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Journal articles on the topic 'Vehicle routing and scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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1

Laporte, Gilbert. "Scheduling issues in vehicle routing." Annals of Operations Research 236, no. 2 (July 13, 2013): 463–74. http://dx.doi.org/10.1007/s10479-013-1423-3.

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2

Lam, Edward, Pascal Van Hentenryck, and Phil Kilby. "Joint Vehicle and Crew Routing and Scheduling." Transportation Science 54, no. 2 (March 2020): 488–511. http://dx.doi.org/10.1287/trsc.2019.0907.

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Traditional vehicle routing problems implicitly assume that only one crew operates a vehicle for the entirety of its journey. However, this assumption is violated in many applications arising in humanitarian and military logistics. This paper considers a joint vehicle and crew routing and scheduling problem in which crews are able to interchange vehicles, resulting in space and time interdependencies between vehicle routes and crew routes. The problem is formulated as a mixed integer programming (MIP) model and a constraint programming (CP) model that overlay crew routing constraints over a standard vehicle routing problem. The constraint program uses a novel optimization constraint to detect infeasibility and to bound crew objectives. This paper also explores methods using large neighborhood search over the MIP and CP models. Experimental results indicate that modeling the vehicle and crew routing problems jointly and supporting vehicle interchanges for crews may bring significant benefits in cost reduction compared with a method that sequentializes these decisions.
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3

Arunapuram, Sundararajan, Kamlesh Mathur, and Daniel Solow. "Vehicle Routing and Scheduling with Full Truckloads." Transportation Science 37, no. 2 (May 2003): 170–82. http://dx.doi.org/10.1287/trsc.37.2.170.15248.

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4

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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5

Bott, Kevin, and Ronald H. Ballou. "Research perspectives in vehicle routing and scheduling." Transportation Research Part A: General 20, no. 3 (May 1986): 239–43. http://dx.doi.org/10.1016/0191-2607(86)90097-x.

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6

LU, Mingzhe, Hiroshi KISE, Yoshiyuki KARUNO, and Toshio OHKAWA. "Routing and Scheduling for Permutation Circulation-Type Vehicle Routing System." Transactions of the Japan Society of Mechanical Engineers Series C 68, no. 673 (2002): 2833–39. http://dx.doi.org/10.1299/kikaic.68.2833.

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7

Zou, Hao, and Tiantian Zhang. "Research on Vehicle Routing Algorithm for Supply Chain Logistics Distribution." MATEC Web of Conferences 227 (2018): 02003. http://dx.doi.org/10.1051/matecconf/201822702003.

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In view of logistics distribution vehicle scheduling problem, this paper proposes a vehicle routing optimization model considering route and time. Secondly, the vehicle routing optimization model is designed in detail, and the mathematical derivation steps are given, and the decision-making process of this problem is simulated with data. Finally, the paper takes the route of logistics distribution vehicles as the goal, and makes a preliminary verification. The results show that the algorithm is conducive to improving the management efficiency of the whole supply chain.
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8

Tung, Dang Vu, and Anulark Pinnoi. "Vehicle routing–scheduling for waste collection in Hanoi." European Journal of Operational Research 125, no. 3 (September 2000): 449–68. http://dx.doi.org/10.1016/s0377-2217(99)00408-7.

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9

Lysgaard, Jens. "Dynamic Transportation Networks in Vehicle Routing and Scheduling." Interfaces 22, no. 3 (June 1992): 45–55. http://dx.doi.org/10.1287/inte.22.3.45.

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10

Goel, Asvin. "Vehicle Scheduling and Routing with Drivers' Working Hours." Transportation Science 43, no. 1 (February 2009): 17–26. http://dx.doi.org/10.1287/trsc.1070.0226.

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11

Bodin, Lawrence, and Laurence Levy. "Commentary—Visualization in Vehicle Routing and Scheduling Problems." ORSA Journal on Computing 6, no. 3 (August 1994): 261–69. http://dx.doi.org/10.1287/ijoc.6.3.261.

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12

Panggabean, Ellis Mardiana, Herman Mawengkang, Zainal Azis, and Rina Filia Sari. "Periodic Heterogeneous Vehicle Routing Problem With Driver Scheduling." IOP Conference Series: Materials Science and Engineering 300 (January 2018): 012017. http://dx.doi.org/10.1088/1757-899x/300/1/012017.

