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Journal articles on the topic 'Vehicle routing software'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Golden, Bruce L., Lawrence Bodin, and Terry Goodwin. "Microcomputer-based vehicle routing and scheduling software." Computers & Operations Research 13, no. 2-3 (January 1986): 277–85. http://dx.doi.org/10.1016/0305-0548(86)90012-2.

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2

Letchford, Adam N., and Juan-José Salazar-González. "Projection results for vehicle routing." Mathematical Programming 105, no. 2-3 (October 12, 2005): 251–74. http://dx.doi.org/10.1007/s10107-005-0652-x.

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3

GUPTA, ARVIND, and RAMESH KRISHNAMURTI. "PARALLEL ALGORITHMS FOR VEHICLE ROUTING PROBLEMS." Parallel Processing Letters 13, no. 04 (December 2003): 673–87. http://dx.doi.org/10.1142/s0129626403001598.

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Vehicle routing problems involve the navigation of one or more vehicles through a network of locations. Locations have associated handling times as well as time windows during which they are active. The arcs connecting locations have time costs associated with them. In this paper, we consider two different problems in single vehicle routing. The first is to find least time cost routes between all pairs of nodes in a network for navigating vehicles; we call this the all pairs routing problem. We show that there is an O( log 3 n) time parallel algorithm using a polynomial number of processors for this problem on a CREW PRAM. We next consider the problem in which a vehicle services all locations in a network. Here, locations can be passed through at any time but only serviced during their time window. The general problem is [Formula: see text] -complete under even fairly stringent restrictions but polynomial algorithms have been developed for some special cases. In particular, when the network is a line, there is no time cost in servicing a location, and all time windows are unbounded at either their lower or upper end, O(n2) algorithms have been developed. We show that under the same conditions, we can reduce this problem to the all pairs routing problem and therefore obtain an O( log 3 n) time parallel algorithm on a CREW PRAM.

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4

Amalia, Atika Dwi Hanun, Herry Suprajitno, and Asri Bekti Pratiwi. "Solving Close-Open Mixed Vehicle Routing Problem Using Bat Algorithm." Contemporary Mathematics and Applications (ConMathA) 2, no. 1 (May 20, 2020): 46. http://dx.doi.org/10.20473/conmatha.v2i1.19301.

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The purpose of this research is to solve the Close-Open Mixed Vehicle Routing Problem (COMVRP) using Bat Algorithm. COMVRP which is a combination of Close Vehicle Routing Problem or commonly known as Vehicle Routing Problem (VRP) with Open Vehicle Routing Problem (OVRP) is a problem to determine vehicles route in order to minimize total distance to serve customers without exceed vehicle capacity. COMVRP occurs when the company already has private vehicles but its capacity could not fulfill all customer demands so the company must rent several vehicles from other companies to complete the distribution process. In this case, the private vehicle returns to the depot after serving the last customer while the rental vehicle does not need to return. Bat algorithm is an algorithm inspired by the process of finding prey from small bats using echolocation. The implementation program to solve was created using Java programming with NetBeans IDE 8.2 software which was implemented using 3 cases, small data with 18 customers, medium data with 75 customers and large data with 100 customers. Based on the implementation results, it can be concluded that the more iterations, the smaller total costs are obtained, while for the pulse rate and the amount of bat tends not to affect the total cost obtained.

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5

Kaku, Ikou, Yiyong Xiao, and Guoping Xia. "The Deterministic Annealing Algorithms for Vehicle Routing Problems." International Journal of Smart Engineering System Design 5, no. 4 (October 2003): 327–39. http://dx.doi.org/10.1080/10255810390224080.

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6

Sztajerowski, Wiktor, Joanna Ochelska-Mierzejewska, and Jacek Kucharski. "SYSTEM FOR VEHICLE ROUTING PROBLEM ALGORITHMS ANALYSIS." Informatics Control Measurement in Economy and Environment Protection 7, no. 2 (June 30, 2017): 28–31. http://dx.doi.org/10.5604/01.3001.0010.4833.

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Paper concerns the software system supporting the analysis of different cases of solving VRP by various algorithms. VRP has been characterised and application structure has been presented. Illustrative experimental results show the usefulness of the system.

