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

Abstract

From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation.

Adaptive Elitist Genetic Algorithm With Improved Neighbor Routing Initialization for Electric Vehicle Routing Problems

“…Let k i e be the current energy level of EV-k at customer i. Then, the difference of k i e and k j e should be larger than the needed electric energy from customer i to customer j, which can be expressed as constraint (7). Fifthly, one EV's battery capacity at customer i should be enough for it to return directly to the depot or return to the depot after serving customer j, which can be expressed as constraint (8), where k io b and k jo b are, respectively, the electric energy consumptions of EV-k from customers i and j to the depot.…”

“…The fifth one makes sure that all EVs have enough battery power to complete the delivery services. Before traveling to the next customer, the EV's battery should be calculated to satisfy the constraints (7) and (8); otherwise, it should be recharged. To make these two constraints effective, the coefficients M1 and M2 are set very large, e.g., The NR-EGA uses the inverse of the cost function (11) as every individual's fitness function ( ) fit i to carry out the selection, crossover, and mutation operations.…”

“…In transportation, electric vehicles (EV) are better for energy saving and reducing pollutants than combustion-engine vehicles, and many countries require EV to be used in their transportation systems. Therefore, electric vehicle routing problems (EVRPs) have been the subject of many studies in recent years [4][5][6][7].…”

Selecting Freight Transportation Modes in Last-Mile Urban Distribution in Pamplona (Spain): An Option for Drone Delivery in Smart Cities

“…The consideration of environmental criteria in urban logistic decision making is gaining momentum. As a first attempt, the traditional vehicle routing problem (VRP) can be modified to consider air pollution in the objective function [4]. These models are based on the idea of reducing the energy consumption will lead to a reduction of air pollutants emissions, as firstly described in Erdoĝan and Miller-Hooks [26].…”

“…Considering only freight distribution, road transport is the largest subsector causing air pollution [2,3]. For that reason, the consideration of energy/fuel consumption and/or CO 2 /greenhouse gas emissions in transport related optimization problems has gained much importance in recent years [4,5]. Nonetheless, the objective of reducing air pollution usually conflicts with other ones such as economic or social welfare objectives.…”