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13

Potvin, Jean-Yves, Ying Xu, and Ilham Benyahia. "Vehicle routing and scheduling with dynamic travel times." Computers & Operations Research 33, no. 4 (April 2006): 1129–37. http://dx.doi.org/10.1016/j.cor.2004.09.015.

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14

Wang, Lixing, Zhenning Wu, and Changyong Cao. "Integrated Optimization of Routing and Energy Management for Electric Vehicles in Delivery Scheduling." Energies 14, no. 6 (March 22, 2021): 1762. http://dx.doi.org/10.3390/en14061762.

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At present, electric vehicles (EVs) are attracting increasing attention and have great potential for replacing fossil-fueled vehicles, especially for logistics applications. However, energy management for EVs is essential for them to be advantageous owing to their limitations with regard to battery capacity and recharging times. Therefore, inefficiencies can be expected for EV-based logistical operations without an energy management plan, which is not necessarily considered in traditional routing exercises. In this study, for the logistics application of EVs to manage energy and schedule the vehicle route, a system is proposed. The system comprises two parts: (1) a case-based reasoning subsystem to forecast the energy consumption and travel time for each route section, and (2) a genetic algorithm to optimize vehicle routing with an energy consumption situation as a new constraint. A dynamic adjustment algorithm is also adopted to achieve a rapid response to accidents in which the vehicles might be involved. Finally, a simulation is performed to test the system by adjusting the data from the vehicle routing problem with time windows. Solomon benchmarks are used for the validations. The analysis results show that the proposed vehicle management system is more economical than the traditional method.
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15

Wu, Lingfang, and Xiaolin Xiang. "Emergency Resource Scheduling During COVID-19." E3S Web of Conferences 253 (2021): 01012. http://dx.doi.org/10.1051/e3sconf/202125301012.

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Due to the suddenness and uncertainty of the COVID-19, sufficient emergency resources should be allocated to the appropriate emergency space within a limited time. In order to ensure the smooth implementation of emergency resource scheduling, emergency resource must be timely and effectively supplied. Therefore, it is of great significance to study how to reasonably distribute emergency resource and how to optimize the vehicle routing problem to improve the efficiency of emergency. Based on the research of capacitated vehicle routing problem (CVRP) solved by traditional genetic algorithm, this paper proposes simulated annealing-genetic algorithm, which has been verified to effectively solve the capacitated vehicle routing problem.
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16

Chen, Gang, Xiaoyuan Wu, Jinghua Li, and Hui Guo. "Green Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center Based on Improved Intelligent Water Drop Algorithms." Mathematical Problems in Engineering 2020 (January 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/9839634.

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The timeliness of the steel distribution center process contributes to the smooth progress of ship construction. However, carbon emissions from vehicles in the distribution process are a major source of pollution. Reasonable vehicle routing and scheduling can effectively reduce the carbon emissions of vehicles and ensure the timeliness of distribution. To solve this problem, a green vehicle routing and scheduling problem model with soft time windows was proposed in this study. An intelligent water drop algorithm was designed and improved and then compared with the genetic algorithm and the traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm was demonstrated. Finally, this algorithm was applied to a specific example to demonstrate that the improved intelligent water drop algorithm effectively reduced the cost of such green vehicle problems, thus reducing the carbon emissions of vehicles during the distribution process and achieving reductions in environmental pollution. Ultimately, this algorithm facilitates the achievement of green shipbuilding.
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17

Wang, Jian, Hong Yun Li, and Hong Chen. "Mixed Ant Colony Algorithm for Vehicle Routing Problem with Time Windows." Advanced Materials Research 706-708 (June 2013): 855–58. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.855.

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To optimize the vehicle routing problem with time windows(VRPTW), a mixed ant colony algorithm (MACO) was proposed to accomplish the vehicles’ scheduling. The pheromone adaptive volatile strategy takes real-time traffic status into consideration. Algorithm was accomplished on computer with the c# language.10 examples were calculated. Results show, MACO has a quick convergence rate, the result is stable.
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18

Ghoseiri, K., and S. F. Ghannad. "Hybrid Genetic Algorithm for Vehicle Routing and Scheduling Problem." Journal of Applied Sciences 9, no. 1 (December 15, 2008): 79–87. http://dx.doi.org/10.3923/jas.2009.79.87.