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7

AbdAllah, Abdel Monaem F. M., Daryl L. Essam, and Ruhul A. Sarker. "On solving periodic re-optimization dynamic vehicle routing problems." Applied Soft Computing 55 (June 2017): 1–12. http://dx.doi.org/10.1016/j.asoc.2017.01.047.

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8

Altabeeb, Asma M., Abdulqader M. Mohsen, Laith Abualigah, and Abdullatif Ghallab. "Solving capacitated vehicle routing problem using cooperative firefly algorithm." Applied Soft Computing 108 (September 2021): 107403. http://dx.doi.org/10.1016/j.asoc.2021.107403.

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9

Yuliza, Evi, Fitri Maya Puspita, and Siti Suzlin Supadi. "The robust counterpart open capacitated vehicle routing problem with time windows on waste transport problems." Bulletin of Electrical Engineering and Informatics 9, no. 5 (October 1, 2020): 2074–81. http://dx.doi.org/10.11591/eei.v9i5.2439.

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The optimum route for garbage transport vehicles is restricted by vehicle capacity and time windows that the garbage transport vehicle starts at the origin and does not return to the origin. The problem of transporting waste routes is a robust optimization problem where the amount of waste in an area and travel time is uncertain. In the real world, traffic jams and vehicle engine damage can cause delays. This paper proposes the robust counterpart open capacitated vehicle routing problem (denoted by RCOCVRP) with soft time windows model. The aim of RCOCVRP with soft time windows model is to find schedule and optimum route of transporting waste. This model calculation uses LINGO software and GAMS software. Finally for the evaluation of the RCOCVRP model with soft time windows on the proposed waste transportation problem is conducted so that it hasa feasible solution.

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10

Cornuejols, Gerard, and Farid Harche. "Polyhedral study of the capacitated vehicle routing problem." Mathematical Programming 60, no. 1-3 (June 1993): 21–52. http://dx.doi.org/10.1007/bf01580599.

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11

Altabeeb, Asma M., Abdulqader M. Mohsen, and Abdullatif Ghallab. "An improved hybrid firefly algorithm for capacitated vehicle routing problem." Applied Soft Computing 84 (November 2019): 105728. http://dx.doi.org/10.1016/j.asoc.2019.105728.

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12

Ursani, Ziauddin, Daryl Essam, David Cornforth, and Robert Stocker. "Localized genetic algorithm for vehicle routing problem with time windows." Applied Soft Computing 11, no. 8 (December 2011): 5375–90. http://dx.doi.org/10.1016/j.asoc.2011.05.021.

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13

Gørtz, Inge Li, and Viswanath Nagarajan. "Locating depots for capacitated vehicle routing." Networks 68, no. 2 (June 20, 2016): 94–103. http://dx.doi.org/10.1002/net.21683.

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14

Spliet, Remy, and Rommert Dekker. "The driver assignment vehicle routing problem." Networks 68, no. 3 (August 4, 2016): 212–23. http://dx.doi.org/10.1002/net.21694.

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15

Campbell, Ann Melissa, and Jill Hardin Wilson. "Forty years of periodic vehicle routing." Networks 63, no. 1 (October 1, 2013): 2–15. http://dx.doi.org/10.1002/net.21527.

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16

Campbell, Ann Melissa, and Jill Hardin Wilson. "Forty years of periodic vehicle routing." Networks 63, no. 3 (March 20, 2014): 276. http://dx.doi.org/10.1002/net.21544.

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17

Quintana, Lisandra, Yalexa Herrera-Mena, José-Luis Martínez-Flores, Marcos Coronado, Gisela Montero, and Patricia Cano-Olivos. "DESIGN OF WASTE VEGETABLE OIL COLLECTION NETWORKS APPLYING VEHICLE ROUTING PROBLEM AND SIMULTANEOUS PICKUP AND DELIVERY." Acta logistica 7, no. 4 (December 31, 2020): 261–68. http://dx.doi.org/10.22306/al.v7i4.188.

