To see the other types of publications on this topic, follow the link: Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning.
Journal articles on the topic 'Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Guo, Mei Ni. "Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem." Applied Mechanics and Materials 365-366 (August 2013): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.194.
Full textAbstract:
mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.
2
Qi, Dingding, Yingjun Zhao, Zhengjun Wang, Wei Wang, Li Pi, and Longyue Li. "Joint Approach for Vehicle Routing Problems Based on Genetic Algorithm and Graph Convolutional Network." Mathematics 12, no. 19 (2024): 3144. http://dx.doi.org/10.3390/math12193144.
Full textAbstract:
The logistics demands of industries represented by e-commerce have experienced explosive growth in recent years. Vehicle path-planning plays a crucial role in optimization systems for logistics and distribution. A path-planning scheme suitable for an actual scenario is the key to reducing costs and improving service efficiency in logistics industries. In complex application scenarios, however, it is difficult for conventional heuristic algorithms to ensure the quality of solutions for vehicle routing problems. This study proposes a joint approach based on the genetic algorithm and graph convolutional network for solving the capacitated vehicle routing problem with multiple distribution centers. First, we use the heuristic method to modularize the complex environment and encode each module based on the constraint conditions. Next, the graph convolutional network is adopted for feature embedding for the graph representation of the vehicle routing problem, and multiple decoders are used to increase the diversity of the solution space. Meanwhile, the REINFORCE algorithm with a baseline is employed to train the model, ensuring quick returns of high-quality solutions. Moreover, the fitness function is calculated based on the solution to each module, and the genetic algorithm is employed to seek the optimal solution on a global scale. Finally, the effectiveness of the proposed framework is validated through experiments at different scales and comparisons with other algorithms. The experimental results show that, compared to the single decoder GCN-based solving method, the method proposed in this paper improves the solving success rate to 100% across 15 generated instances. The average path length obtained is only 11% of the optimal solution produced by the GCN-based multi-decoder method.
3
Gao, Jia, Xiaojun Zheng, Feng Gao, Xiaoying Tong, and Qiaomei Han. "Heterogeneous Multitype Fleet Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in Flexible Manufacturing System." Machines 10, no. 3 (2022): 197. http://dx.doi.org/10.3390/machines10030197.
Full textAbstract:
In this study, we present and discuss a variant of the classical vehicle routing problem (VRP), namely the heterogeneous multitype fleet green automated guided vehicle (AGV) routing problem with time windows (HFGVRPTW) applied in the workshops of flexible manufacturing systems (FMS). Specifically, based on the analysis of AGV body structure and motion state, transport distance and energy consumption are selected as two optimization objectives. According to the characteristics and application context of the problem, this paper designs a hybrid genetic algorithm with large neighborhood search (GA-LNS) considering the farthest insertion heuristic. GA-LNS is improved by increasing the local search ability of genetic algorithm to enhance the solution optimal quality. Extensive computational experiments which are generated from Solomon’s benchmark instances and a real case of FMS are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results reveal that compared with using the traditional homogeneous fleet, the heterogeneous multitype AGV fleet transportation mode has a huge energy-saving potential in workshop intralogistics.
4
Chen, Chien-Ming, Shi Lv, Jirsen Ning, and Jimmy Ming-Tai Wu. "A Genetic Algorithm for the Waitable Time-Varying Multi-Depot Green Vehicle Routing Problem." Symmetry 15, no. 1 (2023): 124. http://dx.doi.org/10.3390/sym15010124.
Full textAbstract:
In an era where people in the world are concerned about environmental issues, companies must reduce distribution costs while minimizing the pollution generated during the distribution process. For today’s multi-depot problem, a mixed-integer programming model is proposed in this paper to minimize all costs incurred in the entire transportation process, considering the impact of time-varying speed, loading, and waiting time on costs. Time is directional; hence, the problems considered in this study are modeled based on asymmetry, making the problem-solving more complex. This paper proposes a genetic algorithm combined with simulated annealing to solve this issue, with the inner and outer layers solving for the optimal waiting time and path planning problem, respectively. The mutation operator is replaced in the outer layer by a neighbor search approach using a solution acceptance mechanism similar to simulated annealing to avoid a local optimum solution. This study extends the path distribution problem (vehicle-routing problem) and provides an alternative approach for solving time-varying networks.
5
Mrinmoyee, Chattoraj, and Udaya Rani.V Dr. "A SOFT COMPUTING APPROACH FOR SMOOTH TRAFFIC FLOW ON ROAD NETWORK." International Journal of Research - Granthaalayah 5, no. 4 (2017): 311–19. https://doi.org/10.5281/zenodo.573002.
Full textAbstract:
Route planning has an important role in navigation systems. In order to select an optimized route the traveller has to take various factors into consideration. Traffic congestion is an important factor which needs to be considered while route planning. As the numbers of vehicles are increasing on the road the traffic congestion is also increasing in an exponential manner. In a congested area the best approach to search for a route is to select an alternative path so that we can reach our destination and indirectly save some time. In the recent years route planning system has become an important area of research since the number of vehicles are increasing day-by-day but the traffic structure is un-expandable. In this paper a genetic algorithm is proposed to develop an alternate route which results in smooth flow of traffic. Genetic Algorithm’s main aim is to create an optimized path.
6
Yang, Lin, Qinghua Chen, Junjie Mu, Tangying Liu, Xiaoxiao Li, and Shuxiang Cai. "Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm." Biomimetics 10, no. 5 (2025): 285. https://doi.org/10.3390/biomimetics10050285.
Full textAbstract:
Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the capacitated multi-ship replenishment path planning problem (CMSRPPP). The proposed methodology employs a three-stage optimization framework: (1) initial path generation via parallel execution of the CW and ACO; (2) population initialization for the GA by strategically combining optimal solutions from ACO and the CW with randomized solutions; (3) iterative refinement using an enhanced GA featuring an embedded evolutionary reversal operation for local intensification. To evaluate performance, the SHOA is benchmarked against ACO, the GA, the particle swarm optimization algorithm, and the simulated annealing algorithm for the capacitated vehicle routing problem. Finally, the SHOA is applied to diverse CMSRPPP instances, demonstrating high adaptability, robust planning capabilities, and promising practical potential.
7
Zhang, Qinglong, Naifu Deng, Yanwen Zhu, and Zhenping Huang. "Multidepot Two-Echelon Vehicle Routing Problem for Earthwork Allocation Optimization." Mathematical Problems in Engineering 2022 (January 29, 2022): 1–14. http://dx.doi.org/10.1155/2022/8373138.
