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C-SPPO:A deep reinforcement learning framework for large-scale dynamic logistics UAV routing problem 认领 引用 被引量：3
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作者 Fei WANG Honghai ZHANG +2 位作者 Sen DU Mingzhuang HUA Gang ZHONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第5期296-316,共21页
Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-S... Unmanned Aerial Vehicle(UAV)stands as a burgeoning electric transportation carrier,holding substantial promise for the logistics sector.A reinforcement learning framework Centralized-S Proximal Policy Optimization(C-SPPO)based on centralized decision process and considering policy entropy(S)is proposed.The proposed framework aims to plan the best scheduling scheme with the objective of minimizing both the timeout of order requests and the flight impact of UAVs that may lead to conflicts.In this framework,the intents of matching act are generated through the observations of UAV agents,and the ultimate conflict-free matching results are output under the guidance of a centralized decision maker.Concurrently,a pre-activation operation is introduced to further enhance the cooperation among UAV agents.Simulation experiments based on real-world data from New York City are conducted.The results indicate that the proposed CSPPO outperforms the baseline algorithms in the Average Delay Time(ADT),the Maximum Delay Time(MDT),the Order Delay Rate(ODR),the Average Flight Distance(AFD),and the Flight Impact Ratio(FIR).Furthermore,the framework demonstrates scalability to scenarios of different sizes without requiring additional training. 展开更多
关键词 Unmanned aerial vehicle Vehicle routing problem Orderdelivery Reinforcement learning Multi-agent Proximal policy optimization
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Two-phase heuristic for vehicle routing problem with drones in multi-trip and multi-drop mode 认领 引用
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作者 MA Huawei HU Xiaoxuan ZHU Waiming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第4期1024-1036,共13页
As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in mult... As commercial drone delivery becomes increasingly popular,the extension of the vehicle routing problem with drones(VRPD)is emerging as an optimization problem of inter-ests.This paper studies a variant of VRPD in multi-trip and multi-drop(VRP-mmD).The problem aims at making schedules for the trucks and drones such that the total travel time is minimized.This paper formulate the problem with a mixed integer program-ming model and propose a two-phase algorithm,i.e.,a parallel route construction heuristic(PRCH)for the first phase and an adaptive neighbor searching heuristic(ANSH)for the second phase.The PRCH generates an initial solution by con-currently assigning as many nodes as possible to the truck–drone pair to progressively reduce the waiting time at the rendezvous node in the first phase.Then the ANSH improves the initial solution by adaptively exploring the neighborhoods in the second phase.Numerical tests on some benchmark data are conducted to verify the performance of the algorithm.The results show that the proposed algorithm can found better solu-tions than some state-of-the-art methods for all instances.More-over,an extensive analysis highlights the stability of the pro-posed algorithm. 展开更多
关键词 vehicle routing problem with drones(VRPD) mixed integer program parallel route construction heuristic(PRCH) adaptive neighbor searching heuristic(ANSH).
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An Adaptive Hybrid Metaheuristic for Solving the Vehicle Routing Problem with Time Windows under Uncertainty 认领 引用
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作者 Manuel J.C.S.Reis 《Computers, Materials & Continua》 SCIE EI 2025年第11期3023-3039,共17页
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ... The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments. 展开更多
关键词 Vehicle routing problem with time windows(VRPTW) hybrid metaheuristic genetic algorithm local search uncertainty modeling stochastic optimization adaptive algorithms combinatorial optimization transportation and logistics robust scheduling
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Optimized Metaheuristic Strategies for Addressing the Multi-Picker Robot Routing Problem in 3D Warehouse Operations 认领 引用 被引量：1
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作者 Thi My Binh Nguyen Thi Hoa Hue Nguyen Thi Ngoc Huyen Do 《Computers, Materials & Continua》 SCIE EI 2025年第9期5063-5076,共14页
Efficient warehouse management is critical for modern supply chain systems,particularly in the era of e-commerce and automation.The Multi-Picker Robot Routing Problem(MPRRP)presents a complex challenge involving the o... Efficient warehouse management is critical for modern supply chain systems,particularly in the era of e-commerce and automation.The Multi-Picker Robot Routing Problem(MPRRP)presents a complex challenge involving the optimization of routes for multiple robots assigned to retrieve items from distinct locations within a warehouse.This study introduces optimized metaheuristic strategies to address MPRRP,with the aim of minimizing travel distances,energy consumption,and order fulfillment time while ensuring operational efficiency.Advanced algorithms,including an enhanced Particle Swarm Optimization(PSO-MPRRP)and a tailored Genetic Algorithm(GA-MPRRP),are specifically designed with customized evolutionary operators to effectively solve the MPRRP.Comparative experiments are conducted to evaluate the proposed strategies against benchmark approaches,demonstrating significant improvements in solution quality and computational efficiency.The findings contribute to the development of intelligent,scalable,and environmentally friendly warehouse systems,paving the way for future advances in robotics and automated logistics management. 展开更多