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The growth of industrialization in Mexico has caused an increase in the demand for materials to satisfy the consumption of goods and services of a growing population. Given this scenario, there is a rise of the residual generation with affectations on the ecosystem and population health. Hence, the objective of this research was to design a network for waste vegetable oil collection based on vehicle routing problem with simultaneous pickup and delivery, starting from a distribution centre to 49 restaurants, as the generation sources of waste vegetable oil. The Vehicle Routing Problem Simultaneous Pickup and Delivery with Time Windows was the variant used as a vehicle routing method to solve the problem. The free software VPRPD was the tool used to solve the vehicle routing problem with simultaneous pickup and delivery that allowed to specify time restrictions. This software uses the simulated annealing metaheuristics in its syntax. As a result, it was obtained a total of 8 networks, for a vehicle capacity utilization of 70 percent in the 6 t vehicle and 46 percent in the 8 t vehicle.

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18

Dinh, Thai, Ricardo Fukasawa, and James Luedtke. "Exact algorithms for the chance-constrained vehicle routing problem." Mathematical Programming 172, no. 1-2 (May 3, 2017): 105–38. http://dx.doi.org/10.1007/s10107-017-1151-6.

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19

Krumke, Sven O., Sleman Saliba, Tjark Vredeveld, and Stephan Westphal. "Approximation algorithms for a vehicle routing problem." Mathematical Methods of Operations Research 68, no. 2 (May 8, 2008): 333–59. http://dx.doi.org/10.1007/s00186-008-0224-y.

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20

Ochoa-Ortíz, Alberto, Francisco Ornelas-Zapata, Lourdes Margain-Fuentes, Miguel Gastón Cedillo-Campos, Jöns Sánchez-Aguilar, Rubén Jaramillo-Vacio, and Isabel Ávila. "Capacitated vehicle routing problem for PSS uses based on ubiquitous computing: An emerging markets approach." DYNA 82, no. 191 (June 22, 2015): 20–26. http://dx.doi.org/10.15446/dyna.v82n191.51141.

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The vehicle routing problem under capacity constraints based on ubiquitous computing in a perspective of deploying PSS (Product-Service Systems) configurations for urban goods transport, is addressed. It takes into account the specificities of city logistics under an emerging markets context. In this case, it involved: i) low logistical capabilities of decision makers; ii) limited availability of data; and iii) restricted access to high performance technology to compute optimal transportation routes. Therefore, the use of free download software providing inexpensive solutions (time and resources) is proposed. The paper shows the implementation of results to a software tool based on Graph Theory used to analyze and solve a CVRP (Capacitated Vehicle Routing Problem). The case of a local food delivery company located in a large city in Mexico was used. Based on small fleet vehicles with the same technical specifications and comparable load capacity.

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21

Expósito, Airam, Julio Brito, José A. Moreno, and Christopher Expósito-Izquierdo. "Quality of service objectives for vehicle routing problem with time windows." Applied Soft Computing 84 (November 2019): 105707. http://dx.doi.org/10.1016/j.asoc.2019.105707.

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22

Reed, Martin, Aliki Yiannakou, and Roxanne Evering. "An ant colony algorithm for the multi-compartment vehicle routing problem." Applied Soft Computing 15 (February 2014): 169–76. http://dx.doi.org/10.1016/j.asoc.2013.10.017.

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23

Abdulkader, Mohamed M. S., Yuvraj Gajpal, and Tarek Y. ElMekkawy. "Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem." Applied Soft Computing 37 (December 2015): 196–203. http://dx.doi.org/10.1016/j.asoc.2015.08.020.

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24

Gunawan, Aldy, Audrey Tedja Widjaja, Pieter Vansteenwegen, and Vincent F. Yu. "A matheuristic algorithm for the vehicle routing problem with cross-docking." Applied Soft Computing 103 (May 2021): 107163. http://dx.doi.org/10.1016/j.asoc.2021.107163.

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25

GroÃ«r, Chris, Bruce Golden, and Edward Wasil. "The balanced billing cycle vehicle routing problem." Networks 54, no. 4 (December 2009): 243–54. http://dx.doi.org/10.1002/net.20336.

