Academic literature on the topic 'Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning'
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Journal articles on the topic "Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning"
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
Dissertations / Theses on the topic "Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning"
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Kovàcs, Akos. "Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3306.
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This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.
Book chapters on the topic "Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning"
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Elgarej, Mouhcine, Mansouri Khalifa, and Mohamed Youssfi. "Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems." In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5339-8.ch094.
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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.
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Ke, Chaobao, Pingshan Liu, Lei Yang, Waner Huang, Huishan Huang, and Na Wei. "A Path Planning Method for Scrap Recycling Vehicles Based on Improved Genetic Algorithm." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221071.
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With the rapid development of e-commerce platforms and new products in China, however, the scrap products such as waste paper, used home appliances and scrap metals that come with them are accumulating in cities, affecting the environment and hygiene. Today, scrap collectors often drive electric tricycles aimlessly to carry out scrap collection. The vehicle path planning is a pressing problem in the recycling process of scrap products from user location points to sorting centers. In order to solve this problem and minimize the cost consumed in the recycling process, this paper solves and analyzes the actual problem based on an improved genetic algorithm, combined with GIS technology. Firstly, based on the characteristics of the scrap recycling process, the shortest path is solved based on GIS software, and then the spatial and distance information such as user location points and sorting points are combined to construct a GIS network to obtain the shortest path. Secondly, this paper establishes a vehicle path planning model for the sorting center considering factors such as actual scrap collectors’ commuting and service time. Then, an improved and optimized genetic algorithm is designed, and then the model is solved. Finally, the solved optimal path of scrap collection is displayed in GIS software. In this paper, the effectiveness of the method is demonstrated by taking Guilin city as an example.
Conference papers on the topic "Genetic Algorithm; Vehicle Routing Problem; Optimal Path Planning"
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Qiu, Rui, and Yongtu Liang. "A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection." In 2020 13th International Pipeline Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ipc2020-9604.
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Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.
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Ma, Shizi, Sheng Wang, Zhitao Ma, and Zhiguo QI. "Internet of Autonomous Vehicles for The Distribution System of Smart Cities." In WCX SAE World Congress Experience. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2882.
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<div class="section abstract"><div class="htmlview paragraph">With the development of internet technology and autonomous vehicles (AVs), the multimodal transportation and distribution model based on AVs will be a typical application paradigm in the smart city scenario. Before AVs carry out logistics distribution, it is necessary to plan a reasonable distribution path based on each customer point, and this is also known as Vehicle Routing Problem (VRP). Unlike traditional VRP, the urban logistics distribution process based on multimodal transportation mode will use a set of different types of AVs, mainly including autonomous ground vehicles and unmanned aerial vehicles (UAVs). It is worth pointing out that there is currently no research on combining the planning of AVs distribution paths with the trajectory planning of UAVs. To address this issue, this article establishes a bilevel programming model. The upper-level model aims to plan the optimal delivery plan for AVs, while the lower-level model aims to plan a driving trajectory for UAVs. Furthermore, this paper proposes an improved heuristic algorithm for the bilevel programming model. Due to the tendency of Group Search Optimizer (GSO) algorithm to fall into local optima during the process of solving large-scale complex problems, this paper designs an improved GSO algorithm based on improved strategies such as spiral search strategy. During the solving process, based on the upper-level model and using the IGSO algorithm, the distribution order of AVs can be directly solved. In the process of trajectory planning based on the lower-level model, the RRT algorithm is first used to plan an initial trajectory for the UAV. Furthermore, the IGSO algorithm is used to further optimize this trajectory, ultimately achieving the delivery task of the UAV. Finally, a simulation experiment was conducted to compare the effectiveness of the proposed scheme with other algorithms.</div></div>