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Automating the Construction of an Optimal Refuelling Plan along a Car Route Taking into account the Limit on the Number of Stops
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This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton’s equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU.
This research proposes an integrated voyage optimization algorithm that combines quadtree graph generation, visibility graph simplification, Dijkstra’s algorithm, and a 3D dynamic programming method. This approach enables the determination of a minimum distance initial reference route and the creation of a 2D navigational graph for efficient route optimization. We effectively store and process complex terrain information by transforming the GEBCO uniform grid into a quadtree structure. By utilizing a nearest neighbor search algorithm, edges are connected between adjacent ocean nodes, facilitating the generation of a quadtree graph. Applying Dijkstra’s algorithm to the quadtree graph, we derive the shortest initial route and construct a visibility graph based on the waypoints. This results in a simplified reference route with reduced search distance, allowing for more efficient navigation. For each waypoint along the reference route, a boundary is defined angled at 90 degrees to the left and right, based on the waypoint’s reference bearing. A line segment formed by the waypoint and both boundaries is defined as a navigational stage. A navigational graph is defined by connecting adjacent stages. Employing a 3D dynamic programming method on the navigational graph, and incorporating weather forecasting data, including wind, wave, and currents, we search for a route that minimizes fuel oil consumption with ETA restrictions. Our approach is tested on several shipping routes, demonstrating a fuel consumption reduction compared to other voyage optimization routes. This integrated algorithm offers a potential solution for tackling complex voyage optimization problems in marine environments while considering various weather factors.
In this study, we considered the electric power delivery problem when using electric vehicles (EVs) for multiple households located in a remote region or a region isolated by disasters. Two optimization problems are formulated and compared; they yield the optimal routes that minimize the overall traveling distance of the EVs and their overall electric power consumption, respectively. We assume that the number of households requiring power delivery and the number of EVs used for power delivery in the region are given constants. Subsequently, we divide the households into groups and assign the households in each group to one EV. Each EV is required to return to its initial position after delivering electric power to all the households in the assigned group. In the first method, the benchmark method, the optimal route that minimizes the overall traveling distance of all the EVs is determined using the dynamic programming method. However, owing to traffic congestion on the roads, the optimal path that minimizes the overall traveling distance of all the EVs does not necessarily yield their minimum overall electric power consumption. In this study, to directly minimize the overall electric power consumption of all the considered EVs, we propose an optimization method that considers traffic congestion. Therefore, a second method is proposed, which minimizes the overall electric power consumption considering traffic congestion. The electric power consumed during the travel of each EV is calculated as a function of the length of each road section and the nominal average speed of vehicles on the road section. A case study in which four EVs are assigned to deliver electric power to serve eight households is conducted to validate the proposed method. To verify the effectiveness of the proposed method, the calculation results considering traffic congestion are compared with the benchmark method results, which minimizes the traveling distance. The comparison of the results from the two different methods shows that the optimal solution for the proposed method reduces the overall electric power consumption of all the EVs by 236.5(kWh) (9.4%) compared with the benchmark method. Therefore, the proposed method is preferable for the reduction of the overall electric power consumption of EVs.
Predicting and optimizing ship fuel use is a crucial technology for lowering greenhouse gas emissions. Unfortunately, existing research is rarely capable of developing fuel consumption forecasts and optimization models for a particular transport system. This study develops a fuel consumption prediction model based on machine learning and a fuel consumption optimization model based on particle swarm optimization for ships. We studied nearly ten years of big data from a large Korean pure car and truck shipping company (PCTC), which contained 16,189 observations from 2012 to 2021. Results indicate that the XGBoost deep learning model outperforms conventional prediction models at the stage of fuel consumption prediction, with an R² of 0.97. Furthermore, in the fuel consumption optimization stage, the particle swarm optimization method can effectively reduce fuel consumption. This study helps PCTC companies control shipping costs and save energy. Insights for shipping businesses to meet environmental demands are provided as well.