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19

Slater, Alan. "Specification for a dynamic vehicle routing and scheduling system." International Journal of Transport Management 1, no. 1 (February 2002): 29–40. http://dx.doi.org/10.1016/s1471-4051(01)00004-0.

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20

TANIGUCHI, Eiichi, and Naoki ANDO. "Probabilistic Vehicle Routing and Scheduling based on Traffic Information." INFRASTRUCTURE PLANNING REVIEW 21 (2004): 687–96. http://dx.doi.org/10.2208/journalip.21.687.

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21

Taniguchi, Eiichi, Tadashi Yamada, and Yasushi Kakimoto. "Probabilistic Vehicle Routing and Scheduling with Variable Travel Times." IFAC Proceedings Volumes 33, no. 9 (June 2000): 33–38. http://dx.doi.org/10.1016/s1474-6670(17)38119-3.

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22

Koç, Çağrı, Ola Jabali, and Gilbert Laporte. "Long-haul vehicle routing and scheduling with idling options." Journal of the Operational Research Society 69, no. 2 (December 19, 2017): 235–46. http://dx.doi.org/10.1057/s41274-017-0202-y.

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23

Campbell, Ann Melissa, and Martin Savelsbergh. "Efficient Insertion Heuristics for Vehicle Routing and Scheduling Problems." Transportation Science 38, no. 3 (August 2004): 369–78. http://dx.doi.org/10.1287/trsc.1030.0046.

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24

Hu, Menglan, Weidong Liu, Kai Peng, Xiaoqiang Ma, Wenqing Cheng, Jiangchuan Liu, and Bo Li. "Joint Routing and Scheduling for Vehicle-Assisted Multidrone Surveillance." IEEE Internet of Things Journal 6, no. 2 (April 2019): 1781–90. http://dx.doi.org/10.1109/jiot.2018.2878602.

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25

Eglese, Richard, Will Maden, and Alan Slater. "A Road Timetable to aid vehicle routing and scheduling." Computers & Operations Research 33, no. 12 (December 2006): 3508–19. http://dx.doi.org/10.1016/j.cor.2005.03.029.

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26

Lorini, Sandro, Jean-Yves Potvin, and Nicolas Zufferey. "Online vehicle routing and scheduling with dynamic travel times." Computers & Operations Research 38, no. 7 (July 2011): 1086–90. http://dx.doi.org/10.1016/j.cor.2010.10.019.

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27

Desrochers, M., J. K. Lenstra, and M. W. P. Savelsbergh. "A classification scheme for vehicle routing and scheduling problems." European Journal of Operational Research 46, no. 3 (June 1990): 322–32. http://dx.doi.org/10.1016/0377-2217(90)90007-x.

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28

Sousa Matos, Marcos Raylan, Yuri Frota, and Luiz Satoru Ochi. "Green Vehicle Routing and Scheduling Problem with Split Delivery." Electronic Notes in Discrete Mathematics 69 (August 2018): 13–20. http://dx.doi.org/10.1016/j.endm.2018.07.003.

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29

Vahdani, Behnam, Reza Tavakkoli-Moghaddam, Mostafa Zandieh, and Jafar Razmi. "Vehicle routing scheduling using an enhanced hybrid optimization approach." Journal of Intelligent Manufacturing 23, no. 3 (July 11, 2010): 759–74. http://dx.doi.org/10.1007/s10845-010-0427-y.

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30

Ullrich, Christian A. "Integrated machine scheduling and vehicle routing with time windows." European Journal of Operational Research 227, no. 1 (May 2013): 152–65. http://dx.doi.org/10.1016/j.ejor.2012.11.049.

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31

Lian, Jie. "An optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1901–15. http://dx.doi.org/10.3233/jifs-210611.