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26

Battarra, Maria, Richard Eglese, and Güneş Erdoğan. "Guest editorial: Recent trends in vehicle routing." Networks 65, no. 2 (December 22, 2014): 101. http://dx.doi.org/10.1002/net.21593.

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27

Gianluca. "Ant Colony Optimization for Capacitated Vehicle Routing Problem." Journal of Computer Science 8, no. 6 (June 1, 2012): 846–52. http://dx.doi.org/10.3844/jcssp.2012.846.852.

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28

Yaman, Hande. "Formulations and Valid Inequalities for the Heterogeneous Vehicle Routing Problem." Mathematical Programming 106, no. 2 (July 14, 2005): 365–90. http://dx.doi.org/10.1007/s10107-005-0611-6.

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29

Franceschi, Roberto De, Matteo Fischetti, and Paolo Toth. "A new ILP-based refinement heuristic for Vehicle Routing Problems." Mathematical Programming 105, no. 2-3 (November 14, 2005): 471–99. http://dx.doi.org/10.1007/s10107-005-0662-8.

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30

Pessoa, Artur, Ruslan Sadykov, Eduardo Uchoa, and François Vanderbeck. "A generic exact solver for vehicle routing and related problems." Mathematical Programming 183, no. 1-2 (June 25, 2020): 483–523. http://dx.doi.org/10.1007/s10107-020-01523-z.

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31

MOURKOUSIS, GEORGE, MATHEW PROTONOTARIOS, and THEODORA VARVARIGOU. "APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM." International Journal of Computational Intelligence and Applications 03, no. 01 (March 2003): 1–21. http://dx.doi.org/10.1142/s1469026803000835.

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This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

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32

Shigaki, Ichiro, and Masami Konishi. "Decentralized Probabilistic Algorithm Using a Multi-Agent System for Vehicle Routing Problems." International Journal of Smart Engineering System Design 5, no. 4 (October 2003): 241–49. http://dx.doi.org/10.1080/10255810390245564.

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33

Karakatič, Sašo, and Vili Podgorelec. "A survey of genetic algorithms for solving multi depot vehicle routing problem." Applied Soft Computing 27 (February 2015): 519–32. http://dx.doi.org/10.1016/j.asoc.2014.11.005.

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34

Islam, Md Anisul, Yuvraj Gajpal, and Tarek Y. ElMekkawy. "Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem." Applied Soft Computing 110 (October 2021): 107655. http://dx.doi.org/10.1016/j.asoc.2021.107655.

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35

Kadhim, Ahmed Jawad, Seyed Amin Hosseini Seno, and Rana Ali Shihab. "Routing Strategy for Internet of Vehicles based on Hierarchical SDN and Fog Computing." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 26, no. 10 (December 24, 2018): 309–19. http://dx.doi.org/10.29196/jubpas.v26i10.1896.

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The fog computing is invited to solve the lack of resources problem in the sensors of Internet of Things (IoT) and handle the tasks quickly. Internet of Vehicles (IoV) is a special application of IoT networks that composed of heterogeneous sensors that are found in vehicles. These sensors transfer the tasks to the fog servers that process them and give the responses to the sensors. However, the mobility of vehicles effects on the delivery operation of responses. When the source vehicle of a task exited from the domain of some fog server through the processing time of this task, the response will not be reached to that vehicle correctly. Therefore, it is need to compute the optimal path to that vehicle. This process causes exceeding the task deadline and decreasing the throughput. To overcome this issue, this paper produces a hierarchical architecture based on Software Defined Network (SDN) and fog computing for IoV networks. This architecture consists of IoV vehicles, fog computing framework, semi-central SDN controllers and central SDN controller layers. Moreover, a routing strategy is proposed called Delay-Efficient Routing strategy based on SDN and Fog computing for IoV (DRSFI). The SDN controllers perform DRSFI to compute the routes with minimum delay with taking into consideration the available bandwidth constraint and the location and speed of the vehicle. From the results of simulation of different scenarios with various mobility speeds and various number of tasks, we concluded that the proposed system is better than IoV-Fog-central SDN system and IoV-Fog system in terms of average delay from end to end, percentage of packet loss and percentage of successfully transmission.