In this 21st century, there has been an increase in the usage of renewable products for the economic drifting of vehicle transportations systems. Furthermore, due to recent trends in climate change, researchers have started focusing on statistical optimization techniques for sustainable vehicle routings. However, until now, a major gap has been noticed in the multidomain statistical analysis for optimizing the parametric levels of the vehicle fuel economy. Therefore, in this research work, two widely utilized cars (Toyota and GMC Yukon) are considered on a particular route of Jeddah for the collection of the fuel economy data under the realistic conditions of air conditioner temperature, traffic patterns, and tire pressure. The outcomes of the factorial design of the experiment highlight that the fuel economy is optimal under the low air conditioner temperature, light traffic patterns, and 34 PSI tire pressure. Three replications of the fuel economy have been considered, and the statistical significance of the correlated variables has been justified by implementing the analysis of variance (ANOVA) approach on the various levels of fuel economy. During the analysis, the statistical hypothesis for random exogenous factors has been developed by incorporating a multivariate regression model. The outcomes highlight that both air conditioner temperature and traffic patterns in Jeddah have a significant negative effect on fuel economy. Results also depict that the effect of air conditioner temperature, traffic patterns, and tire pressure is substantially higher for heavy-engine automobiles such as the GMC Yukon compared to light-engine cars (Toyota Corolla). Furthermore, a normality test has also been considered to validate the outcomes of the proposed model. Therefore, it is highly recommended to utilize the proposed methodology in optimizing the trends of fuel economy for sustainable vehicle routings. Based on the findings of multidomain statistical analysis, it is also highly recommended the utilization of the Toyota Corolla car model for investigating the correlation of external undeniable factors (braking frequency, metrological conditions, etc.) with the trends of vehicle fuel economy.
Unplanned waste collection causes environmental pollution, cost increment and large consumption of fuel. Optimized route planning is one of the most important factors in the smart waste management system (WMS). In this work, an optimal route planning model and algorithm based on Dijkstra’s algorithm are proposed. For the link-cost calculation, real-life parameters such as the bins’ status (fill-levels), road congestion status, distance are considered. Data analysis is done based on the practical scenario. The proposed model is found more cost-effective and Eco-friendly than the conventional model.
The transport activity is one of the most expensive processes in the supply chain and the fuel cost is the highest cost among the cost components of transportation. The goal of the research is to optimize the transport costs in case of a given transport task both by the selecting the optimal petrol station and by determining the optimal amount of the refilled fuel. Recently, in practice, these two decisions have not been made centrally at the forwarding company, but they depend on the individual decision of the driver. The aim of this study is to elaborate a precise and reliable mathematical method for selecting the optimal refuelling stations and determining the optimal amount of the refilled fuel to fulfil the transport demands. Based on the elaborated model, new decision-supporting software is developed for the economical fulfilment of transport trips.
Ежегодно туристско-рекреационные услуги особо охраняемых природных территорий становятся более востребованными. Однако не все особо охраняемые природные территории одинаково эффективно используют свой туристско-рекреационный потенциал. Цель научной работы состоит в оценке туристско-рекреационного потенциала национальных парков на примере Дальневосточного федерального округа. Методическую основу работы составила адаптированная авторами оценка туристско-рекреационного потенциала с использованием методов сравнительного анализа, классификации, статистического, балансового. В работе основное внимание уделяется адаптации методики оценки туристско-рекреационного потенциала, проводится отбор пятидесяти двух показателей, объединённых в шесть блоков: природный, культурно-исторический, социально-экономический, туристский блок, а также блоки неблагоприятных факторов и экологической ситуации. Собраны данные по выбранным критериям и проведена оценка туристско-рекреационного потенциала тринадцати национальных парков Дальневосточного федерального округа. Определены пять национальных парков, имеющих высокий потенциал, четыре – средний и четыре – низкий. Для национальных парков со средним и низким туристско-рекреационным потенциалом проведённая оценка позволила выделить ряд проблем, затрагивающих систему развития туризма. Полученные результаты могут иметь практическую значимость для руководителей федеральных государственных бюджетных учреждений, осуществляющих руководство особо охраняемыми природными территориями.
We present the first randomized polynomial-time simplex algorithm for linear programming. Like the other known polynomial-time algorithms for linear programming, its run- ning time depends polynomially on the number of bits used to represent its input. We begin by reducing the input linear program to a spe- cial form in which we merely need to certify boundedness. As boundedness does not depend upon the right-hand-side vector, we run the shadow-vertex simplex method with a random right-hand-side vector. Thus, we do not need to bound the diameter of the original polytope. Our analysis rests on a geometric statement of indepen- dent interest: given a polytope Ax ≤ b in isotropic posi- tion, if one makes a polynomially small perturbation to b then the number of edges of the projection of the perturbed polytope onto a random 2-dimensional subspace is expected to be polynomial.
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