Full textAbstract:
Prior to the construction of most engineering projects, earthwork is a complex and time-consuming task, requiring iterative operations in civil engineering. The effectiveness of earthworks determines the cost of many AEC (architecture, engineering, and construction) projects (e.g., road, embankment, railway, and slope engineering). As a result, creating effective earthwork planning is critical. The earthwork allocation problem is simplified in this study to the vehicle route problem (VRP), which is often studied in the field of transportation and logistics. An optimization model for the earthwork allocation path based on the modified genetic algorithm with a self-adaptive mechanism is developed to work out the global optimal hauling path for earthwork. The findings of the study are also used to shape the basic topographic shape of the Winter Olympic Skiing Course Project. Furthermore, a comparative study with the former methods is conducted to validate the performance of our proposed method on tackling such a multidepot two-echelon vehicle routing problem. Because of its flexibility, this optimization model is extremely compatible with various evolutionary methods in many fields, making future development viable and practicable.
8
Chen, Lei, Haiyan Ma, Yi Wang, and Feng Li. "Vehicle Routing Problem for the Simultaneous Pickup and Delivery of Lithium Batteries of Small Power Vehicles under Charging and Swapping Mode." Sustainability 14, no. 16 (2022): 9883. http://dx.doi.org/10.3390/su14169883.
Full textAbstract:
Due to the national policy of encouraging the development of power exchange modes, the reasonable planning of vehicle distribution paths to meet the demand of lithium battery power exchange points has become a topic of considerable research interest. In this study, we propose the “centralized charging + unified distribution” power exchange mode for optimizing the charging and transporting of lithium batteries. Considering lithium batteries are dangerous goods, the vehicle path problem of simultaneous pickup and delivery of lithium batteries with vehicle load and soft time window constraints is studied. The model objective is to minimize the transportation risk and total cost of delivery. By performing crossover and mutation operations on the initial solutions generated by the ant colony algorithm, a hybrid ant colony genetic algorithm (ACO-GA) is designed to solve the model. The results of ACO-GA are compared with the GA, ACO, and SAA methods using the Solomon dataset; the results show that the optimized ant colony algorithm can achieve a smaller total cost in solving the model. Finally, taking a lithium battery leasing business in Company A, we determine the optimal path under different preferences by setting different weights for distribution cost and transportation risk in the model transformation, which provides a reference for the company to select the distribution route. Thus, the model provides a reference for companies that intend to develop power exchange businesses.
9
Albalawneh, Da’ad Ahmad, and Mohamad Afendee Mohamed. "Evaluation of Using Genetic Algorithm and ArcGIS for Determining the Optimal-Time Path in the Optimization of Vehicle Routing Applications." Mathematical Problems in Engineering 2022 (September 27, 2022): 1–20. http://dx.doi.org/10.1155/2022/7769951.
Full textAbstract:
Transportation is regarded as one of the most important issues currently being researched; this issue needs the search for approaches or processes that might lessen many contemporary traffic concerns. Congestion, pollution, and accidents have escalated lately, negatively impacting urban environments, economic development, and citizens’ lifestyles. The rise of illnesses and epidemics throughout the world, such as COVID-19, has created an urgent need to find the best way to save people’s lives. The vehicle routing problem (VRP) is a well-known moniker for improving transportation systems and is regarded as one of the ancient and contemporary difficulties in route planning applications. One of the main tasks of VRP is serving many customers by determining the optimal route from an initial point to a destination on a real-time road map. The best route is not necessarily the shortest-distance route, but, in emergency cases, it is the route that takes the least fitness cost (time) and the fastest way to arrive. This paper aims to provide an adaptive genetic algorithm (GA) to determine the optimal time route, taking into account the factors that influence the vehicle arrival time and cause delays. In addition, the Network Analyst tool in ArcGIS is used to determine the optimal route using real-time map based on the user’s preferences and suggest the best one. Experimental results indicate that the performance of GA is mainly determined by an efficient representation, evaluation of fitness function, and other factors such as population size and selection method.
10
Saputra, Arie, and Desi Fadhilah. "Optimasi Jalur Distribusi Menggunakan Pendekatan Algoritma Genetika." Jurnal Optimalisasi 10, no. 2 (2024): 244. https://doi.org/10.35308/jopt.v10i2.10601.
Full textAbstract:
Management and planning of transportation distribution channels are important in increasing the company's operational efficiency. Optimal distribution arrangements can reduce transportation costs and time, as well as increase product competitiveness in the market. One method used to determine the best distribution route is the Traveling Salesman Problem (TSP), which helps companies achieve maximum efficiency. This research was carried out at the AMDK CV company. Tirta Naga Lestari (TNL) as a case study to analyze product distribution to 11 locations in Aceh. Unplanned product distribution causes high shipping costs. A genetic algorithm (GA) approach is used to find the optimal distribution route. Previous research shows that GA effectively solves complex transportation problems, such as Vehicle Routing Problems (VRP) and transportation scheduling. This research is a development of the use of the GA method for multiple routing case studies where a route repetition process occurs. This is interesting because previous studies only focused on developing GA methods for single or multiple routing cases but there was no route repetition process (looping). This research aims to determine the optimal distribution path for cases with multiple routing and repetition of the same route. The different constraints of the solved cases require a more adaptive GA approach. From the results obtained, it is proven that it can minimize total distribution costs of IDR 2,900,000,- with an efficiency of 37.52%. This study is proven to be able to use the GA approach to solve multiple routing problems with route repetition, thus helping to find optimal route solutions that are more efficient for product distribution.
11
Kouretas, Konstantinos, and Konstantinos Kepaptsoglou. "Planning Integrated Unmanned Aerial Vehicle and Conventional Vehicle Delivery Operations under Restricted Airspace: A Mixed Nested Genetic Algorithm and Geographic Information System-Assisted Optimization Approach." Vehicles 5, no. 3 (2023): 1060–86. http://dx.doi.org/10.3390/vehicles5030058.
Full textAbstract:
Using Unmanned Aerial Vehicles (UAVs), commonly referred to as “drones”, as a supplementary mode for last-mile deliveries has been a research focus for some years now. Motivation lies in the reduced dependency on Conventional Vehicles (CVs) and fossil fuels and in serving remote areas and underprivileged populations. We are building a flexible, modular framework for integrated CV-UAV parcel delivery operations planning that is responsive to infrastructure and demand and offers an open and practical tool for future adaptations. The entire model and solution methodology are practical tools for decision making and strategic planning, with novelties such as the variable Launch Site types for Launch and Recovery Operations (LAROs), the tailored Assignment and Routing Optimization nested GA, the consideration of airspace restrictions of any shape and size, the inclusion of GIS tools in the process, the modularity of the platform, and most importantly, the inclusion of all the above in a single, comprehensive, and holistic approach. Because of the need for safe UAV deployment sites and the high presence of restricted airspace zones in urban environments, the intended field of application is assumed to be the delivery of small packages in rural and under-connected areas, the execution of inter-city deliveries, and the expansion of a city’s original service range. A single CV is equipped onboard with UAVs, while special locations, such as Remote Depots (RDs) with UAVs and Virtual Hubs (VHs) for UAV deployment facilitation, are introduced. The framework considers the presence of Restricted Zones (RZs) for UAV flights. Part of the methodology is implemented in a GIS environment, taking advantage of modern tools for spatial analysis and optimal path planning. We have designed a tailored nested GA method for solving the occurring mode assignment and vehicle routing optimization problems and have implemented our workflow on a devised case study with benchmark characteristics. Our model responds well to unfavorable network types and demand locations, while the presence of RZs notably affects the expected solution and should be considered in the decision-making process.