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36

Zhao, Ning, Xue Li, Mei Yang, and Xin Ting Huang. "Vehicular Ad-Hoc Network and Routing Design." Applied Mechanics and Materials 641-642 (September 2014): 829–32. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.829.

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To analyze vehicular Ad-Hoc network routing performance, the GPSR routing performance is studied using network simulation software NS2 compared with traffic simulation software VanetMobiSim. 100 nodes communicated with each other was stimulated by adding routing protocol into NS2, building the environment of simulation, setting simulation parameters and writing TCL script. The data packet delivery path was examined by trace files and GPSR routing performance was concluded through Gawk. The average end-to-end transmission delay trends to increase and the average delivery rate trends to drop when the average speed of vehicle nodes increase.

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37

Wang, Bochen, Qiyuan Qian, Zheyi Tan, Peng Zhang, Aizhi Wu, and Yi Zhou. "Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis." Scientific Programming 2020 (August 28, 2020): 1–11. http://dx.doi.org/10.1155/2020/8839628.

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This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

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38

Mocková, Denisa, and Alena Rybičková. "APPLICATION OF GENETIC ALGORITHMS TO VEHICLE ROUTING PROBLEM." Neural Network World 24, no. 1 (2014): 57–78. http://dx.doi.org/10.14311/nnw.2014.24.003.

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39

Dror, Moshe, Gilbert Laporte, and Francois V. Louveaux. "Vehicle routing with stochastic demands and restricted failures." ZOR Zeitschrift f�r Operations Research Methods and Models of Operations Research 37, no. 3 (October 1993): 273–83. http://dx.doi.org/10.1007/bf01415995.

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40

Rybickova, Alena, Jakub Brodsky, Adela Karaskova, and Denisa Mockova. "A Genetic Algorithm for the Multi-Depot Vehicle Routing Problem." Applied Mechanics and Materials 803 (October 2015): 69–75. http://dx.doi.org/10.4028/www.scientific.net/amm.803.69.

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In this paper, we focus on the optimization of the system of the spare parts distribution for authorized garages in the Czech Republic. A spare parts market belongs to one of the key elements of the car industry. However, it has to adapt to still higher requirements on accuracy, speed and minimum error rate of the deliveries with keeping the costs at its minimum at the same time. The distribution of products from depots to customers is a practical and challenging problem in logistics that opens a significant space for application of software products.The design of optimal routes of vehicles from two depots can be formulated in combinatorial optimization as a multi-depot vehicle routing problem.The goal of a multi-depot vehicle routing problem is to design routes that start and end in one of the depots and visit a subset of customers in a specific sequence. Every customer has to be visited on one of the routes and the total costs for the delivery should be minimal.Vehicle routing problems belong to the class of NP-hard problems which means that there is no efficient algorithm for finding optimal solution available. To find a solution in an efficient way, we propose an approximate method based on a genetic algorithm.

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41

Sari, Linna Oktaviana, Agusurio Azmi, Ery Safrianti, and Feranita Jalil. "Performance Analysis of DSR and TORA Model Routing Protocols In Vehicular Ad Hoc Network." International Journal of Electrical, Energy and Power System Engineering 3, no. 3 (October 13, 2020): 94–99. http://dx.doi.org/10.31258/ijeepse.3.3.94-99.

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Pekanbaru city is a large area, therefore traffic congestion often occurs due to the density of society’s vehicles. From this problem, it is needed a technology that can exchange information between vehicles. Information Technology that can involve many vehicles with special network types without dependence on an infrastructure is Ad Hoc Network. One type of this network is Vehicular Ad Hoc Network (VANET). VANET is a new concept in enabling communication between Vehicle to Vehicle (V2V). For efficient data packet delivery, VANET requires a routing protocol. In this research, for simulated and analyzed performance is used the Dynamic Source Routing (DSR) and Temporally Ordered Routing Algorithm (TORA) protocol. NS-2 is used to simulated a moved nodes, SUMO software is used to simulated real map of SKA Mall crossroad and parameter the quality of performance routing protocol DSR can determined by End to End Delay, Packet Delivery Ratio (PDR) and Routing Overhead (RO). This simulation uses scenario 100 nodes, 150 nodes, 200 nodes and 250 nodes. The simulation results with the scenario of changing the number of nodes, the DSR routing protocol produces better performance with an average of End to End Delay is 0.1066 s, average of PDR is 95.45% and average of RO is 1.0076. While the TORA routing protocol has an average of End to End Delay is 0.1163s, average of PDR is 93.49% and average of RO is 1.0801. And in the scenario of node speed changes, the TORA routing protocol produces better performance with an average of End to End Delay is 0.0861 s and average of PDR 97.37%. While the DSR routing protocol is better with an average of RO is 1.0076.