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In order to improve the distribution efficiency of cold chain logistics and reduce the distribution cost, an optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window is constructed. According to the complexity of cold chain logistics network, a multi-objective optimization model of cross-docking scheduling of cold chain logistics vehicle routing with fuzzy time window is established. In order to ensure the lowest total cost of cold chain logistics distribution and improve the overall customer satisfaction with service time, the Drosophila optimization algorithm is used to solve the model to obtain the optimal vehicle routing of cross-docking scheduling optimization of cold chain logistics. The simulation test results show that: after the application of the model, the cold chain logistics distribution time is significantly shortened, the distribution cost is significantly reduced, the damage cost is reduced, the carbon emission of vehicles is reduced, and the economic and low-carbon benefits are significantly improved, which can be used as an effective tool to solve the cross-docking scheduling optimization problem of cold chain logistics.
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32

Paraphantakul, Chutipong, Elise Miller-Hooks, and Sathaporn Opasanon. "Scheduling Deliveries with Backhauls in Thailand's Cement Industry." Transportation Research Record: Journal of the Transportation Research Board 2269, no. 1 (January 2012): 73–82. http://dx.doi.org/10.3141/2269-09.

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The problem of truckload delivery with backhaul scheduling was formulated, and a methodology for ant colony optimization that was developed for a related problem, the problem of vehicle routing with backhaul and time windows, was adapted for the solution of the truckload delivery problem. The problem of truckload delivery with backhaul scheduling differs from the problem of vehicle routing with backhaul and time windows in that shipments are in units of truckloads, multiple time windows in multiple days are available for delivery to customers, limited space for servicing customers is available, and multiple visits to each customer may be required. The problem is motivated by a real-world application arising at a leading cement producer in Thailand. Experts at the cement production plant assign vehicles to cement customers and lignite mines on the basis of manual computations and experience. Mathematical and computational frameworks are provided for modeling and solving this real-world application.
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33

Chai, Huo, Ruichun He, Changxi Ma, Cunjie Dai, and Kun Zhou. "Path Planning and Vehicle Scheduling Optimization for Logistic Distribution of Hazardous Materials in Full Container Load." Discrete Dynamics in Nature and Society 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9685125.

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Mathematical models for path planning and vehicle scheduling for logistic distribution of hazardous materials in full container load (FCL) are established, with their problem-solving methods proposed. First, a two-stage multiobjective optimization algorithm is designed for path planning. In the first stage, pulse algorithm is used to obtain the Pareto paths from the distribution center to each destination. In the second stage, a multiobjective optimization method based on Nondominated Sorting Genetic Algorithm II (NSGA-II) is designed to obtain candidate transport paths. Second, with analysis on the operating process of vehicles with hazardous materials in FCL, the vehicle scheduling problem is converted to Vehicle Routing Problem with Time Windows (VRPTW). A problem-solving method based on estimation of distribution is adopted. A transport timetable for all vehicles based on their transport paths is calculated, with participation of the decision-makers. A visual vehicle scheduling plan is presented for the decision-makers. Last, two examples are used to test the method proposed in this study: distribution of hazardous materials in a small-scale test network and distribution of oil products for sixteen gas stations in the main districts of Lanzhou city. In both examples, our method is used to obtain the path selection and vehicle scheduling plan, proving that validity of our method is verified.
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Li, Jinghua, Hui Guo, Qinghua Zhou, and Boxin Yang. "Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center under Green Shipbuilding Mode." Sustainability 11, no. 15 (August 6, 2019): 4248. http://dx.doi.org/10.3390/su11154248.

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Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding.
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35

Barco, J., A. Guerra, L. Muñoz, and N. Quijano. "Optimal Routing and Scheduling of Charge for Electric Vehicles: A Case Study." Mathematical Problems in Engineering 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/8509783.

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There are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). This approach leads to a new challenge as the BEVs’ routing is exposed to the traditional routing problems of conventional vehicles, as well as the particular requirements of the electrical technologies of BEVs. Examples of BEVs’ routing problems include the autonomy, battery degradation, and charge process. This work presents a differential evolution algorithm for solving an electric vehicle routing problem (EVRP). The formulation of the EVRP to be solved is based on a scheme to coordinate the BEVs’ routing and recharge scheduling, considering operation and battery degradation costs. A model based on the longitudinal dynamics equation of motion estimates the energy consumption of each BEV. A case study, consisting of an airport shuttle service scenario, is used to illustrate the proposed methodology. For this transport service, the BEV energy consumption is estimated based on experimentally measured driving patterns.
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36

Yeh, Wei-Chang, and Shi-Yi Tan. "Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function." Electronics 10, no. 15 (July 24, 2021): 1775. http://dx.doi.org/10.3390/electronics10151775.

