reduce energy consumption by almost 11%. Sicre et al. [102] combined a GA with fuzzy parameters for energy-efficient manual driving. The fuzzy parameters help to capture the uncertainty in manual driving operations. The objective of the GA is to find a solution with a target running time and minimal energy requirements. The algorithm was implemented on a real Spanish high-speed train line, and shown to provide an improvement of about $6.7$% over the traditional manual-driving method. Chevrier et al. [103] proposed a bi-objective evolutionary algorithm that yields a set of solutions optimizing both the running time and energy consumption. The compromise between the running time and energy consumption is summarized using a Pareto curve. The algorithm was tested on two case studies, where significant energy savings were achieved as a result. Considering a train as a continuous rigid particle chain, Lin et al. [104] devise da multi-point combinatorial optimization problem with the consideration of multi-point coasting control. A GA is used to solve this problem using data from the Shanghai metro.
- Numerical Simulations: Domínguez et al. [105] developed a comprehensive simulation model of the Madrid’s metro system to determine the optimal speed profiles. The simulation model yields a Pareto curve, which helps to determine the most energy-efficient speed profile given the running time of the train. The approach was shown to yield about 13% energy savings, and resulted in the redesign of the current ATO speed profiles. Domínguez et al. [106] further extended this study to consider regenerative breaking in the substations. Energy savings from $6.19$% to $10.62$% are reported. Dominguez et al. [107] considered the possibility of storing the regenerated energy and feeding it back to the train by means of an on-board energy-saving device. The new model shows about 20% energy savings compared to the current practice.
- Ant Colony Optimization: Ke et al. [108] considered a block-layout design between successive stations for mass rapid transit systems. The energy saving problem is formulated as a combinatorial optimization problem, which is then solved by ant colony optimization. Ke et al. [109] further extended the previous algorithm to consider a moving-block system metro line, which can perform online optimization with further energy savings of up to $19.4$% compared to the former algorithm.
- Tabu Search: Motivated by the inaccuracy in speed tracking of ATO drivers, Liu et al. [110] proposed two tabu search algorithms for the energy-efficient train problem. The resulting algorithms were tested on two case studies conducted with the Beijing subway. The first case study shows about $8.93$% energy reduction, while the second case study shows $2.54$% energy reduction. The main advantage of both algorithms is their speed, i.e., they both provide a good solution within 1 s, which makes them suitable for online control of trains.
- Artificial Neural Networks: Chuang et al. [111] proposed an artificial neural network for the energy-efficient driving problem with two objectives: minimizing energy usage and traveling time of passengers. A two-layer network was tested on the Kaohsiung (Taiwan Island) transit system and shown to provide energy savings with a small increase in the passengers’ travel time.
- Particle Swarm Optimization (PSO): Domínguez et al. [112] presented a multi-objective PSO algorithm, which considers both the running time and the energy consumption as objectives. This algorithm, which is based on the accurate simulation of the ATO and train motion, leads to a Pareto curve that compromises between the running time and the energy consumption. The algorithm is shown to provide better computational time compared to a non-dominated sorting GA. Considering the two main uncertainties in a train operation, i.e., the train load and the delays in the line, Fernández-Rodríguez et al. [113] developed a model that designs robust and efficient speed profiles that can be integrated into an ATO system. The model also takes into account the running time and energy consumption as objectives. The proposed procedure has the following steps: in the first step, a Pareto curve of all possible speed profiles that are robust to passenger load variations is obtained; and, in the second step, a PSO algorithm, which also takes into account energy efficiency and possible delays, is used to select the speed profiles from the robust Pareto frontier. This procedure was tested on a case study, showing that the inclusion of the delay factor provides energy savings of up to 14%.

