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A novel genetic algorithm for vehicle routing problem with time windows

Journal of Harbin Institute of Technology(New Series) 2010年第3期17卷 437-444页

作者：刘云忠 Statistic School Xi'an University of Finance and Economics

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.

关键词： genetic algorithm multiple species neural network premature problem vehicle routing problem with time windows

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NSGAII enhanced with a local search for the vehicle routing problem with time windows and synchronization constraints

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IFAC-PapersOnLine 2016年第12期49卷 1198-1203页

作者： Haddadene, S.R. Ait Labadie, N. Prodhon, C. ICD-LOSI University of Technology of Troyes CNRS-UMR 6281 12 Rue Marie Curie Troyes CedexCS42060-10004 France

In the field of Home Health Care (HHC), the vehicle routing problem with time windows, synchronization, and precedence constraints (VRPTW-SP) involves finding the lowest-cost set of routes to visit patients requesting one or more services in a specific time window and during a fixed time service. Each patient has preference weights associated to the vehicles who are available to provide the requested service. To solve the problem, different variations for multi-objective met a-heuristic based on the Non-dominated Sorting Genetic Algorithm (NSGAII) are proposed. The considered objectives are the minimization of travel costs and the maximization of patients preferences. This problem was solved in a previous work as a single-objective variant, despite the conflicting nature of these two criteria. Numerical results are presented for instances with up to 73 patients. Metrics such as spacing, hyper-volume and the number of non dominated solutions are used to measure the quality of solutions. © 2016

关键词： problem solving Genetic algorithms Home health care Local search (optimization) Multiobjective optimization Synchronization vehicle routing vehicles Met a heuristics Non dominated sorting genetic algorithms Nondominated solutions Precedence constraints Quality of solution routing problems Synchronization constraints vehicle routing problem with time windows

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A Hybrid Iterative Local Search Algorithm for The Electric Fleet Size and Mix vehicle routing problem with time windows and Recharging Stations

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IFAC-PapersOnLine 2016年第12期49卷 955-960页

作者： Vaz Penna, Puca Huachi Afsar, H. Murat Prins, Christian Prodhon, Caroline Universidade Federal de Ouro Preto Departamento de Computação Campus Morro do Cruzeiro s/n Ouro Preto-MG 35400-000 Brazil ICD-LOSI UMR CNRS 6281 Université de Technologie de Troyes 12 Marie Currie CS 42060 Troyes CEDEX10004 France

As the cities around the world become larger, quality of life of the citizens is more and more threatened due to the traffic congestion, energy consumption, noise disturbance and carbon emissions because of of increasing transport. Electrical vehicles present an opportunity to reduce greenhouse gas emissions. But limited driving range and long recharging times are among the challenges that the research community has to face. This paper proposes an iterative local search algorithm coupled with a set partitioning model to solve The Electric Fleet Size and Mix vehicle routing problem with time windows and Recharging Stations. Several types of electrical vehicles with varying driving range, capacity and fixed cost should service a set of customers within their time limits and during their tours each vehicle can be recharged in stations. We show the efficiency and the quality of our method by solving benchmark instances of the Heterogeneous Fleet Electric vehicle problems with time windows. © 2016

关键词： Fleet operations Benchmarking Energy utilization Gas emissions Greenhouse gases Iterative methods Learning algorithms Local search (optimization) routing algorithms Traffic congestion vehicle routing vehicles Electrical vehicles Fleet size and mixes Heterogeneous fleet Iterative local searches Metaheuristic Noise disturbance Research communities vehicle routing problem with time windows

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A multi-compartment vehicle routing problem with time windows for urban distribution - A comparison study on particle swarm optimization algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年第0期133卷 95-106页

作者： Chen, Jiumei Shi, Jing Chongqing Technol & Business Univ Sch Business Planning Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Elect Commerce & Supply Chain S Chongqing 400067 Peoples R China Univ Cincinnati Coll Engn & Appl Sci Dept Mech & Mat Engn Cincinnati OH 45221 USA