12
Huang, Min, and Ping Ding. "An Improved Ant Colony Algorithm and Its Application in Vehicle Routing Problem." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/785383.
Full textAbstract:
Optimal path planning is an important issue in vehicle routing problem. This paper proposes a new vehicle routing path planning method which adds path weight matrix and save matrix. The method uses a new transition probability function adding the angle factor function and visibility function, while setting penalty function in a new pheromone updating model to improve the accuracy of the route searching. Finally, after each cycle, we use 3-opt method to update the optimal solution to optimize the path length. The results of comparison also confirm that this method is better than the traditional ant colony algorithm for vehicle routing path planning method. The result of computer simulation confirms that the method can plan a more rational rescue path focused on the real traffic situation.
13
Ding, Xiaoyin, Jun Zhou, Jian Cai, et al. "Based on Particle Group Algorithm of Route Planning for Transportable Charging Station." Journal of Physics: Conference Series 2025, no. 1 (2021): 012044. http://dx.doi.org/10.1088/1742-6596/2025/1/012044.
Full textAbstract:
Abstract Aiming at the problem of path planning for transportable charging stations, a site selection model and path planning model for dedicated routes for transportable charging stations are proposed. The path planning problem model includes the vehicle path problem and the dynamic path problem, and the path planning model is emphatically studied. Considering the network optimization model of chargers and transportable charging station paths, by establishing a path planning model, setting parameters and models, using particle swarm algorithm to calculate the path planning model of chargers and mobile charging stations (Vehicle Routing Problem Considering Stationary and Movable Charging Points (referred to as “VRP-SMCP”) is solved, and the feasibility and effectiveness of the proposed model and algorithm is verified through comparative analysis of calculation examples, and the optimal path planning under the condition of minimizing cost is obtained.
14
WU, S., and C. CHEN. "MULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM." Latin American Applied Research - An international journal 48, no. 3 (2018): 151–56. http://dx.doi.org/10.52292/j.laar.2018.218.
Full textAbstract:
In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.
15
WANG, Mengqin, and Qiyue XIE. "Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm." Mechanics 29, no. 3 (2023): 235–42. http://dx.doi.org/10.5755/j02.mech.31954.
Full textAbstract:
Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.
16
Li, H. H., H. R. Fu, and W. H. Li. "Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation." Computer Optics 45, no. 1 (2021): 154–60. http://dx.doi.org/10.18287/2412-6179-co-732.
Full textAbstract:
With the development of economy, the distribution problem of logistics becomes more and more complex. Based on the traffic network data, this study analyzed the vehicle routing problem (VRP), designed a dynamic vehicle routing problem with time window (DVRPTW) model, and solved it with genetic algorithm (GA). In order to improve the performance of the algorithm, the genetic operation was improved, and the output solution was further optimized by hill climbing algorithm. The analysis of example showed that the improved GA algorithm had better performance in path optimization planning, the total cost of planning results was 31.44 % less than that of GA algorithm, and the total cost of planning results increased by 11.48 % considering the traffic network data. The experimental results show that the improved GA algorithm has good performance and can significantly reduce the cost of distribution and that research on VRP based on the traffic network data is more in line with the actual situation of logistics distribution, which is conducive to the further application of the improved GA algorithm in VRP.
17
Zhou, Jianxu. "Research on multi-path optimization problem based on particle swarm optimization algorithm." Theoretical and Natural Science 43, no. 1 (2024): 156–61. http://dx.doi.org/10.54254/2753-8818/43/20240857.
Full textAbstract:
The problem of finding the optimal path within a certain range exists in various scenarios in our lives, such as the traveling salesman problem, robot automatic path selection problem, vehicle and pedestrian navigation, game path navigation, communication routing, logistics and transportation planning problems, etc. , through the optimization of path problems, we can help people improve resource utilization, improve work efficiency, reduce production costs, or complete the goal of a specific scenario, etc. Therefore, solving the path optimization problem is a very important task in our reality. In order to improve the game experience of the players in the game, this paper studies how to use the particle swarm optimization method to solve the optimal route of NPC.
18
Ma, Fangfang. "Multimedia Urban Road Path Optimization Based on Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (April 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/7898871.
Full textAbstract:
In order to study multimedia urban road path optimization based on genetic algorithm, a dynamic path optimization based on genetic algorithm is proposed. Firstly, for the current situation of traffic congestion, time constraints are strictly considered based on the traditional hard time window logistics distribution vehicle scheduling problem model. Then, the mathematical model is established, and the optimal solution is solved by the combination of decomposition coordination algorithm and genetic algorithm. We divide multiple customers into different customer groups and determine the service object order of each express car in each customer group, so as to obtain the most valuable scheduling scheme. Finally, in the process of solving the model, the relevant and reliable distribution basis for enterprise distribution is collected, including customer geographical coordinates, demand, delivery time window, unit cost required for loading and unloading, loading and unloading time, and penalty cost to be borne by distribution enterprises after early arrival and late arrival. Using the improved genetic algorithm, the optimal solution of each objective function is actually obtained in about 140 generations, which is faster than that before the improvement. Using the genetic algorithm based on sequence coding, a hybrid genetic algorithm is constructed to solve the model problem. Through the comparative analysis of experimental data, it is known that the algorithm has good performance, is a feasible algorithm to solve the VSP problem with time window, and can quickly obtain the vehicle routing scheduling scheme with reference value.
19
Kareem, Abbas Abdulrazzaq, Mohamed Jasim Mohamed, and Bashra Kadhim Oleiwi. "Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 905–15. http://dx.doi.org/10.11591/eei.v13i2.6020.
Full textAbstract:
The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.
20
Zeng, Xinzhu, and Yiding Wang. "Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm." Journal of Physics: Conference Series 2555, no. 1 (2023): 012014. http://dx.doi.org/10.1088/1742-6596/2555/1/012014.
