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Variable neighbourhood search for close-open vehicle routing problem with time windows

IMA JOURNAL OF MANAGEMENT MATHEMATICS 2016年第1期27卷 25-38页

作者： Brito, Julio Exposito, Airam Moreno, Jose A. Univ La Laguna IUDR San Cristobal la Laguna 38271 Spain

The close-open vehicle routing problem (VRP) with time windows is a realistic variant of the classical VRP with customers that must be served in a specific time interval and where some routes are open and others are closed. A typical situation in logistics and transport activities is when many companies opt for collaboration and recruitment of other specialist companies to carry them out. The company has its own fleet of vehicles and hires the services of other companies that provide complementary resources for specific service needs or business strategy. The aim of this paper is to test the variable neighbourhood search algorithm for solving this problem. In this paper, we present some results using different strategies to obtain the neighbourhoods, applying real problem instances and some Solomon instances.

关键词： variable neighbourhood search vehicle routing problem with time windows close-open vehicle routing problem

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Simultaneous product and service delivery vehicle routing problem with time windows and order release dates

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APPLIED MATHEMATICAL MODELLING 2021年第Part1期89卷 669-687页

作者： Li, Wenli Li, Kunpeng Kumar, P. N. Ram Tian, Qiannan Wuhan Text Univ Sch Management Sunshine Ave Wuhan 430200 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Management 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Indian Inst Management Kozhikode Quantitat Methods & Operat Management Area IIMK Campus Kozhikode 673570 Kerala India Hubei Univ Econ Hubei Logist Dev Res Ctr 8 Yangqiaohu Rd Wuhan 430205 Hubei Peoples R China

In this paper, a new variant of the well-known vehicle routing problem with time windows (VRPTW) called as the simultaneous product and service delivery problem with order release dates is studied. The problem is motivated by the operations of a 3PL service provider who manages the last mile delivery of electrical and electronic home appliances of a large retailing company based out of China. Apart from the usual constraints on time windows and vehicle capacities, an interesting characteristic of this problem is that the product and service delivery has to happen simultaneously with the explicit consideration of order release dates. This situation necessitates the presence of a skilled service technician at the time of product delivery itself. Being a proven NP-hard problem, we propose a mixed-integer linear programming (MILP) formulation for the problem. For solving large problem instances, an adaptive large neighborhood search (ALNS) algorithm is developed with three new customized removal operators. The proposed approaches are validated using real-life data as well as benchmark problem instances available in the literature. The computational results are quite encouraging and demonstrate the superior performance of the proposed algorithm in terms of computational time as well as the quality of the solutions generated. We also analyze the effect of length of time windows and compare simultaneous and separate modes of delivery and identify favorable conditions for choosing one mode over the other. (C) 2020 Published by Elsevier Inc.

关键词： Simultaneous product and service delivery Order release date vehicle routing problem with time windows Home appliances delivery and installation service Adaptive large neighborhood search algorithm

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Partial recharge strategies for the electric vehicle routing problem with time windows

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2016年 65卷 111-127页

作者： Keskin, Merve Catay, Bulent Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey

The Electric vehicle routing problem with time windows (EVRPTW) is an extension to the well-known vehicle routing problem with time windows (VRPTW) where the fleet consists of electric vehicles (EVs). Since EVs have limited driving range due to their battery capacities they may need to visit recharging stations while servicing the customers along their route. The recharging may take place at any battery level and after the recharging the battery is assumed to be full. In this paper, we relax the full recharge restriction and allow partial recharging (EVRPTW-PR), which is more practical in the real world due to shorter recharging duration. We formulate this problem as a 0-1 mixed integer linear program and develop an Adaptive Large Neighborhood Search (ALNS) algorithm to solve it efficiently. We apply several removal and insertion mechanisms by selecting them dynamically and adaptively based on their past performances, including new mechanisms specifically designed for EVRPTW and EVRPTW-PR. These new mechanisms include the removal of the stations independently or along with the preceding or succeeding customers and the insertion of the stations with determining the charge amount based on the recharging decisions. We test the performance of ALNS by using benchmark instances from the recent literature. The computational results show that the proposed method is effective in finding high quality solutions and the partial recharging option may significantly improve the routing decisions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Electric vehicle vehicle routing problem with time windows Adaptive large neighborhood search Metaheuristics Partial recharge