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Transportation planning has been established as a key topic in the literature and practices of social production, especially in urban contexts. To consider traffic environment factors, more and more researchers are taking time-varying factors into account when scheduling their logistic activities. The time-dependent vehicle routing problem (TDVRP) is an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) by determining a set of optimal routes serving a set of customers within specific time windows. However, few of them use the continuous speed function to express the time-varying. In practice, many vehicle routing problems are addressed by a fleet of heterogeneous vehicles with different capacities and travel costs including fix costs and variable costs. In this study, a Heterogeneous Fleet Vehicle Routing Problem (HFPRP) Time-Varying Continuous Speed Function has been proposed. The objective is to minimize distribution costs, which contained fixed costs of acquiring and variable fuel costs. To address this problem, our research developed a mathematical model and proposed a Simplified Swarm Optimization (SSO) heuristic for HFVRP with Time-Varying Continuous Speed Function.
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37

Wolfenburg, Andrzej. "NEW VERSION OF THE BBS METHOD AND ITS USAGE FOR DETERMINING AND SCHEDULING VEHICLE ROUTES." Archives of Transport 31, no. 3 (September 30, 2014): 83–91. http://dx.doi.org/10.5604/08669546.1146991.

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The work presents a method of the vehicle routing and scheduling using the modified Branch and Bound Simulation method for vehicles simultaneously carrying loads from multiple orders. Limitations concerning vehicle loads and time windows in pickup and delivery points are taken into consideration. The developed algorithm appoints the shortest, the fastest and the cheapest routes with a certain known accuracy. A modification of the BBS method is described A sample of result using Google API is shown.
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38

Qureshi, Ali Gul, Eiichi Taniguchi, and Tadashi Yamada. "EFFECTS OF RELAXING TIME WINDOWS ON VEHICLE ROUTING AND SCHEDULING." INFRASTRUCTURE PLANNING REVIEW 24 (2007): 927–36. http://dx.doi.org/10.2208/journalip.24.927.

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39

Zäpfel, Günther, and Michael Bögl. "Multi-period vehicle routing and crew scheduling with outsourcing options." International Journal of Production Economics 113, no. 2 (June 2008): 980–96. http://dx.doi.org/10.1016/j.ijpe.2007.11.011.

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40

Rancourt, Marie-Eve, Jean-François Cordeau, and Gilbert Laporte. "Long-Haul Vehicle Routing and Scheduling with Working Hour Rules." Transportation Science 47, no. 1 (February 2013): 81–107. http://dx.doi.org/10.1287/trsc.1120.0417.

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41

Yan, Shangyao, Sin-Siang Wang, and Yu-Hsuan Chang. "Cash transportation vehicle routing and scheduling under stochastic travel times." Engineering Optimization 46, no. 3 (May 7, 2013): 289–307. http://dx.doi.org/10.1080/0305215x.2013.768240.

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42

Lee, Young Hae, Jung Woo Jung, and Kyong Min Lee. "Vehicle routing scheduling for cross-docking in the supply chain." Computers & Industrial Engineering 51, no. 2 (October 2006): 247–56. http://dx.doi.org/10.1016/j.cie.2006.02.006.

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43

Faiz, Tasnim Ibn, Chrysafis Vogiatzis, and Md Noor-E-Alam. "A column generation algorithm for vehicle scheduling and routing problems." Computers & Industrial Engineering 130 (April 2019): 222–36. http://dx.doi.org/10.1016/j.cie.2019.02.032.

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44

Shahin Moghadam, S., S. M. T. Fatemi Ghomi, and B. Karimi. "Vehicle routing scheduling problem with cross docking and split deliveries." Computers & Chemical Engineering 69 (October 2014): 98–107. http://dx.doi.org/10.1016/j.compchemeng.2014.06.015.