#### 5.2. Energy-Efficient Train Timetabling

- Genetic Algorithms: Albrecht [115] was among the first to present a GA to solve a model that reduces power peaks and energy consumption to control the running time of trains with given headway and synchronization times. Controlling power peaks is directly related to better utilizing the regenerative energy. This situation is explained in the following lines: if the power peak is not controlled, then the voltage of the overhead contact line (that transfers the energy) reaches its maximum level and the regenerative braking is replaced by mechanical braking (which causes the energy to be lost as heat energy). Chen et al. [116] considered the scheduling of multiple trains, so that the total traction power load can be evenly used and, hence, peak power consumption can be reduced. The experiments illustrate that GA does a good job in providing an optimal solution with reduced maximum traction power. There are also papers that examine direct approaches to the better utilization of regenerative energy. Nasri et al. [117] optimized the scheduling of multiple trains in order to better utilize the regenerative energy by taking into account the effect of headway and reserve times on the amount of energy consumption. The proposed GA is shown to reduce the energy consumption up to 14%. Fournier et al. [118] developed a model to modify the dwell time of metro trains, so that accelerations and braking are better synchronized and regenerative energy is utilized. The model is solved with a hybrid algorithm (that combines linear programming with a GA), showing up to 6% energy savings. Yang et al. [119] formulated an integer programming model that maximizes the overlap time between successive trains with headway time and dwell time control, such that accelerating and braking synchronization is better achieved. They used a GA to solve this model and tested the algorithm on six case studies using data from the Beijing subway in China. The results show a 22% increase in the overlap time during the peak hours, and about 15% increase in the overlap time during the off-peak hours. Yang et al. [120] further extended their previous model to also minimize passenger waiting times and coordinate up and down trains at the same station, so that regenerative energy is better utilized. A two-objective integer programming model is developed and solved with a GA, which is shown to save energy by $8.86$% and reduce passenger waiting times by $3.22$%. Yang et al. [121] further extendrf their previous works by considering more realistic situations. For example, up and down trains located in the same electric supply interval are considered, and the operation time is extended to one day. They developed an integer programming model whose goal is to fully utilize the regenerative energy while taking into account the aforementioned considerations. A GA, combined with an allocation algorithm, is devised to solve this model. The resulting algorithm is shown to improve the utilization of regenerative energy by $36.16$% and to reduce total energy consumption by $4.28$%. Li and Yang [122] considered stochastic delay times and running times. They proposed a stochastic cooperative scheduling approach with the goal of maximizing the utilization of regenerative energy. Again, a GA is shown to provide energy savings of about 15%. More recently, Yang et al. [123] presented a multi-objective timetable optimization approach that integrates energy consumption, passenger waiting time, and robustness (defined as eliminating the effect of delays). The resulting model is solved with a non-dominated sorting GA, which was tested on a real-life dataset obtained from the Beijing metro. The results show that this approach can decrease total energy consumption by $2.1$%, while improving the passenger waiting time by $15.8$% and robustness by $24.81$%.
- Simulated Annealing: Zhao et al. [124] formulated an optimization model with two objectives, overlapping time (as a measure of regenerative braking energy utilization) and total passenger time. They designed a simulated annealing algorithm to solve the optimal timetable. Experiments, which were conducted with real-life data from Hong Kong, show that the resulting algorithm reduces the overlapping time by $21.9$%, as well as the total passenger time by $4.3$%. Le et al. [125] developed a nonlinear integer programming model to maximize the utilization of regenerative braking energy. A simulated annealing algorithm is used to solve the model, which is shown to provide improvements between 4% and 12% in the utilization of regenerative braking energy.
- Swarm Intelligence Algorithms: Liu et al. [126] presented a timetable optimization problem that maximizes the use of regenerative braking energy by considering dwell time and headway time as decision variables. They proposed an improved artificial bee colony optimization algorithm and compared its performance with the CPLEX solver and a GA.