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Multigraph modeling and adaptive large neighborhood search for the vehicle routing problem with time windows

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COMPUTERS & OPERATIONS RESEARCH 2019年 104卷 113-126页

作者： Ben Ticha, Hamza Absi, Nabil Feillet, Dominique Quilliot, Alain Ecole Mines St Etienne F-13541 Gardanne France CMP Georges Charpak UMR CNRS LIMOS 6158 F-13541 Gardanne France Inst Super Informat Modelisat & Leurs Applicat ISIMA LIMOS Campus Cezeaux Aubiere France

In this paper we propose a multigraph model and a heuristic for the vehicle routing problem with time windows (VRPTW). In the classical VRPTW, travel information is commonly represented with a customer-based graph, where an arc is an abstraction of the best road-network path between two nodes. We consider the case when parallel arcs are added to this graph to introduce different compromises between travel time and cost. It has been shown in the literature that this multigraph modeling enables substantial gains in the solution quality, while highly complicating the problem. We develop an Adaptive Large Neighbourhood Search (ALNS) heuristic in which a special data structure and dynamic programming algorithms are used to efficiently address the multigraph setting. Computational experiments on several set of instances demonstrate the effectiveness of our solution method and the impact of alternative paths on the solution quality. (C) 2018 Published by Elsevier Ltd.

关键词： vehicle routing problem with time windows Multigraph Large neighborhood search Dynamic programming Road-network information

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Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 7758-7763页

作者： Cheng, Chi-Bin Wang, Keng-Pin Tamkang Univ Dept Informat Management Tamsui Taipei County Taiwan Sentien Printing Factory Co Ltd Tainan Taiwan

The present study investigates the cost concerns of distribution centers and formulates a vehicle routing problem with time window constraints accordingly. Based on the embedded structure of the original problem, a decomposition technique is employed to decompose the original problems to a clustering problem (main problem) and a set of traveling salesman problems (sub-problems) with time window constraints. This decomposition not only reduces the problem size but also enable the use of simpler solution procedures. A genetic algorithm is developed to solve the clustering problem, while a simple heuristic algorithm is formulated to solve the set of traveling salesman problems. The solution of the original problem is obtained through iterative interactions between the main problem and the set of subproblems. The performance of the proposed approach is compared with the well-known insertion method and a manual scheduling of a distribution center. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： vehicle routing problem with time windows problem decomposition Traveling salesman problem with time windows Genetic algorithm Insertion method Heuristics

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Multi-Trip Open vehicle routing problem with time windows: A Case Study

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2020年第1期27卷 37-57页

作者： Hashemi, Saeedeh Salari, Majid Ranjbar, Mohammad Ferdowsi Univ Mashhad Dept Ind Engn Mashhad Razavi Khorasan Iran

In this paper, we introduce a practical variation of the standard vehicle routing problem (VRP). The problem is a combination of the multi-trip, open and VRP with time windows. A practical application of the introduced problem is to provide service for university professors. In the studied problem, a set of professors have to teach at a university located in another city. The goal is planning and scheduling services for professors to minimize the total transportation cost with a set of side constraints, including the maximum travel time for each passenger. We provide two mathematical models namely, node-based and scenario-based formulations. Also, we present a heuristic method to produce feasible scenarios for the scenario-based model. Computational results indicate the effectiveness of the models by solving real size instances.