In this paper, we introduce a multi-compartment vehicle routing problem with time window (MCVRPTW) arising from urban distribution, which essentially reflects the "last mile" delivery challenge for modern logistics. The distinguishing feature of MCVRPTW is that products must be transported in independent vehicle compartments because they cannot be mixed together due to differences in their characteristics. Based on the mathematical formulation established for the optimization problem, we propose and compare two solution approaches, with one being the hybrid particle swarm optimization (HPSO) with simulated annealing, and the other being the conventional particle swarm optimization (PSO). Based on Solomon's vehicle routing problem with time windows (Solomon, 1987), experimental instances with 25 customers, 50 customers and 100 customers are developed to investigate the performance of the proposed solution approaches. The results indicate that both approaches are reasonably efficient for the MCVRPTW. Moreover, the HPSO algorithm has overall better performance, particularly in delivering the best solution, for all cases, and the HPSO algorithm becomes more efficient than the PSO algorithm as the problem size increases. On the other hand, the PSO algorithm shows a slight edge in terms of the worst solution and standard deviation.

关键词： Multi-compartment vehicle routing problem vehicle routing problem with time windows Hybrid particle swarm optimization Simulated annealing Urban distribution

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A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

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COMPUTERS & OPERATIONS RESEARCH 2015年第Oct.期62卷 61-77页

作者： Qi, Yutao Hou, Zhanting Li, He Huang, Jianbin Li, Xiaodong Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Xidian Univ Sch Software Xian Peoples R China RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic Australia

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems. It decomposes a target problem into a set of scalar sub-problems and optimizes them simultaneously. Due to its simplicity and outstanding performance, MOEA/D has been widely studied and applied. However, for solving the multi-objective vehicle routing problem with time windows (MO-VRPTW), MOEA/D faces a difficulty that many sub-problems have duplicated best solutions. It is well-known that MO-VRPTW is a challenging problem and has very few Pareto optimal solutions. To address this problem, a novel selection operator is designed in this work to enhance the original MOEA/D for dealing with MO-VRPTW. Moreover, three local search methods are introduced into the enhanced algorithm. Experimental results indicate that the proposed algorithm can obtain highly competitive results on Solomon's benchmark problems. Especially for instances with long time windows, the proposed algorithm can obtain more diverse set of non-dominated solutions than the other algorithms. The effectiveness of the proposed selection operator is also demonstrated by further analysis. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Memetic algorithm Decomposition vehicle routing problem with time windows

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A Multi-Trip Split-Delivery vehicle routing problem with time windows for Inventory Replenishment Under Stochastic Travel times

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NETWORKS & SPATIAL ECONOMICS 2017年第1期17卷 41-68页

作者： Chu, James C. Yan, Shangyao Huang, Han-Jheng Natl Taiwan Univ Dept Civil Engn Taipei Taiwan Natl Cent Univ Dept Civil Engn 300 Jhongda Rd Jhongli 32001 Taiwan Natl Cent Univ Dept Civil Engn Jhongli Taiwan

This study considers a multi-trip split-delivery vehicle routing problem with soft time windows for daily inventory replenishment under stochastic travel times. Considering uncertainty in travel times for vehicle routing problems is beneficial because more robust schedules can be generated and unanticipated consequences can be reduced when schedules are implemented in reality. However, uncertainties in model parameters have rarely been addressed for the problems in this category mainly due to the high problem complexity. In this study, an innovative and practical approach is proposed to consider stochastic travel times in the planning process. In the planning model, the possible outcomes of vehicle arrivals and product delivery at retailers are systematically categorized and their associated penalty and reward are estimated. Thus, unanticipated costs for every scheduling decision can be incorporated into the planning model to generate vehicle routing schedules that are more robust facing uncertain traffic conditions. To solve the model that is characterized as an NP-hard problem in a reasonable amount of time, a two-stage heuristic solution algorithm is proposed. Finally, the stochastic model is compared with the deterministic model in both planning and simulated operation stages using the data of a supply chain in Taiwan. The result confirms that the schedule generated by the stochastic model is more robust than the one created with the deterministic model because undesired outcomes such as unfulfilled demands are greatly reduced.

