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vehicle routing problem with time windows and a limited number of vehicles

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2003年第3期148卷 559-569页

作者： Lau, HC Sim, M Teo, KM Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117543 Singapore MIT Ctr Operat Res Cambridge MA 02139 USA Savy Technol Asia Pte Ltd Singapore 469001 Singapore

This paper introduces a variant of the vehicle routing problem with time windows where a limited number of vehicles is given (m-VRPTW). Under this scenario, a feasible solution is one that may contain either unserved customers and/or relaxed time windows. We provide a computable upper bound to the problem. To solve the problem, we propose a tabu search approach characterized by a holding list and a mechanism to force dense packing within a route. We also allow time windows to be relaxed by introducing the notion of penalty for lateness. In our approach, customer jobs are inserted based on a hierarchical objective function that captures multiple objectives. Computational results on benchmark problems show that our approach yields solutions that are competitive to best published results on VRPTW. On m-VRPTW instances, experiments show that our approach produces solutions that are very close to computed upper bounds. Moreover, as the number of vehicles decreases, the routes become more densely packed monotically. This shows that our approach is good from both the optimality as well as stability point of view. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： tabu search heuristics routing combinatorial optimization vehicle routing problem with time windows

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vehicle routing problem with time windows Arising in Urban Delivery 4

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

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4th International Conference on Electrical, Automation and Mechanical Engineering (EAME)

作者： Cao, Can Zhang, Xiangfei Guo, Zhanyuan Chongqing Yucai Middle Sch Chongqing 400050 Peoples R China

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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ADMM-based problem decomposition scheme for vehicle routing problem with time windows

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2019年 129卷 156-174页

作者： Yao, Yu Zhu, Xiaoning Dong, Hongyu Wu, Shengnan Wu, Hailong Tong, Lu Carol Zhou, Xuesong Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Beijing Jingdong Zhenshi Informat Technol Co Ltd Logist R&D Dept Beijing 100176 Peoples R China Beihang Univ Res Inst Frontier Sci Natl Engn Lab Comprehens Transportat Big Data App Beijing 100091 Peoples R China Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85281 USA

Emerging urban logistics applications need to address various challenges, including complex traffic conditions and time-sensitive requirements. In this study, in the context of urban logistics, we consider a vehicle routing problem with time-dependent travel times and time windows (VRPTW), and the goal is to minimize the total generalized costs including the transportation, waiting time, and fixed costs associated with each vehicle. We adopt a high-dimensional space-time network flow model to formulate an underlying vehicle routing problem (VRP) with a rich set of criteria and constraints. A difficult issue, when solving VRPs, is how to iteratively improve both the primal and dual solution quality in general and how to break the symmetry generated by many identical solutions, particularly with homogeneous vehicles. Along this line, many coupling constraints, such as the consensus constraints across different agents or decision makers, need to be carefully addressed to find high-quality optimal or close-to-optimal solutions under medium- or large-scale instances. Currently, the alternating direction method of multipliers (ADMM) is widely used in the field of convex optimization, as an integration of the augmented Lagrangian relaxation and block coordinate descent methods, for machine learning and large-scale continuous systems optimization and control. In this work, we introduce the use of ADMM to solve the multi-VRP, which is a special case of integer linear programming, and demonstrate a manner to reduce the quadratic penalty terms used in ADMM into simple linear functions. In a broader context, a computationally reliable decomposition framework is developed to iteratively improve both the primal and dual solution quality. Essentially, the least-cost path subproblem or other similar subproblems involving binary decisions can be embedded into a sequential solution scheme with an output of both lower bound estimates and upper bound feasible solutions. We examine the

关键词： Urban logistics vehicle routing problem with time windows Alternating direction method of multipliers problem decomposition

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An improved ant colony optimization and its application to vehicle routing problem with time windows

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NEUROCOMPUTING 2012年 98卷 101-107页

作者： Ding, Qiulei Hu, Xiangpei Sun, Lijun Wang, Yunzeng Dongbei Univ Finance & Econ Sch Business Adm Dalian 116025 Peoples R China Dalian Univ Technol Inst Syst Engn Dalian 116023 Peoples R China Univ Calif Riverside A Gary Anderson Grad Sch Management Riverside CA 92521 USA