关键词 Particle swarm optimization algorithm genetic algorithm multi-picker robot routing problem
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An improved estimation of distribution algorithm for multi-compartment electric vehicle routing problem 认领 引用 被引量：10
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作者 SHEN Yindong PENG Liwen LI Jingpeng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期365-379,共15页
The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendl... The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service cost.To solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum.Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm.In addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances.Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles. 展开更多
关键词 multi-compartment vehicle routing problem electric vehicle routing problem(EVRP) soft time window multiple charging type estimation of distribution algorithm(EDA) Lévy flight
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Optimization of Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery 认领 引用
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作者 Biao Wang 《Journal of Electronic Research and Application》 2025年第3期350-358,共9页
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 opti... 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. 展开更多
关键词 Vehicle routing problem Time windows Multi-vehicle types Simultaneous pickup and delivery Genetic algorithm
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Algorithm and Application in Vehicle Routing Problem: A Review 认领 引用
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作者 Zhenyu Chen 《Journal of Electronic Research and Application》 2025年第2期166-174,共9页
This paper systematically reviews the latest research developments in Vehicle Routing Problems(VRP).It examines classical VRP models and their classifications across different dimensions,including load capacity,operat... This paper systematically reviews the latest research developments in Vehicle Routing Problems(VRP).It examines classical VRP models and their classifications across different dimensions,including load capacity,operational characteristics,optimization objectives,vehicle types,and time constraints.Based on literature retrieval results from the Web of Science database,the paper analyzes the current state and trends in VRP research,providing detailed explanations of VRP models and algorithms applied to various scenarios in recent years.Additionally,the article discusses limitations in existing research and provides perspectives on future development trends in VRP research.This review offers researchers in the VRP field a comprehensive overview while identifying future research directions. 展开更多
关键词 Vehicle routing problem VRP Delivery route optimization Logistics planning
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An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem 认领 引用 被引量：11
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作者 Bingjie Li Guohua Wu +2 位作者 Yongming He Mingfeng Fan Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1115-1138,共24页
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute... The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. 展开更多
关键词 End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP)
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Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm 认领 引用 被引量：7
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作者 高贵兵 张国军 +2 位作者 黄刚 朱海平 顾佩华 《Journal of Central South University》 SCIE EI CAS 2012年第2期433-442,共10页
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency... The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II. 展开更多
关键词 material distribution routing problem multi-objective optimization evolutionary algorithm local search
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Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows 认领 引用 被引量：16
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作者 DENG Ye ZHU Wanhong +1 位作者 LI Hongwei ZHENG Yonghui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2018年第3期625-638,共14页
The time dependent vehicle routing problem with time windows(TDVRPTW)is considered.A multi-type ant system(MTAS)algorithm hybridized with the ant colony system(ACS)and the max-min ant system(MMAS)algorithms is propose... The time dependent vehicle routing problem with time windows(TDVRPTW)is considered.A multi-type ant system(MTAS)algorithm hybridized with the ant colony system(ACS)and the max-min ant system(MMAS)algorithms is proposed.This combination absorbs the merits of the two algorithms in solutions construction and optimization separately.In order to improve the efficiency of the insertion procedure,a nearest neighbor selection(NNS)mechanism,an insertion local search procedure and a local optimization procedure are specified in detail.And in order to find a balance between good scouting performance and fast convergence rate,an adaptive pheromone updating strategy is proposed in the MTAS.Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows(VRPTW)benchmark instances and the TDVRPTW instances,and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research. 展开更多
关键词 multi-type ant system(MTAS) time dependent vehicle routing problem with time windows(VRPTW) nearest neighbor selection(NNS)