Full textAbstract:
Abstract Logistics distribution is a collection of interrelated organizations and facilities. There is a waste of cost and time in many links. Therefore, it is particularly important to use information technology to improve distribution efficiency. Under the constraints of delivery vehicle cost and time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines the global search ability of the genetic algorithm (GA) and the simulated annealing algorithm (SA) with strong local search ability to solve the vehicle routing problem with time windows (VRPTW). The perturbation factor is introduced to improve the local search, and the crossover method is optimized to obtain more efficient genetic operators by using population information. In this paper, combined with the actual application case, the simulation experiment is carried out in MATLAB. The experimental results show that, compared with the traditional genetic algorithm and simulated annealing algorithm, the total cost of the improved genetic simulated annealing algorithm is reduced by about 15%, which provides a more suitable vehicle route planning scheme.
21
Han, Xue. "Path Planning Algorithm for the Multiple Depot Vehicle Routing Problem Based on Parallel Clustering." Scientific Programming 2023 (April 25, 2023): 1–12. http://dx.doi.org/10.1155/2023/7588595.
Full textAbstract:
It is necessary to study the problem of vehicle routing in multidistribution centers to improve the speed, time, and cost thereof. It is preferable to use as few vehicles as possible to complete the delivery of goods and minimize the total mileage. With the development of artificial intelligence technology, machine learning is usually used to solve the problem of k shortest paths in multiple distribution centers. User needs are constantly changing; the iterative convergence speed of traditional machine learning methods is low and cannot meet the requirements of path planning in a big-data environment. Aiming at difficult problems in multipath planning, the parallel characteristics of traditional machine learning algorithms are fully exploited; k-means clustering and simulated annealing algorithms are improved through the distributed computing; and the multiple depot vehicle routing problem clustering analysis and path planning under the framework of Spark distributed computing are proposed. Through 30 simulation experiments on the TSPLIB dataset, the optimal solution is obtained with a 100% accuracy rate in problem solving. Experimental comparison and analysis show that the algorithm proposed in this article can solve the problem at least twice as fast as other parallel algorithms. This finding verifies that this method can effectively solve the multipath planning problem, thus greatly improving the quality and efficiency of path planning in large-scale logistics.
22
Parsons, Tyler, Farhad Baghyari, Jaho Seo, Byeongjin Kim, Mingeuk Kim, and Hanmin Lee. "Surveillance Unmanned Ground Vehicle Path Planning with Path Smoothing and Vehicle Breakdown Recovery." Applied Sciences 14, no. 16 (2024): 7266. http://dx.doi.org/10.3390/app14167266.
Full textAbstract:
As unmanned ground vehicles (UGV) continue to be adapted to new applications, an emerging area lacks proper guidance for global route optimization methodology. This area is surveillance. In autonomous surveillance applications, a UGV is equipped with a sensor that receives data within a specific range from the vehicle while it traverses the environment. In this paper, the ant colony optimization (ACO) algorithm was adapted to the UGV surveillance problem to solve for optimal paths within sub-areas. To do so, the problem was modeled as the covering salesman problem (CSP). This is one of the first applications using ACO to solve the CSP. Then, a genetic algorithm (GA) was used to schedule a fleet of UGVs to scan several sub-areas such that the total distance is minimized. Initially, these paths are infeasible because of the sharp turning angles. Thus, they are improved using two methods of path refinement (namely, the corner-cutting and Reeds–Shepp methods) such that the kinematic constraints of the vehicles are met. Several test case scenarios were developed for Goheung, South Korea, to validate the proposed methodology. The promising results presented in this article highlight the effectiveness of the proposed methodology for UGV surveillance applications.
23
Chai, Huo, Ruichun He, Changxi Ma, Cunjie Dai, and Kun Zhou. "Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load." Discrete Dynamics in Nature and Society 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9685125.
Full textAbstract:
Mathematical models for path planning and vehicle scheduling for logistic distribution of hazardous materials in full container load (FCL) are established, with their problem-solving methods proposed. First, a two-stage multiobjective optimization algorithm is designed for path planning. In the first stage, pulse algorithm is used to obtain the Pareto paths from the distribution center to each destination. In the second stage, a multiobjective optimization method based on Nondominated Sorting Genetic Algorithm II (NSGA-II) is designed to obtain candidate transport paths. Second, with analysis on the operating process of vehicles with hazardous materials in FCL, the vehicle scheduling problem is converted to Vehicle Routing Problem with Time Windows (VRPTW). A problem-solving method based on estimation of distribution is adopted. A transport timetable for all vehicles based on their transport paths is calculated, with participation of the decision-makers. A visual vehicle scheduling plan is presented for the decision-makers. Last, two examples are used to test the method proposed in this study: distribution of hazardous materials in a small-scale test network and distribution of oil products for sixteen gas stations in the main districts of Lanzhou city. In both examples, our method is used to obtain the path selection and vehicle scheduling plan, proving that validity of our method is verified.
24
Elgarej, Mouhcine, Mansouri Khalifa, and Mohamed Youssfi. "Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems." International Journal of Applied Metaheuristic Computing 11, no. 3 (2020): 58–78. http://dx.doi.org/10.4018/ijamc.2020070103.
Full textAbstract:
With the increase in the number of electric vehicles (EV) on the street in the last years, the drivers of EVs are suffering from the problem of guiding themselves toward the nearest charging stations for recharging their batteries or finding the shortest routes toward their destinations. Although, the electric vehicle planning problem (EPP) is designed to achieve several transactions such as battery energy restrictions and the challenge of finding the nearest charging stations to the position of the electric vehicle. In this work, a new distributed system for electric vehicle routing is based on a novel driving strategy using a distributed Ant system algorithm (AS). The distributed architecture minimizes the total travelling path for the EV to attain the destination by proposing a set of the nearest charging stations that can be visited for recharging during his travels. Simulation result proved that our prototype is able to prepare optimal solutions within a reasonable time and forwarding EVs toward the nearest charging stations during their trips.
25
Xin, Junfeng, Jiabao Zhong, Fengru Yang, Ying Cui, and Jinlu Sheng. "An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle." Sensors 19, no. 11 (2019): 2640. http://dx.doi.org/10.3390/s19112640.
Full textAbstract:
The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others.
26
Li, Jiaqi, Yun Wang, and Ke-Lin Du. "Distribution Path Optimization by an Improved Genetic Algorithm Combined with a Divide-and-Conquer Strategy." Technologies 10, no. 4 (2022): 81. http://dx.doi.org/10.3390/technologies10040081.