#### 5.3. Integrated Approaches

## 6. Maritime Transportation

#### 6.1. General Speed Reduction

#### 6.2. Weather Routing

#### 6.3. Fuel Consumption Monitoring

## 7. Air Transportation

#### 7.1. Aircraft Trajectory Optimization

#### 7.2. Airport Ground Operations

- Taxiway Optimization: This is also referred to as the ground movement problem, and is mostly focused on minimizing total taxi time of one or more aircrafts on the airport surface [176]. Ravizza et al. [167] were the first to study the trade-off between taxi time and fuel consumption during taxiing. A bi-objective optimization problem is developed, which simultaneously minimizes the taxi time and fuel consumption. A routing and scheduling problem, called the k-quickest path problem with time windows, is solved with a population-based algorithm, which allows the analysis of the trade-off between taxi time and fuel consumption. This algorithm performed well with real-life data from the Zurich airport. The primary result is that this trade-off is very sensitive to how the fuel-based objective function is modeled. With the goal of speeding up the approach of Ravizza et al. [167], Weiszer et al. [177] proposed a multi-component optimization problem that combines the ground movement problem and the speed profile optimization problem. Two objective functions are considered: minimization of total taxi time and minimization of fuel consumption. The goal of the ground movement problem is to route aircrafts from one destination to another in a fuel efficient manner, while adhering to the constraints of other aircrafts around them. The speed profile optimization problem, on the other hand, aims at finding the optimal speed profiles in four different phases (acceleration, traveling at constant speed, braking, and rapid braking), while minimizing taxiing time and fuel burn simultaneously. Tianci et al. [168] decomposed the ground movement problem and the speed profile optimization problem, so that the results on the former can be fully utilized. An approach utilizing PSO is presented. The resulting heuristic is shown to generate fast and efficient solutions. Chen et al. [178] introduced a decision-making framework that integrates the efficient speed profiles generated in [179] into a routing and scheduling framework. The goal here is to minimize the total time and the fuel burnt. The resulting framework was tested using data from the Zurich and Manchester airports, showing a 9% and 50% reduction in time and fuel consumption, respectively.
- Runway Scheduling: Runway scheduling encompasses two problems: ‘the aircraft landing problem (ALP)’ and ‘the aircraft take-off problem’. The goal is to determine the sequence of landing or taking-off aircrafts on a runway or multiple runways, while optimizing certain objectives subject to given constraints [180]. Focusing on the ALP, Mesgarpour et al. [169] considered a multi-objective optimization model with the goal of minimizing average delay, maximizing runway throughput, and minimizing fuel cost. Since the resulting problem is NP-hard, the authors suggested the use of a heuristic method. Later, Mesgarpour [181] compared the performance of dynamic programming, iterated descent, and simulated annealing algorithms for the static ALP, using data from the Heathrow airport. The optimization model minimizes extra fuel cost arising from trying to achieve the desired landing schedule, as well as costs related to violations of time windows regarding landing times. It also tries to maximize runway throughput. All three algorithms are found to perform well, but the iterated descent algorithm is found to be computationally faster. The iterated descent is also the preferred approach for the dynamic version of the problem. Recently, Rodríguez-Díaz et al. [182] proposed a multi-objective ALP with the goal of maximizing runway capacity and also minimizing the fuel consumption and noise levels. The Pareto frontier is explored via the $\u03f5$-constraints method. Using real-life data, a $4.5$% reduction in fuel consumption was achieved.
- Integrated Approach: Since airport ground operations are related to each other, it is important to consider an integrated approach, which takes into account multiple sub-problems and the interactions among them. The sub-problems are difficult to solve already, making their combination an even more challenging problem. Therefore, heuristics have been utilized in the literature for the integrated problems. For example, Weiszer et al. [170] considered three airport ground operations simultaneously: the ground movement problem, the runway scheduling problem, and the airport-buses scheduling problem. They proposed an integrated optimization problem, which seeks to minimize the total time, fuel consumption, and bus scheduling cost. The resulting problem is solved with a GA. Weiszer et al. [183] further enriched this model by incorporating preferences of the decision maker to a multi-objective optimization model, thus facilitating the generation of the Pareto frontier.

## 8. Discussion

## 9. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**2018**, 89, 1–14. [Google Scholar] [CrossRef] - Calvet, L.; de Armas, J.; Masip, D.; Juan, A.A. Learnheuristics: Hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Math.
**2017**, 15, 261–280. [Google Scholar] [CrossRef] - Calvet, L.; Ferrer, A.; Gomes, M.I.; Juan, A.A.; Masip, D. Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation. Comput. Ind. Eng.
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**Figure 2.**(