The ant colony optimization (ACO), inspired from the foraging behavior of ant species, is a swarm intelligence algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application. To improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented in this paper. By adjusting pheromone approach and introducing a disaster operator, the HACO prevents the search process from getting trapped in the local optimal solution. Then, by taking the candidate list into consideration and combining the ACO with the saving algorithm and A-interchange mechanism, the HACO improves the convergence speed. Furthermore, this paper gives a guideline on how to adjust the parameters to achieve the good performance of the algorithm. Finally, the HACO is applied to solve the vehicle routing problem with time windows. The effectiveness of the HACO on solving combinatorial optimization problems is validated by comparing the computational results with those previously presented in the literature. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Ant colony optimization vehicle routing problem with time windows Combinatorial optimization problems

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A HEURISTIC ALGORITHM OF vehicle routing problem with time windows AND LESS-THAN-TRUCKLOAD CARRIER SELECTION

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2017年第2期25卷 129-141页

作者： Wu, Chao-Sheng Chug, Ching-Wu Hsu, Hsiu-Li Taipei Chengshih Univ Sci & Technol Dept Mkt & Logist Taipei Taiwan Natl Taiwan Ocean Univ Dept Shipping & Transportat Management Keelung Taiwan Taipei Coll Maritime Technol Dept Air & Sea Logist & Mkt New Taipei Taiwan

The vehicle routing problem with time windows is an important and practical problem for logistics managers. In reality, when facing fluctuations of demand, logistics managers may consider using an outside carrier to satisfy partial customer demand during the peak season. That is, the logistics managers must make a selection between a truckload (a private truck) and a less-than-truckload carrier (an outside carrier). Selecting the right mode to transport a shipment may bring significant cost savings to the company. In this paper, we address the problem of routing a fixed number of trucks from a central warehouse to customers with known demand and time windows. A heuristic algorithm is developed for routing the private trucks with time windows and for selecting of less-than-truckload carriers by minimizing the total cost function. Computational results are encouraging and some suggestions for future research are presented.

关键词： vehicle routing problem with time windows less-than-truckload Heuristics logistics 0-1 integer programming

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Modeling rolling batch planning as vehicle routing problem with time windows

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COMPUTERS & OPERATIONS RESEARCH 1998年第12期25卷 1127-1136页

作者： Chen, X Wan, WS Xu, XH Northeastern Univ Dept Automat Control Shenyang 110006 Peoples R China Northeastern Univ Dept Math Shenyang 110006 Lianoning Peoples R China

In this paper, the vehicle routing problem with time window (VRPTW) of combinational optimization is used to analyze and model the rolling batch planning problem. Genetic algorithm and heuristic are used to solve the problem. Simulation results based on the actual production data show that this model is precise and the genetic algorithm based method is very promising. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： rolling batch planning vehicle routing problem with time windows genetic algorithm heuristics

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vehicle capacity planning system: A case study on vehicle routing problem with time windows

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2003年第2期33卷 169-178页

作者： Lee, LH Tan, KC Ou, K Chew, YH Natl Univ Singapore Dept Ind & Syst Engn Singapore 117548 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

In this paper, we consider a local logistic company that provides transportation service for moving empty and laden containers within Singapore. Due to the limited capacity of its own fleet of vehicles, the company cannot handle all the job orders and have to outsource some orders to other smaller local transportation companies. The current operation of assigning jobs for outsourcing goes through two steps. In the first step, a certain percentage of jobs will be preselected for outsourcing according to some simple rules. Then at the second step, the rest of the jobs will be put into an in-house computer system which assigns jobs to its internal fleet of vehicles according to some greedy rules and the remaining jobs that cannot be served by the internal fleet of vehicles will be outsourced. This paper presents a vehicle capacity planning system (VCPS), which models the problem as a vehicle routing problem with time window constraints (VRPTW) and tabu search (TS) is applied to find a solution for the problem. From the simulation results, some new rules on how to assign jobs for outsourcing are derived, which are shown to be about 8% better than existing rules currently adopted by the company.