Full textAbstract:
The multivehicle routing problem (MVRP) is a variation of the classical vehicle routing problem (VRP). The MVRP is to find a set of routes by multiple vehicles that serve multiple customers at a minimal total cost while the travelling-time delay due to traffic congestion is tolerated. It is an NP problem and is conventionally solved by metaheuristics such as evolutionary algorithms. For the MVRP in a distribution network, we propose an optimal distribution path optimization method that is composed of a distribution sequence search stage and a distribution path search stage that exploits a divide-and-conquer strategy, inspired by the idea of dynamic programming. Several optimization objectives subject to constraints are defined. The search for the optimal solution of the number of distribution vehicles, distribution sequence, and path is implemented by using an improved genetic algorithm (GA), which is characterized by an operation for preprocessing infeasible solutions, an elitist’s strategy, a sequence-related two-point crossover operator, and a reversion mutation operator. The improved GA outperforms the simple GA in terms of total cost, route topology, and route feasibility. The proposed method can help to reduce costs and increase efficiency for logistics and transportation enterprises and can also be used for flow-shop scheduling by manufacturing enterprises.
27
Liu, Zhiqiang, Weidong Wang, Junyi He, et al. "A New Hybrid Algorithm for Vehicle Routing Optimization." Sustainability 15, no. 14 (2023): 10982. http://dx.doi.org/10.3390/su151410982.
Full textAbstract:
To solve the vehicle routing problem with simultaneous pickup–delivery and time windows (VRPSDPTW), a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is presented. Based on the standard sparrow search algorithm, the initial population uses tent chaotic mapping to change the population diversity; then, the discoverer location is updated using the sine cosine fluctuation range of the random weight factor, and finally the global population location is updated using the firefly perturbation strategy. In this study, SFSSA was compared with a genetic algorithm (GA), parallel simulated annealing algorithm (p-SA), discrete cuckoo search algorithm (DCS), and novel mimetic algorithm with efficient local search and extended neighborhood (MATE) adopting improved Solomon’s benchmark test cases. The computational results showed that the proposed SFSSA was able to achieve the current optimal solutions for 100% of the nine small-to-medium instances. For large-scale instances, SFSSA obtained the current optimal solutions for 25 out of 56 instances. The experimental findings demonstrated that SFSSA was an effective method for solving the VRPSPDTW problem.
28
Shabaev, Anton, Anton Sokolov, Alexander Urban, and Dmitry Pyatin. "An approach to the optimal timber transport scheduling." E3S Web of Conferences 164 (2020): 03019. http://dx.doi.org/10.1051/e3sconf/202016403019.
Full textAbstract:
An approach to the optimal timber transport scheduling is described in the paper. A description of this problem is given, a multi-criteria mathematical model is created. It is noted that the problem belongs to the class of General vehicle routing problems (GVRP) associated with the job-shop scheduling. A hybrid algorithm for solving this problem based on the decomposition method using the simplex method and the genetic algorithm is developed. Testing of the proposed approach using real data from wood harvesting enterprises showed its effectiveness. The algorithm was implemented in “Opti-Wood” decision support system for wood harvesting planning and management, developed by Opti-Soft company (Russia).
29
Yuan, Yunmei, Hongyu Li, and Lili Ji. "Application of Deep Reinforcement Learning Algorithm in Uncertain Logistics Transportation Scheduling." Computational Intelligence and Neuroscience 2021 (September 25, 2021): 1–9. http://dx.doi.org/10.1155/2021/5672227.
Full textAbstract:
Nowadays, finding the optimal route for vehicles through online vehicle path planning is one of the main problems that the logistics industry needs to solve. Due to the uncertainty of the transportation system, especially the last-mile delivery problem of small packages in uncertain logistics transportation, the calculation of logistics vehicle routing planning becomes more complex than before. Most of the existing solutions are less applied to new technologies such as machine learning, and most of them use a heuristic algorithm. This kind of solution not only needs to set a lot of constraints but also requires much calculation time in the logistics network with high demand density. To design the uncertain logistics transportation path with minimum time, this paper proposes a new optimization strategy based on deep reinforcement learning that converts the uncertain online logistics routing problems into vehicle path planning problems and designs an embedded pointer network for obtaining the optimal solution. Considering the long time to solve the neural network, it is unrealistic to train parameters through supervised data. This article uses an unsupervised method to train the parameters. Because the process of parameter training is offline, this strategy can avoid the high delay. Through the simulation part, it is not difficult to see that the strategy proposed in this paper will effectively solve the uncertain logistics scheduling problem under the limited computing time, and it is significantly better than other strategies. Compared with traditional mathematical procedures, the algorithm proposed in this paper can reduce the driving distance by 60.71%. In addition, this paper also studies the impact of some key parameters on the effect of the program.
30
Zhou, Zhongxin, Minghu Ha, Hao Hu, and Hongguang Ma. "Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation." Sustainability 13, no. 3 (2021): 1262. http://dx.doi.org/10.3390/su13031262.
Full textAbstract:
How to reduce the accidents of hazardous materials has become an important and urgent research topic in the safety management of hazardous materials. In this study, we focus on the half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. The goal is to determine the vehicle allocation and the optimal route with minimum risk and cost for hazardous materials transportation. A novel transportation risk model is presented considering the variation of vehicle loading, vehicle types, and hazardous materials category. In order to balance the transportation risk and the transportation cost, we propose a bi-objective mixed integer programming model. A hybrid intelligent algorithm is developed based on the ε-constraint method and genetic algorithm to obtain the Pareto optimal solutions. Numerical experiments are provided to demonstrate the effectiveness of the proposed model. Compared with the close multi-depot heterogeneous vehicle routing problem, the average risk and cost obtained by the proposed bi-objective mixed integer programming model can be reduced by 3.99% and 2.01%, respectively. In addition, compared with the half open multi-depot homogeneous vehicle routing problem, the cost is significantly reduced with the acceptable risk.
31
Wang, D. L., A. Ding, G. L. Chen, and L. Zhang. "A combined genetic algorithm and A* search algorithm for the electric vehicle routing problem with time windows." Advances in Production Engineering & Management 18, no. 4 (2023): 403–16. http://dx.doi.org/10.14743/apem2023.4.481.
Full textAbstract:
With growing environmental concerns, the focus on greenhouse gases (GHG) emissions in transportation has increased, and the combination of smart microgrids and electric vehicles (EVs) brings a new opportunity to solve this problem. Electric vehicle routing problem with time windows (EVRPTW) is an extension of the vehicle routing problem (VRP) problem, which can reach the combination of smart microgrids and EVs precisely by scheduling the EVs. However, the current genetic algorithm (GA) for solving this problem can easily fall into the dilemma of local optimization and slow iteration speed. In this paper, we present an integer hybrid planning model that introduces time of use and area price to enhance realism. We propose the GA-A* algorithm, which combines the A* algorithm and GA to improve global search capability and iteration speed. We conducted experiments on 16 benchmark cases, comparing the GA-A* algorithm with traditional GA and other search algorithms, results demonstrate significant enhancements in searchability and optimal solutions. In addition, we measured the grid load, and the model implements the vehicle-to-grid (V2G) mode, which serves as peak shaving and valley filling by integrating EVs into the grid for energy delivery and exchange through battery swapping. This research, ranging from model optimization to algorithm improvement, is an important step towards solving the EVRPTW problem and improving the environment.