**a**) Energy consumption (in billion toe) by the end user. (

**b**) Compound annual growth rate (data from [28]).

**Figure 3.**(

**a**) Energy consumption (in billion toe) in transportation by region. (

**b**) Energy consumption in transportation by fuel type.

Entity | Entity Type | Report | Reference |
---|---|---|---|

International Energy Agency | Official | World Energy Outlook | [27] |

U.S. Energy Information Administration | Official | International Energy Outlook | [28] |

BP | Company | BP Statistical Review of World Energy | [29] |

ExxonMobil | Company | Outlook for Energy | [30] |

WWF | NGO | The Energy Report | [31] |

Resources for the future | NGO | Global Energy Outlook | [32] |

World | EU-28 | USA | China | OECD | Non OECD | |
---|---|---|---|---|---|---|

Industry | 31.7 | 24.7 | 19.1 | 54.9 | 24.1 | 39.3 |

Transport | 31.6 | 30.7 | 45.1 | 16.5 | 37.5 | 22.3 |

Other | 36.7 | 44.6 | 35.8 | 28.6 | 38.4 | 38.4 |

Strategy Category | Greening the Objective Function | Improving Load Factors | Horizontal Cooperation |
---|---|---|---|

Description | Optimization of an energy related objective function (e.g., CO${}_{2}$ emissions, fuel consumption). | Better utilization of the vehicle capacity using back-hauling or pick-up and delivery applications. | Cooperation among companies, sharing a fleet of vehicles for the delivery and pick-up operations. |

Key references | Bektaş and Laporte [42] | Santos et al. [40] | Serrano-Hernández et al. [41] |

**Table 4.**Relevant literature in energy-related road freight transportation optimization based on solution approach (E, Exact; H, Heuristic) and strategy adopted (GOF, Green objective function; ILF, Improving load factors; HC, Horizontal cooperation).