关键词： tabu search vehicle capacity planning vehicle routing problem with time windows

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A Stochastic and Dynamic vehicle routing problem with time windows and Customer Impatience

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MOBILE NETWORKS & APPLICATIONS 2009年第3期14卷 350-364页

作者： Pavone, M. Bisnik, N. Frazzoli, E. Isler, V. MIT Informat & Decis Syst Lab Cambridge MA 02139 USA MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA Rensselaer Polytech Inst Troy NY 12180 USA

In this paper, we study the problem of designing motion strategies for a team of mobile agents, required to fulfill request for on-site service in a given planar region. In our model, each service request is generated by a spatio-temporal stochastic process;once a service request has been generated, it remains active for a certain deterministic amount of time, and then expires. An active service request is fulfilled when one of the mobile agents visits the location of the request. Specific problems we investigate are the following: what is the minimum number of mobile agents needed to ensure that a certain fraction of service requests is fulfilled before expiration? What strategy should they use to ensure that this objective is attained? This problem can be viewed as the stochastic and dynamic version of the well-known vehicle routing problem with time windows. We also extend our analysis to the case in which the time service requests remain active is itself a random variable, describing customer impatience. The customers' impatience is only known to the mobile agents via prior statistics. In this case, it is desired to minimize the fraction of service requests missed because of impatience. Finally, we show how the routing strategies presented in the paper can be executed in a distributed fashion.

关键词： mobile robotic networks sensor networks traveling salesman problem vehicle routing problem with time windows customer impatience

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Adapted large neighborhood search algorithm with optimized golden ratio spiral initial solution for capacitated vehicle routing problem with time windows

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2024年第2期39卷 797-810页

作者： Celikdin, Alperen Ekrem Sutas Aksaray Integrated Facil Aksaray Turkiye

Purpose: In this study, it is aimed to make an adaptation of the large neighborhood search heuristic, where the initial solution is started by optimizing the golden ratio spiral to return from the closest points to the customers, instead of random *** and Methods: The golden ratio, which is frequently encountered in nature, art and architecture, has been used to find the initial solution to the vehicle routing problem. The equation that creates the spiral turns has been transferred to a mathematical model and optimized to cluster the customers close to golden ratio at ***: It is seen that the proposed method improves a total of 15 test problems up to 6.53% in problem classes R1, R2, RC1 and ***: Initial solution clustering procedure which helps to find good feasible solutions obtained. It can be said that the method can be beneficial in reducing transportation costs in sectors with non-clustered, narrow time windows and randomly distributed customers.

关键词： vehicle routing problem with time windows Optimized golden spiral clustering Adapted large neighborhood search Heuristic algorithm

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A Hybrid Swarm Intelligence Algorithm for vehicle routing problem with time windows

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IEEE ACCESS 2020年 8卷 93882-93893页

作者： Shen, Yang Liu, Mingde Yang, Jian Shi, Yuhui Middendorf, Martin Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Univ Leipzig Dept Comp Sci D-04009 Leipzig Germany

The vehicle routing problem with time windows (VRPTW) has drawn considerable attention in the last decades. The objective of VRPTW is to find the optimal set of routes for a fleet of vehicles in order to serve a given set of customers within capacity and time window constraints. As a combinatorial optimization problem, VRPTW is proved NP-hard and is best solved by heuristics. In this paper, a hybrid swarm intelligence algorithm by hybridizing Ant Colony System (ACS) and Brain Storm Optimization (BSO) algorithm is proposed, to solve VRPTW with the objective of minimizing the total distance. In the BSO procedure, both inter-route and intra-route improvement heuristics are introduced. Experiments are conducted on Solomon's 56 instances with 100 customers benchmark, the results show that 42 out of 56 optimal solutions (18 best and 24 competitive solutions) are obtained, which illustrates the effectiveness of the proposed algorithm.

关键词： Ant colony system brain storm optimization heuristics swarm intelligence vehicle routing problem with time windows

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