32
Peng, Ya Li, Jia Yao Liu, and Hong Yin. "Research on Vehicle Path Planning Based on the BDD in the Uncertain Environment." Applied Mechanics and Materials 373-375 (August 2013): 1144–49. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1144.
Full textAbstract:
Aimed at the high dynamics and uncertainty of road traffic, we propose a method combine BDD (binary decision diagram)-Based heuristic algorithm which used to do the initial path planning with BDD-Based incremental to solve the route replanning problem. In order to get the optimal path set, BDD-Based heuristic Search is firstly used for global planning. BDD is a compact data structure, the BDD-Based heuristic Search use this characteristic to represent state space and compress the search space through heuristic information at the same time; when the road network information changes, incremental replanning was used in difference type of congestion and the optimum path set again. The simulation results show that the BDD-Based heuristic Search and incremental replanning method has high efficiency and practicability in solving vehicle routing problem under dynamic and uncertain environment.
33
Wei, Haitao, Shusheng Zhang, and Xiaohui He. "Shortest Path Algorithm in Dynamic Restricted Area Based on Unidirectional Road Network Model." Sensors 21, no. 1 (2020): 203. http://dx.doi.org/10.3390/s21010203.
Full textAbstract:
Accurate and fast path calculation is essential for applications such as vehicle navigation systems and transportation network routing. Although many shortest path algorithms for restricted search areas have been developed in the past ten years to speed up the efficiency of path query, the performance including the practicability still needs to be improved. To settle this problem, this paper proposes a new method of calculating statistical parameters based on a unidirectional road network model that is more in line with the real world and a path planning algorithm for dynamically restricted search areas that constructs virtual boundaries at a lower confidence level. We conducted a detailed experiment on the proposed algorithm with the real road network in Zhengzhou. As the experiment shows, compared with the existing algorithms, the proposed algorithm improves the search performance significantly in the condition of optimal path under the premise of ensuring the optimal path solution.
34
Cong, Ying, and Kai Zhu. "Research on Vehicle Path Planning Method with Time Windows in Uncertain Environments." World Electric Vehicle Journal 15, no. 12 (2024): 566. https://doi.org/10.3390/wevj15120566.
Full textAbstract:
With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints.
35
Fan, Linsheng. "Routing optimization method of waste transportation vehicle using biological evolutionary algorithm under the perspective of low carbon and environmental protection." Environmental Engineering Research 28, no. 1 (2022): 210458–0. http://dx.doi.org/10.4491/eer.2021.458.
Full textAbstract:
Reasonably and effectively formulating the best route for urban waste transportation vehicle is particularly important for realizing low carbon and environmental protection of Green China construction concept. However, the current path planning method has shortcomings such as local optimization. In order to solve this problem, this paper aims at low carbon and environmental protection construction needs and proposes a routing optimization method of waste transportation vehicles based on improved ant colony algorithm. Firstly, the mathematical model of Vehicle Routing Problem (VRP) is constructed by considering transportation distance and carbon emissions cost. Then, network parameters in traditional ant colony algorithm are combined and optimized to realize self-adaptive update in the whole cycle. Furthermore, the neighborhood algorithm is used to iteratively optimize improved algorithm to avoid the defects of local optimization when solving VRP problem. Finally, the simulation results based on an actual dataset in North China show that the proposed method can achieve efficient and accurate optimal routing optimization for complex samples, and its solution stability index is 0.87 and the average deviation is 0.011, the lowest distribution cost after optimization is about 3,080 yuan, which are better than the comparison methods.
36
Du, Lijing, Xiaohuan Li, Yuan Gan, and Kaijun Leng. "Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies." Sustainability 14, no. 8 (2022): 4651. http://dx.doi.org/10.3390/su14084651.
Full textAbstract:
To reduce distribution risk and improve the efficiency of medical materials delivery under major public health emergencies, this paper introduces a drone routing problem with time windows. A mixed-integer programming model is formulated considering contactless delivery, total travel time, and customer service time windows. Utilizing Dantzig–Wolfe decomposition, the proposed optimization model is converted into a path-based master problem and a pricing subproblem based on an elementary shortest path problem with resource constraints. We embed the pulse algorithm into a column generation framework to solve the proposed model. The effectiveness of the model and algorithm is verified by addressing different scales of Solomon datasets. A case study on COVID-19 illustrates the application of the proposed model and algorithm in practice. We also perform a sensitivity analysis on the drone capacity that may affect the total distribution time. The experimental results enrich the research related to vehicle routing problem models and algorithms under major public health emergencies and provide optimized relief distribution solutions for decision-makers of emergency logistics.
37
Song, Rui, Wanen Qin, Wen Shi, and Xingjian Xue. "Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis." Sustainability 15, no. 21 (2023): 15504. http://dx.doi.org/10.3390/su152115504.
Full textAbstract:
In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.
38
Zheng, Xiaojun, Feng Gao, and Xiaoying Tong. "Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop." Symmetry 15, no. 8 (2023): 1505. http://dx.doi.org/10.3390/sym15081505.
Full textAbstract:
In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), the green automated guided vehicle (AGV) routing problem, which involves simultaneous pickup and delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, we conduct a comprehensive analysis of the mechanical forces present during AGV transportation and evaluate the overall operational efficiency of the workshop. Then, we construct a green vehicle path planning model to minimize the energy consumption during AGV transportation and the standby period. Hence, the problems considered in this study are modeled based on asymmetry, making the problem solving more complex. We also design a hybrid differential evolution algorithm based on large neighborhood search (DE-LNS) to increase the local search ability of the algorithm. To enhance the optimal quality of solutions, we design an adaptive scaling factor and use the squirrel migration operator to optimize the population. Last, extensive computational experiments, which are generated from the VRPSPDTW instances set and a real case of an intelligent workshop, are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results show that DE-LNS yields competitive results, compared to advanced heuristic algorithms. The effectiveness and applicability of the proposed algorithm are verified. Additionally, the proposed model demonstrates significant energy-saving potential in workshop logistics. It can optimize energy consumption by 15.3% compared with the traditional VRPSPDTW model. Consequently, the model proposed in this research carries substantial implications for minimizing total energy consumption costs and exhibits promising prospects for real-world application in intelligent workshops.
39
Tang, Yongwei, Jun Zhou, Huijuan Hao, Fengqi Hao, and Haigang Xu. "Path Planning and Trajectory Tracking for Automatic Guided Vehicles." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/8981778.