关键词： Multi-trip split-delivery vehicle routing problem with time windows Stochastic travel time time-space network Heuristic

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Multiobjective memetic algorithm based on adaptive local search chains for vehicle routing problem with time windows

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EVOLUTIONARY INTELLIGENCE 2022年第4期15卷 2283-2294页

作者： Zhang, Kaikai Cai, Yiqiao Fu, Shunkai Zhang, Huizhen Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China

This paper presents a multiobjective memetic algorithm based on adaptive local search chains (MMA-ALSC) for vehicle routing problem with time window (VRPTW) which is an important research area in logistics. As shown in most previous studies, VRPTW is essentially a multiobjective optimization problem and can be solved effectively by the multiobjective algorithms with various local search operators. We have observed, however, that the promising solutions obtained during the process of evolution are not fully utilized to guide the search together with different local search operators. This will lead to the discontinuous and insufficient search in the regions showing promise. To alleviate this drawback, MMA-ALSC is proposed and characterized by combining a multi-directional local search strategy (MD-LS) with an enhanced local search chain technique (eLS-Chain). In MMA-ALSC, on the one hand, with MD-LS, different local search operators are designed to perform the search towards multiple directions with distinct problem-specific knowledge of multiobjective VRPTW (MOVRPTW). On the other hand, with eLS-Chain, the promising solutions obtained during the process of evolution are adaptively selected for the subsequent local search operators. In this way, MMA-ALSC can not only effectively explore the search space in multiple directions, but also fully exploit the promising solutions in a chain-based way. Experimental results on two suites of benchmark instances have demonstrated the competitive performance of MMA-ALSC when compared with other representative algorithms.

关键词： vehicle routing problem with time windows Multiobjective optimization Local search chains Multi-directional search strategy Combinatorial optimization

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A discrete salp swarm algorithm for the vehicle routing problem with time windows

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第6期16卷 552-563页

作者： Chen, Huajun Cai, Yanguang Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

The emphasis on this paper is about implementing an algorithm for the vehicle routing problem with time windows (VRPTW), which is a key class of vehicle routing problem. Due to its NP-hard feature, it is quite difficult to solve it within an acceptable calculation time. As a result, many metaheuristics have been developed to find suboptimal solutions to VRPTW. To settle the VRPTW, a discrete salp swarm algorithm (DSSA) is put forward. To prove the property of this algorithm in solving solutions, the algorithm is applied to the Solomon benchmark. Compared with existing methods, DSSA provides relatively better results in total distance.

关键词： DSSA discrete salp swarm algorithm VRPTW vehicle routing problem with time windows local search optimal solution random exchange

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A vehicle routing problem with time windows Approach for Planning Service Operations in Gas Distribution Network of a Metropolitan Area

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IFAC-PapersOnLine 2016年第12期49卷 1365-1370页

作者： Anghinolfi, Davide Paolucci, Massimo Tonelli, Flavio Department of Informatics Bio engineering Robotics and Systems Engineering University of Genova Via Opera Pia 13 Genova16145 Italy Department of Mechanical Engineering Energy Management and Transports University of Genova Via Opera Pia 15 Genova16145 Italy

Service operations management of metropolitan gas networks at operational level implies the optimization of decisions related to logistic activities, taking into account multi-objectives and operational constraints. This paper proposes a metaheuristic approach for the operational planning of the daily logistic activities based on vehicle routing with time window model. Experimental results for a real planning case in a gas distribution network demonstrate the approach effectiveness. © 2016

关键词： vehicle routing Gases Optimization Planning vehicles Gas distribution network Meta heuristic approach Service operations Service operations management time windows vehicle routing problem with time windows vehicle routing problems vehicle routing with time windows

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vehicle routing problem with time windows Arising in Urban Delivery

Vehicle Routing Problem with Time Windows Arising in Urban D...

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作者： Can Cao Xiangfei Zhang Zhanyuan Guo Chongqing Yucai Middle School

Delivery is an important part of logistics operation. The way to plan the vehicle route with consideration of the demand, time window and total travel cost will directly affect the operation cost and the efficiency of delivery. In this paper, a mathematical model of the vehicle routing with time windows arising in urban delivery was formulated, and Clarke and Wright saving algorithm was used to solve it. A real case of a company was used to test its performance. The result shows that the Clarke and Wright saving algorithm is more effective than the actual operation of the company.

关键词： vehicle routing problem with time windows Clarke and Wright saving algorithm Urban delivery

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