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42

You, Cheng, Wang, Chen, and Chen. "Cross-Layer and SDN Based Routing Scheme for P2P Communication in Vehicular Ad-Hoc Networks." Applied Sciences 9, no. 22 (November 6, 2019): 4734. http://dx.doi.org/10.3390/app9224734.

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Conventional routing protocols proposed for Vehicular Ad-hoc Network (VANET) are usually inefficient and vulnerable for multi-hop data forwarding due to the unavailability of global information and inefﬁciencies in their route discovering schemes. However, with the recently emerged software deﬁned vehicular network (SDVN) technologies, link stability can be better improved through the availability of global network information. Thus, in this paper, we present a novel software-defined network (SDN) based routing scheme for P2P connection under urban inter-vehicle networks that can ﬁnd a global optimal route between source and destination. This is a cross-layer routing protocol in VANETs, which utilizes metrics not only considering the position and velocity of vehicles, but also channel allocation and link duration when selecting the relay vehicles. Consequently, it starts a route discovery process which can improve the network performance in terms of end-to-end delay and low overhead. Furthermore, packet loss is largely minimized by the relatively stable paths. With the help of realistic simulation, we show that the proposed routing framework performs better than other three latest SDVN and conventional VANET protocols in routing overhead, average end-to-end delay, packet drop ratio, and average throughput. Therefore, our routing scheme is more suitable for 5G-enabled vehicular ad-hoc networks in future.

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43

Erera, Alan L., Martin Savelsbergh, and Emrah Uyar. "Fixed routes with backup vehicles for stochastic vehicle routing problems with time constraints." Networks 54, no. 4 (December 2009): 270–83. http://dx.doi.org/10.1002/net.20338.

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44

Vigo, Daniele, Paolo Toth, and Aristide Mingozzi. "Route 2005: Recent advances in vehicle routing optimization." Networks 49, no. 4 (2007): 243–44. http://dx.doi.org/10.1002/net.20175.

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45

Erera, Alan L., and Martin W. P. Savelsbergh. "ROUTE 2007: Recent advances in vehicle routing optimization." Networks 54, no. 4 (August 11, 2009): 165–66. http://dx.doi.org/10.1002/net.20329.

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46

Nagarajan, Viswanath, and R. Ravi. "Approximation algorithms for distance constrained vehicle routing problems." Networks 59, no. 2 (March 10, 2011): 209–14. http://dx.doi.org/10.1002/net.20435.

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47

Madsen, Oli B. G., and Stefan Ropke. "ROUTE 2009: Recent advances in vehicle routing optimization." Networks 58, no. 4 (October 20, 2011): 239–40. http://dx.doi.org/10.1002/net.20468.

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48

Psaraftis, Harilaos N., Min Wen, and Christos A. Kontovas. "Dynamic vehicle routing problems: Three decades and counting." Networks 67, no. 1 (August 17, 2015): 3–31. http://dx.doi.org/10.1002/net.21628.

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49

Rincon-Garcia, Nicolas, Ben J. Waterson, and Tom J. Cherrett. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry." Transport Reviews 38, no. 1 (March 5, 2017): 117–38. http://dx.doi.org/10.1080/01441647.2017.1297869.

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50

Wang, Chu Fu, and Yang Chih Chiu. "Routing problems for vehicle ad-hoc networks using the virtual message ferry routing scheme." International Journal of Ad Hoc and Ubiquitous Computing 28, no. 4 (2018): 258. http://dx.doi.org/10.1504/ijahuc.2018.093333.

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