Full textAbstract:
Automated guided vehicle technology has become a hot area of scientific research due to its increasing use in manufacturing and logistics. Its main features are programming and control, remote computer eye tracking, command receiving and execution, autonomous route planning, and autonomous driving execution of tasks, with the advantages of high intelligence and flexibility. In this work, a simple vehicle model is used to study the route planning and tracking control of automatic guided vehicles. This paper uses wireless communication to find the optimal route planning problem. Using geometric methods, we develop a model of the working environment of the mobile automatic guided vehicle and develop a route finding algorithm. Based on the kinematic model, an advanced routing controller is designed to conduct experimental simulation of two trajectories and verify the effectiveness of the trajectory tracking controller. When the time is after 2 s, the position error is almost completely zero. In the path planning, when the number of iterations is greater than 10, the path length remains constant, verifying the effectiveness of the method in this paper.
40
Wang, Biao. "Optimization of Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery." Journal of Electronic Research and Application 9, no. 3 (2025): 350–58. https://doi.org/10.26689/jera.v9i3.10815.
Full textAbstract:
This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery (MVRPTWSPD), aiming to optimize logistics distribution routes and minimize total costs. A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center, focusing on minimizing vehicle dispatch, loading and unloading, operating, and time window penalty costs. The model incorporates constraints such as vehicle capacity, time windows, and travel distance, and is solved using a genetic algorithm to ensure optimal route planning. Through MATLAB simulations, 34 customer points are analyzed, demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%, increases vehicle loading rates by 20.04%, and decreases travel distance compared to delivery-only or pickup-only models. The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization, offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.
41
Lin, Chaoxiong, and Xuecong Zhang. "Application of UAV path planning based on parameter optimization GA-PSO fusion algorithm." Journal of Physics: Conference Series 2258, no. 1 (2022): 012018. http://dx.doi.org/10.1088/1742-6596/2258/1/012018.
Full textAbstract:
Abstract Aiming at the complexity of the unmanned aerial vehicle (UAV) path planning problem and the great influence of genetic algorithm parameters on the stability of the results, a fusion algorithm based on parameter optimization is proposed in this paper. In the iterative process, the GA-PSO fusion algorithm uses particle swarm optimization algorithm to search the optimal value of crossover rate and mutation rate in genetic algorithm, which makes the algorithm convergence speed is fast and search ability is strong. In addition, the core part of the algorithm fusion framework is realized by introducing the optimal adaptive value of the population after crossover and mutation as the adaptive value of the parameter particle. Finally, we design two groups of experiments and compare the proposed fusion algorithm with the classical particle swarm optimization algorithm (PSO), ant colony algorithm (ACA), genetic algorithm (GA), artificial fish swarm algorithm (AFSA), Wolf pack algorithm (WPA), artificial bee colony algorithm (ABC) and the improved algorithm through experimental simulation. The experimental results show that: In general, the path planned by the GA-PSO fusion algorithm in this paper is 10% shorter than that planned by other algorithms on average. Simulation results also show that the convergence speed of the fusion algorithm in this paper is faster, and the final search path is smoother.
42
Deng, Naifu, Xuyang Li, and Yanmin Su. "Optimization of Earthwork Allocation Path as Vehicle Route Problem Based on Genetic Algorithm." E3S Web of Conferences 165 (2020): 04057. http://dx.doi.org/10.1051/e3sconf/202016504057.
Full textAbstract:
In civil engineering, earthwork, prior to the construction of most engineering projects, is a lengthy and time-consuming work involving iterative processes. The cost of many AEC (Architecture, Engineering and Construction) projects is highly dependent on the efficiency of earthworks (e.g. road, embankment, railway and slope engineering). Therefore, designing proper earthwork planning is of importance. This paper simplifies the earthwork allocation problem to Vehicle Route Problem (VRP) which is commonly discussed in the field of transportation and logistics. An optimization model for the earthwork allocation path based on the modified Genetic Algorithm with a self-adaptive mechanism is developed to work out the global optimal hauling path for earthwork. The research results also instruct the initial topographic shaping of the Winter Olympic Skiing Courses Project. Furthermore, this optimization model is highly compatible with other evolutionary algorithms due to its flexibility, therefore, further improvement in this model is feasible and practical.
43
He, Dan. "Intelligent Selection Algorithm of Optimal Logistics Distribution Path Based on Supply Chain Technology." Computational Intelligence and Neuroscience 2022 (April 14, 2022): 1–8. http://dx.doi.org/10.1155/2022/9955726.
Full textAbstract:
How to realize the intelligence of logistics distribution is a hot research topic at present. How to reasonably allocate vehicles, optimize driving routes and travel time, deliver goods to customers on time at the lowest cost, and realize efficient and low-cost operation of the logistics distribution system has always been a problem in academia and industry for many years. Logistics enterprises face problems such as low efficiency of logistics operation, lack of scientific rationality of logistics resource planning, and lack of overall optimization of logistics management operation mode. These are severe tests that steel companies must accept. Under the background of logistics supply chain, the integrated service platform of logistics supply chain has become an urgent research topic. This study takes a steel enterprise as the main research background. On this basis, the two core modules of warehousing and distribution in the logistics business of iron and steel enterprises are qualitatively analyzed, the concept of business process reengineering is proposed, and the logistics supply chain of iron and steel enterprises is established. The concept of comprehensive service platform is realized through RFID technology. In addition, this study conducts a comprehensive analysis and research on the logistics distribution path optimization and vehicle scheduling problem, designs and implements a logistics vehicle scheduling management system, and then adopts the multiobjective method to solve the logistics distribution path planning problem, SMEs. Genetic algorithm and a simulation decision-making subsystem suitable for this problem are designed, which can better solve the problem of route optimization and vehicle scheduling in small-scale distribution.
44
Hsiao, Yu-Hsiang, Mu-Chen Chen, Kuan-Yu Lu, and Cheng-Lin Chin. "Last-mile distribution planning for fruit-and-vegetable cold chains." International Journal of Logistics Management 29, no. 3 (2018): 862–86. http://dx.doi.org/10.1108/ijlm-01-2017-0002.
Full textAbstract:
Purpose The purpose of this paper is to formulate and solve a last-mile distribution plan problem with concern for the quality of fruits and vegetables in cold chains. Design/methodology/approach The vehicle routing problem with time windows (VRPTW) is extended based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods, continuing decline in quality, various requirements for quality levels and optimal temperature settings during vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimation of residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness function and evolution operators are designed to deal with the complicated problem herein. Findings A distribution plan including required fleet size, vehicle routing sequence and what quality level should be shipped out to account for the quality degradation during vehicle transportation is generated. The results indicate that the fulfillment of various requirements of different customers for various fruits and vegetables and quality levels can be ensured with cost considerations. Originality/value This study presents a problem for last-mile delivery of fresh fruits and vegetables which considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to address this problem. The proposed model is easily applied to other types of perishable products.