关键词： Multi-trip vehicle routing vehicle routing problem with time windows Open vehicle routing Mixed integer programming

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THE SPLIT DELIVERY vehicle routing problem with time windows AND THREE-DIMENSIONAL LOADING CONSTRAINTS

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第2期20卷 786-807页

作者： Yan, Miao Chu, Liang-Yong Wu, Xin-Cheng Jimei Univ Modern Logist Res Ctr Xiamen 361021 Peoples R China

This paper presents complex variants of the vehicle routing problem: the vehicle routing problem with time windows and Three-Dimensional Loading Constraints (3L-VRPTW) and the Split Delivery vehicle routing problem with time windows and Three-Dimensional Loading Constraints (3LSDVRPTW). The difference between the two problems is whether a customer can be visited in two or more tours. Under the conditions of satisfying customer demands and loading constraints, the 3L-VRPTW model and the 3LSDVRPTW model are constructed with the aim of minimizing transportation costs. To efficiently solve the above problems, a two-layer method is proposed in this study, including a routing stage and a packing stage. The Adaptive Large Neighborhood Search algorithm based on the Metropolis criterion is used to obtain the vehicle routing. The genetic algorithm is used to solve the packing stage, ensuring that the goods are packed in a way that minimizes pre-movements. The proposed algorithms are tested on different instances, verifying their effectiveness. Additionally, numerical experiments are conducted using instances with different customer distributions. The results show that when customers are located in multiple small areas, split delivery it distribution can significantly reduce penalty costs and be more economical.

关键词： vehicle routing problem with time windows split delivery three-dimensional loading constraints adaptive large neighborhood search genetic algorithm

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Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

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SOFT COMPUTING 2016年第6期20卷 2309-2327页

作者： Nalepa, Jakub Blocho, Miroslaw Silesian Tech Univ Inst Informat Akad 16 PL-44100 Gliwice Poland ABB ISDC Zeganska 1 PL-04713 Warsaw Poland

This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with the two-tailed Wilcoxon test for verifying the statistical significance of the results.

关键词： Memetic algorithm Adaptation Parameter control Selection scheme vehicle routing problem with time windows

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A hybrid evolutionary multitask algorithm for the multiobjective vehicle routing problem with time windows

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INFORMATION SCIENCES 2022年 612卷 168-187页

作者： Cai, Yiqiao Cheng, Meiqin Zhou, Ying Liu, Peizhong Guo, Jing-Ming Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Xiamen Key Lab Data Secur & Blockchain Technol Xiamen Fujian Peoples R China Shenzhen Inst Informat Technol Sch Comp Sci Shenzhen 518000 Peoples R China Huaqiao Univ Coll Engn Quanzhou 362000 Peoples R China Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan

The vehicle routing problem with time windows (VRPTW), as an important logistic problem, has been widely investigated in recent decades. However, it is still a great challenge for most existing approaches to solve multiobjective VRPTWs (MOVRPTWs) with many conflicting objectives. In this study, a hybrid evolutionary multitask algorithm, termed HEMT, is proposed to address MOVRPTWs under the framework of evolutionary multitasking, where multiple MOVRPTWs are optimized simultaneously by leveraging the similarity between them. In particular, HEMT is characterized by three aspects: (1) an exploration stage with knowledge transfer (ESKT) is designed to globally explore the search space by transferring knowledge across different MOVRPTWs;(2) an exploitation stage with knowledge reuse (ESKR) is employed to further promote the quality of solutions by conducting local searches and reusing problem-specific knowledge;and (3) a tradeoff mechanism (TOM) is suggested to adaptively switch the search between ESKT and ESKR. Furthermore, to verify the efficacy of the proposed HEMT, four suites of new multitasking instances are generated based on 45 real-world MOVRPTW benchmarks under different multitask environments. The experimental results show that HEMT not only effectively solves multiple MOVRPTWs simultaneously but also achieves better performances than the traditional single-task counterparts.

关键词： vehicle routing problem with time windows Evolutionary multitask optimization Multiobjective optimization Knowledge transfer

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A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows

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INFORMATION SCIENCES 2019年 481卷 311-329页

作者： Marinakis, Yannis Marinaki, Magdalene Migdalas, Athanasios Tech Univ Crete Sch Prod Engn & Management Univ Campus Khania 73100 Greece Aristotle Univ Thessalonike Dept Civil Engn Thessalonike 54124 Greece Lulea Tech Univ Ind Logist S-97187 Lulea Sweden

In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the vehicle routing problem with time windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Particle swarm optimization vehicle routing problem with time windows Combinatorial neighborhood topology Greedy randomized adaptive search procedure Adaptive strategy

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