Solution | Strategy | |||||
---|---|---|---|---|---|---|

Reference | Year | E | H | GOF | ILF | HC |

Granada-Echeverri et al. [56] | 2019 | ✔ | ✔ | |||

Hooshmand and MirHassani [49] | 2019 | ✔ | ✔ | |||

Kirci [51] | 2019 | ✔ | ✔ | |||

Belloso et al. [63] | 2019 | ✔ | ✔ | |||

Lu and Yang [82] | 2019 | ✔ | ✔ | ✔ | ||

Fernández et al. [70] | 2018 | ✔ | ✔ | |||

de Oliveira da Costa et al. [55] | 2018 | ✔ | ✔ | |||

Reil et al. [60] | 2018 | ✔ | ✔ | |||

Serrano-Hernandez et al. [75] | 2018 | ✔ | ✔ | |||

Eshtehadi et al. [45] | 2017 | ✔ | ||||

Huang et al. [50] | 2017 | ✔ | ✔ | |||

Androutsopoulos and Zografos [53] | 2017 | ✔ | ✔ | |||

Xiao and Konak [54] | 2017 | ✔ | ✔ | |||

Belloso et al. [64] | 2017 | ✔ | ✔ | |||

Quintero-Araujo et al. [73] | 2017 | ✔ | ✔ | |||

Fukasawa et al. [44] | 2016 | ✔ | ✔ | |||

Koç and Karaoglan [48] | 2016 | ✔ | ✔ | |||

Ehmke et al. [52] | 2016 | ✔ | ✔ | |||

Quintero-Araujo et al. [74] | 2016 | ✔ | ✔ | |||

Küçükoğlu and Öztürk [59] | 2015 | ✔ | ✔ | |||

Lai et al. [61] | 2015 | ✔ | ✔ | |||

Pérez-Bernabeu et al. [71] | 2015 | ✔ | ✔ | |||

Zachariadis et al. [79] | 2015 | ✔ | ✔ | ✔ | ||

Davis et al. [57] | 2014 | ✔ | ✔ | |||

Contardo and Martinelli [68] | 2014 | ✔ | ✔ | |||

Küçükoğlu and Öztürk [66] | 2014 | ✔ | ✔ | |||

Pan et al. [77] | 2014 | ✔ | ✔ | |||

Juan et al. [81] | 2014 | ✔ | ✔ | ✔ | ||

Pradenas et al. [78] | 2013 | ✔ | ✔ | ✔ | ||

Paraphantakul et al. [65] | 2012 | ✔ | ✔ | |||

Bektaş and Laporte [42] | 2011 | ✔ | ✔ | |||

Bailey et al. [80] | 2011 | ✔ | ✔ | ✔ | ||

Ballot and Fontane [76] | 2010 | ✔ | ✔ | |||

Kara et al. [43] | 2007 | ✔ | ✔ |

**Table 5.**Relevant literature in energy-related railway transportation optimization based on the employed heuristic method (GA, Genetic Algorithm; NS, Numerical Simulation; ACO, Ant Colony Optimization; TS, Tabu Search; ANN, Artificial Neural Networks; PSO, Particle Swarm Optimization; SA, Simulated Annealing; SIA, Swarm Intelligence Algorithm; DP, Dynamic Programming) and strategy adopted.

Reference | Heuristic | Strategy | Additional Considerations | ||
---|---|---|---|---|---|

Eco Driving | Timetable Optimization | Integrated Approach | |||

Chang and Sim [96] | GA | ✔ | The model includes punctuality and rider discomfort | ||

Han et al. [97] | GA | ✔ | The constraints include non-constant gradient, curve, and speed limits | ||

Wong and Ho [99] | GA | ✔ | The model includes robustness to changing service demand | ||

Lechelle and Mouneimne [100] | GA | ✔ | Train and track characteristics and operational constraints are considered | ||

Ma et al. [101] | GA | ✔ | The model also includes constraints on running time | ||

Sicre et al. [102] | GA | ✔ | Uncertainty in manual driving operations are also considered | ||

Chevrier et al. [103] | GA | ✔ | The model also optimizes running time | ||

Lin et al. [104] | GA | ✔ | Multi-point coasting control is allowed in the model | ||

Domínguez et al. [105] | NS | ✔ | Trade-off between energy efficiency and running time is considered | ||

Domínguez et al. [106] | NS | ✔ | Regenerative braking is considered in the model hypotheses | ||

Dominguez et al. [107] | NS | ✔ | The model assumptions allow for storing the regenerative energy and reusing it | ||

Ke et al. [109] | ACO | ✔ | Online optimization is possible in the considered models | ||

Liu et al. [110] | TS | ✔ | The paper includes two TS algorithms that are both computationally fast | ||

Chuang et al. [111] | ANN | ✔ | The model minimizes traveling time of passengers | ||

Domínguez et al. [112] | PSO | ✔ | The model minimizes running time | ||

Fernández-Rodríguez et al. [113] | PSO | ✔ | The model considers running time, being robust to passenger load variations | ||

Albrecht [115] | GA | ✔ | The model reduces power peaks | ||

Chen et al. [116] | GA | ✔ | The model reduces power peaks making better utilization of energy | ||

Nasri et al. [117] | GA | ✔ | The model involves the effect of headway and reserve time on energy usage | ||

Fournier et al. [118] | GA | ✔ | This paper explains how to make a better utilization of regenerative energy | ||

Yang et al. [119] | GA | ✔ | The model also includes headway and dwell time control | ||

Yang et al. [120] | GA | ✔ | The model also minimizes passenger waiting time | ||

Li and Yang [122] | GA | ✔ | The model also considers stochastic delay time and running time | ||

Yang et al. [123] | GA | ✔ | The model also considers minimization of passenger waiting time and delays | ||

Zhao et al. [124] | SA | ✔ | The model also considers the minimization of passenger waiting time | ||

Liu et al. [126] | SIA | ✔ | The model considers the headway and dwell time control | ||

Yong et al. [128] | GA | ✔ | A two-level hierarchical model is considered | ||

Yang et al. [129] | GA | ✔ | Total travel time is also considered | ||

Li and Lo [130] | GA | ✔ | Integrated approach is shown to improve upon solo approaches | ||

Su et al. [132] | DP & SA | ✔ | Passenger demand, headway, cycle time, and trip time are considered | ||