45
Chen, Yang, Jianda Han, and Xingang Zhao. "Three-dimensional path planning for unmanned aerial vehicle based on linear programming." Robotica 30, no. 5 (2011): 773–81. http://dx.doi.org/10.1017/s0263574711000993.
Full textAbstract:
SUMMARYIn this paper, an approach based on linear programming (LP) is proposed for path planning in three-dimensional space, in which an aerial vehicle is requested to pursue a target while avoiding static or dynamic obstacles. This problem is very meaningful for many aerial robots, such as unmanned aerial vehicles. First, the tasks of target-pursuit and obstacle-avoidance are modelled with linear constraints in relative coordination according to LP formulation. Then, two weighted cost functions, representing the optimal velocity resolution, are integrated into the final objective function. This resolution, defined to achieve the optimal velocity, deals with the optimization of a pair of orthogonal vectors. Some constraints, such as boundaries of the vehicle velocity, acceleration, sensor range, and flying height, are considered in this method. A number of simulations, under static and dynamic environments, are carried out to validate the performance of generating optimal trajectory in real time. Compared with ant colony optimization algorithm and genetic algorithm, our method has less parameters to tune and can achieve better performance in real-time application.
46
Tan, Aiping, Chang Wang, Yan Wang, and Chenglong Dong. "Electric Vehicle Charging Route Planning for Shortest Travel Time Based on Improved Ant Colony Optimization." Sensors 25, no. 1 (2024): 176. https://doi.org/10.3390/s25010176.
Full textAbstract:
Electric vehicles (EVs) are gaining significant attention as an environmentally friendly transportation solution. However, limitations in battery technology continue to restrict EV range and charging speed, resulting in range anxiety, which hampers widespread adoption. While there has been increasing research on EV route optimization, personalized path planning that caters to individual user needs remains underexplored. To bridge this gap, we propose the electric vehicle charging route planning based on user requirements (EVCRP-UR) problem, which aims to integrate user preferences and multiple constraints. Our approach utilizes topology optimization to reduce computational complexity and improve path planning efficiency. Furthermore, we introduce an improved ant colony optimization (IACO) algorithm incorporating novel heuristic functions and refined probability distribution models to select optimal paths and charging stations. To further enhance charging strategies, we develop a discrete electricity dynamic programming (DE-DP) algorithm to determine charging times at efficiently chosen stations. By combining these methods, the proposed IACO algorithm leverages the strengths of each approach, overcoming their individual limitations and delivering superior performance in EV routing and charging optimization.
47
Li, Jin, Feng Wang, and Yu He. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions." Sustainability 12, no. 24 (2020): 10537. http://dx.doi.org/10.3390/su122410537.
Full textAbstract:
In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.
48
Vidal, Thibaut, Rafael Martinelli, Tuan Anh Pham, and Minh Hoàng Hà. "Arc Routing with Time-Dependent Travel Times and Paths." Transportation Science 55, no. 3 (2021): 706–24. http://dx.doi.org/10.1287/trsc.2020.1035.
Full textAbstract:
Vehicle routing algorithms usually reformulate the road network into a complete graph in which each arc represents the shortest path between two locations. Studies on time-dependent routing followed this model and therefore defined the speed functions on the complete graph. We argue that this model is often inadequate, in particular for arc routing problems involving services on edges of a road network. To fill this gap, we formally define the time-dependent capacitated arc routing problem (TDCARP), with travel and service speed functions given directly at the network level. Under these assumptions, the quickest path between locations can change over time, leading to a complex problem that challenges the capabilities of current solution methods. We introduce effective algorithms for preprocessing quickest paths in a closed form, efficient data structures for travel time queries during routing optimization, and heuristic and exact solution approaches for the TDCARP. Our heuristic uses the hybrid genetic search principle with tailored solution-decoding algorithms and lower bounds for filtering moves. Our branch-and-price algorithm exploits dedicated pricing routines, heuristic dominance rules, and completion bounds to find optimal solutions for problems counting up to 75 services. From these algorithms, we measure the benefits of time-dependent routing optimization for different levels of travel-speed data accuracy.
49
Sun, Guofeng, Zhiqiang Tian, Renhua Liu, Yun Jing, and Yawen Ma. "Research on Coordination and Optimization of Order Allocation and Delivery Route Planning in Take-Out System." Mathematical Problems in Engineering 2020 (July 23, 2020): 1–16. http://dx.doi.org/10.1155/2020/7248492.
Full textAbstract:
This paper studies the take-out route delivery problem (TRDP) with order allocation and unilateral soft time window constraints. The TRDP considers the order allocation and delivery route optimization in the delivery service process. The TRDP is a challenging version of vehicle routing problem. In order to solve this problem, this paper aims to minimize the total cost of delivery, builds an optimization model of this problem by using cumulative time, and adds time dimension in order allocation and path optimization dimensions. It can not only track the real-time location of delivery personnel but also record the delivery personnel to perform a certain task. The main algorithm is the dynamic allocation algorithm designed from the perspective of dispatch efficiency, and the subalgorithm is the improved genetic algorithm. Finally, some experiments are designed to verify the effectiveness of the established model and the designed algorithm, the order allocation and route optimization are calculated with/without the consideration of traffic jam, and the results show that the algorithm can generate better solution in each scene.
50
Liu, Yong, Zhicheng Yue, Yong Wang, and Haizhong Wang. "Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint." Sustainability 15, no. 4 (2023): 3594. http://dx.doi.org/10.3390/su15043594.
Full textAbstract:
As an important support of the e-commerce industry, the express delivery industry is particularly important in national development. Low loading rates caused by numerous types of containers and cost increases caused by low loading and unloading efficiency are still remaining issues in the process of goods delivery and packing. This study introduced the pallet with telescopic support height as the middle to address these issues and proposed a distribution scheme based on the constraints of three-dimensional pallet loading with a time window. First, combining the path optimization of the time window and cargo loading, a solution model was established to solve the existing express delivery problem with the lowest total delivery cost and the highest average vehicle loading rate. In addition, the multi-objective problem was transformed through the multi-objective linear weighting method. Second, we cluster the customer nodes. In order to solve the large number of gaps generated by the hierarchy theory, we adopt the descending order of cargo volume as the initial sequence and design the coding and decoding for path optimization and pallet loading, solving the problem through the simulated anneal-genetic algorithm. Finally, the effectiveness of the algorithm is obtained through the comparison with other algorithms and the simple three-dimensional loading and distribution scheme by using examples. It is proved that the optimization of three-dimensional packing for express delivery using pallets as carriers can not only meet the high loading rate but also improve the loading and unloading speed, reduce the time penalty cost, and improve the operability of loading. This paper provides decision reference and method support for path optimization under three-dimensional loading constraints.