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Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows 认领 引用 被引量：14
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作者 汤雅连 蔡延光 杨期江 《Journal of Southeast University(English Edition)》 EI CAS 2015年第1期94-99,共6页
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ... Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful. 展开更多
关键词 vehicle routing problem soft time window improved ant colony optimization customer service priority genetic algorithm
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A GA approach to vehicle routing problem with time windows considering loading constraints 认领 引用 被引量：4
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作者 刘建胜 Luo Zhiwen +2 位作者 Duan Duanzhi Lai Zhihui Huang Jiali 《High Technology Letters》 EI CAS 2017年第1期54-62,共9页
As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with t... As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry. 展开更多
关键词 finished vehicle logistics （FVL） vehicle routing problem （VRP） genetic algo-rithm （GA） time windows
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GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION 认领 引用 被引量：4
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作者 WEI Tian FAN Wenhui XU Huayu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2008年第6期18-24,共7页
Vehicle routing problem in distribution(VRPD)is a widely used type of vehicle routing problem(VRP),which has been proved as NP-Hard,and it is usually modeled as single objective optimization problem when modeling.For ... Vehicle routing problem in distribution(VRPD)is a widely used type of vehicle routing problem(VRP),which has been proved as NP-Hard,and it is usually modeled as single objective optimization problem when modeling.For multi-objective optimization model,most researches consider two objectives.A multi-objective mathematical model for VRP is proposed,which considers the number of vehicles used,the length of route and the time arrived at each client.Genetic algorithm is one of the most widely used algorithms to solve VRP.As a type of genetic algorithm(GA),non-dominated sorting in genetic algorithm-Ⅱ(NSGA-Ⅱ)also suffers from premature convergence and enclosure competition.In order to avoid these kinds of shortage,a greedy NSGA-Ⅱ(GNSGA-Ⅱ)is proposed for VRP problem.Greedy algorithm is implemented in generating the initial population,cross-over and mutation.All these procedures ensure that NSGA-Ⅱis prevented from premature convergence and refine the performance of NSGA-Ⅱat each step.In the distribution problem of a distribution center in Michigan,US,the GNSGA-Ⅱis compared with NSGA-Ⅱ.As a result,the GNSGA-Ⅱis the most efficient one and can get the most optimized solution to VRP problem.Also,in GNSGA-Ⅱ,premature convergence is better avoided and search efficiency has been improved sharply. 展开更多
关键词 Greedy non-dominated sorting in genetic algorithm-Ⅱ(GNSGA-Ⅱ) Vehicle routing problem(VRP) Multi-objective optimization
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Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm 认领 引用 被引量：3
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作者 郑建国 李康 伍大清 《Journal of Donghua University(English Edition)》 CAS 2017年第4期533-539,共7页
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location... In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem. 展开更多
关键词 cold chain logistics multi-objective location inventory routing problem(LIRP) non-dominated sorting in genetic algorithm Ⅱ(NSGA-Ⅱ)
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Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm 认领 引用 被引量：1
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作者 Jinhui Ge Xiaoliang Liu Guo Liang 《Computers, Materials & Continua》 SCIE EI 2020年第9期1945-1958,共14页
With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various so... With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes,cultures,and the emotional needs of customers.The actual soft time window vehicle routing problem,speeding up the response of customer needs,improving distribution efficiency,and reducing operating costs is the focus of current social computing problems.Therefore,designing fast and effective algorithms to solve this problem has certain theoretical and practical significance.In this paper,considering the time delay problem of customer demand,the compensation problem is given,and the mathematical model of vehicle path problem with soft time window is given.This paper proposes a hybrid tabu search(TS)&scatter search(SS)algorithm for vehicle routing problem with soft time windows(VRPSTW),which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework.TS uses the scattering of SS to avoid the dependence on the quality of the initial solution,and SS uses the climbing ability of TS improves the ability of optimizing,so that the quality of search for the optimal solution can be significantly improved.The hybrid algorithm is still based on the basic framework of SS.In particular,TS is mainly used for solution improvement and combination to generate new solutions.In the solution process,both the quality and the dispersion of the solution are considered.A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution,parameters’control over the degree of convergence,and the influence of the number of diverse solutions on algorithm performance.Based on the determined parameters,simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness.The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability. 展开更多
关键词 Time window tabu search scatter search vehicle routing problem with soft time windows(VRPSTW).