Huang et al. [133] | GA & PSO | ✔ | Dwelling time is also considered |

Strategy | |||
---|---|---|---|

Reference | Year | Speed | Weather Conditions |

Wang et al. [135] | 2018 | ✔ | |

Rehmatulla et al. [138] | 2017 | ✔ | |

Raza and Khosravi [145] | 2016 | ✔ | |

Beşikçi et al. [146] | 2016 | ✔ | ✔ |

Lindstad et al. [147] | 2011 | ✔ | |

Psaraftis and Kontovas [12] | 2013 | ✔ | |

Wang et al. [140] | 2015 | ✔ | |

Fagerholt et al. [148] | 2010 | ✔ | |

De et al. [149] | 2019 | ✔ | |

Padhy et al. [150] | 2008 | ✔ | |

Wen et al. [151] | 2017 | ✔ | |

Meng et al. [152] | 2016 | ✔ | ✔ |

Azaron and Kianfar [153] | 2003 | ✔ | |

Shao et al. [154] | 2012 | ✔ | |

Wang and Meng [155] | 2012 | ✔ | |

Brouer et al. [156] | 2013 | ✔ |

**Table 7.**Relevant literature in energy-related air transportation optimization based on the employed heuristic method (GA, Genetic Algorithm; SA, Simulated Annealing; EA, Evolutionary Algorithm; PAIA, population adaptive immune algorithm; PSO, Particle Swarm Optimization; DP, Dynamic Programming) and the strategy adopted.

Airport Ground Operations | |||||
---|---|---|---|---|---|

Reference | Heuristic | Trajectory Optimization | Taxiway Optimization | Runway Scheduling | Integrated Approach |

Pervier et al. [162] | GA | ✔ | |||

Celis et al. [163] | GA | ✔ | |||

Zhang et al. [164] | GA | ✔ | |||

Bouttier et al. [165] | SA | ✔ | |||

Ho-Huu et al. [166] | EA | ✔ | |||

Ravizza et al. [167] | PAIA | ✔ | |||

Tianci et al. [168] | PSO | ✔ | |||

Mesgarpour et al. [169] | DP & SA | ✔ | |||

Weiszer et al. [170] | GA | ✔ |

Reference | Mode | Short Description | Impact |
---|---|---|---|

de Oliveira da Costa et al. [55] | Road | Green Vehicle Routing Problem | $-15.22$% |

Kara et al. [43] | Road | Green Vehicle Routing Problem | $-16.00$% |

Pradenas et al. [78] | Road | Back-hauling & Horizontal Cooperation | $-30.00$% |

Ballot and Fontane [76] | Road | Horizontal Cooperation | $-25.00$% |

Franke et al. [88] | Rail | Speed Optimization | $-30.00$% |

Ke et al. [109] | Rail | Speed Optimization | $-19.40$% |

Yang et al. [123] | Rail | Timetable Optimization | $-2.00$% |

Fagerholt et al. [148] | Maritime | Speed Optimization | $-21.00$% |

Wang et al. [140] | Maritime | Speed Optimization | $-19.04$% |

Wang et al. [135] | Maritime | Consideration of navigation conditions | $-28.00$% |

Celis et al. [163] | Air | Trajectory Optimization | $-17.20$% |

Weiszer et al. [170] | Air | Taxiway & Runway Scheduling Optimization | $-19.00$% |

Rodríguez-Díaz et al. [182] | Air | Runway Scheduling Optimization | $-4.50$% |

Chen et al. [178] | Air | Taxiway Optimization | $-50.00$% |

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## Share and Cite

**MDPI and ACS Style**

Corlu, C.G.; de la Torre, R.; Serrano-Hernandez, A.; Juan, A.A.; Faulin, J.
Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities. *Energies* **2020**, *13*, 1115.
https://doi.org/10.3390/en13051115

**AMA Style**

Corlu CG, de la Torre R, Serrano-Hernandez A, Juan AA, Faulin J.
Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities. *Energies*. 2020; 13(5):1115.
https://doi.org/10.3390/en13051115

**Chicago/Turabian Style**

Corlu, Canan G., Rocio de la Torre, Adrian Serrano-Hernandez, Angel A. Juan, and Javier Faulin.
2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities" *Energies* 13, no. 5: 1115.
https://doi.org/10.3390/en13051115