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A Hybrid TCNN Optimization Approach for the Capacity Vehicle Routing Problem 认领 引用 被引量：1
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作者 孙华丽 谢剑英 薛耀锋 《Journal of Shanghai Jiaotong university(Science)》 EI 2006年第1期34-39,共6页
A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehic... A novel approximation algorithm was proposed for the problem of finding the minimum total cost of all routes in Capacity Vehicle Routing Problem (CVRP). CVRP can be partitioned into three parts: the selection of vehicles among the available vehicles, the initial routing of the selected fleet and the routing optimization. Fuzzy C-means (FCM) can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition. Transiently chaotic neural network (TCNN) combines local search and global search, possessing high search efficiency. It will solve the routes to near optimality. A simple tabu search (TS) procedure can improve the routes to more optimality. The computations on benchmark problems and comparisons with other results in literatures show that the proposed algorithm is a viable and effective approach for CVRP. 展开更多
关键词 capacity vehicle routing problem fuzzy C-means transiently chaotic neural network hybrid optimization algorithm
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A novel genetic algorithm for vehicle routing problem with time windows 认领 引用 被引量：2
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作者 刘云忠 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第3期437-444,共8页
A novel genetic algorithm with multiple species in dynamic region is proposed,each of which occupies a dynamic region determined by the weight vector of a fuzzy adaptive Hamming neural network. Through learning and cl... A novel genetic algorithm with multiple species in dynamic region is proposed,each of which occupies a dynamic region determined by the weight vector of a fuzzy adaptive Hamming neural network. Through learning and classification of genetic individuals in the evolutionary procedure,the neural network distributes multiple species into different regions of the search space. Furthermore,the neural network dynamically expands each search region or establishes new region for good offspring individuals to continuously keep the diversification of the genetic population. As a result,the premature problem inherent in genetic algorithm is alleviated and better tradeoff between the ability of exploration and exploitation can be obtained. The experimental results on the vehicle routing problem with time windows also show the good performance of the proposed genetic algorithm. 展开更多
关键词 genetic algorithm multiple species neural network premature problem vehicle routing problem with time windows
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An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem 认领 引用
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作者 陈爱玲 杨根科 吴智铭 《Journal of Shanghai Jiaotong university(Science)》 EI 2006年第1期50-55,共6页
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high ... Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem. 展开更多
关键词 capacitated vehicle routing problem artificial immune clonal algorithm simulated annealing
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Vehicle routing problem with time-varying speed 认领 引用
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作者 刘云忠 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第4期584-587,共4页
Vehicle routing problem with time-varying speed ( VRPTS) is a generalization of vehicle routing problem in which the travel speed between two locations depends on the passing areas and the time of a day. This paper pr... Vehicle routing problem with time-varying speed ( VRPTS) is a generalization of vehicle routing problem in which the travel speed between two locations depends on the passing areas and the time of a day. This paper proposes a simple model for estimating time-varying travel speeds in VRPTS that relieves much burden to the data-related problems. The study further presents three heuristics ( saving technique,proximity priority searching technique,and insertion technique) for VRPTS,developed by extending and modifying the existing heuristics for conventional VRP. The results of computational experiments demonstrate that the proposed estimation model performs well and the saving technique is the best among the three heuristics. 展开更多
关键词 vehicle routing problem time-varying speed saving technique proximity priority searching technique insertion technique
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Intelligent Iterated Local Search Methods for Solving Vehicle Routing Problem with Different Fleets 认领 引用
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作者 李妍峰 李军 赵达 《Journal of Southwest Jiaotong University(English Edition)》 2007年第4期344-352,共9页
To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to a... To solve vehicle routing problem with different fleets, two methodologies are developed. The first methodology adopts twophase strategy. In the first phase, the improved savings method is used to assign customers to appropriate vehicles. In the second phase, the iterated dynasearch algorithm is adopted to route each selected vehicle with the assigned customers. The iterated dynasearch algorithm combines dynasearch algorithm with iterated local search algorithm based on random kicks. The second methodplogy adopts the idea of cyclic transfer which is performed by using dynamic programming algorithm, and the iterated dynasearch algorithm is also embedded in it. The test results show that both methodologies generate better solutions than the traditional method, and the second methodology is superior to the first one. 展开更多
关键词 Vehicle routing problem Savings method Iterated dynasearch algorithm Dynamic programming Iterated local search Random kick Cyclic transfer
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