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45

Díaz-Parra, Ocotlán, Jorge A. Ruiz-Vanoye, Beatriz Bernábe Loranca, Alejandro Fuentes-Penna, and Ricardo A. Barrera-Cámara. "A Survey of Transportation Problems." Journal of Applied Mathematics 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/848129.

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This paper aims at being a guide to understand the different types of transportation problems by presenting a survey of mathematical models and algorithms used to solve different types of transportation modes (ship, plane, train, bus, truck, Motorcycle, Cars, and others) by air, water, space, cables, tubes, and road. Some problems are as follows: bus scheduling problem, delivery problem, combining truck trip problem, open vehicle routing problem, helicopter routing problem, truck loading problem, truck dispatching problem, truck routing problem, truck transportation problem, vehicle routing problem and variants, convoy routing problem, railroad blocking problem (RBP), inventory routing problem (IRP), air traffic flow management problem (TFMP), cash transportation vehicle routing problem, and so forth.
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46

Zhang, Zhi Gang, and Yan Cheng Gong. "Improved Genetic Algorithm of Vehicle Routing Problems with Time Window for Military Logistic Distribution." Applied Mechanics and Materials 135-136 (October 2011): 585–91. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.585.

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By changing the constrain conditions of delivery time windows and vehicle capacities to objective function, A vehicle scheduling model was built up based on minimum length of total transportation distance, which included penalty function terms of time window and vehicle capacity constrains, and the model characteristics and application prospects was analyzed. A improved Genetic Algorithm program was put forward to solve the model, in which a chromosome coding suitable to describe delivery routes was designed, a suitable-degree function was proposed, and a reproduction operator, a crossover operator and a mutation operator were constructed. An example was given to demonstrate feasibility of the algorithm. The study indicates that the Algorithm has higher algorithm efficiency and can effectively solve vehicle scheduling problems of military distribution centers.
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47

Rahman, M. Azizur, Al-Amin Hossain, Binoy Debnath, Zinnat Mahmud Zefat, Mohammad Sarwar Morshed, and Ziaul Haq Adnan. "Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh." Logistics 5, no. 3 (September 14, 2021): 63. http://dx.doi.org/10.3390/logistics5030063.

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Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved.
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48

Zhao, Chong, Jianghong Han, Xu Ding, Lei Shi, and Fan Yang. "An Analytical Model for Interference Alignment in Broadcast Assisted VANETs." Sensors 19, no. 22 (November 15, 2019): 4988. http://dx.doi.org/10.3390/s19224988.

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Application of safety-related information interaction among vehicles has always been a research frontier in Vehicular Ad-hoc NETworks (VANETs). These messages require high real-time performance. There is a lot of research dependant on creating optimization model for communication task scheduling or routing protocols to reduce communication delay. In this paper, we analyze characteristics of safety-related information and introduce Interference Alignment (IA) technology in VANETs. To further improve routing efficiency, a data-driven assisted transmission routing and broadcast model framework for Vehicle to Vehicle(V2V) and Vehicle to Infrastructure (V2I) communication are constructed which are the basis for IA. Depending on the proposed model, we propose an optimization problem of minimizing total number of time slots required for safety information sharing in VANETs. Then a clustering algorithm is designed to narrow feasible solution space. Simulation results show that the approach can effectively reduce the number of time slots required and improve link use by 20% percent compared with no IA applied.
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49

Zheng, Yue, Liangpeng Gao, and Wenquan Li. "Vehicle Routing and Scheduling of Flex-Route Transit under a Dynamic Operating Environment." Discrete Dynamics in Nature and Society 2021 (January 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/6669567.

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Abstract:
To improve the reliability, responsiveness, and productivity of the flex-route transit service, this paper investigates the vehicle scheduling and routing problem under a dynamic operating environment. First, we discuss the new operating polices after the introduction of intelligent transportation systems (ITSs), including automatic vehicle location (AVL) system, mobile data terminal (MDT), and computer-aided dispatch (CAD) system. Second, a mixed integer programming (MIP) formulation is employed to solve the offline routing problem. Third, an online scheduling scheme is presented to tackle different dynamic events, such as dynamic requests, travel time fluctuations, cancellations of requests, and customer no-shows. Finally, simulation experiments based on a real-life flex-route transit service are conducted to assess the influence of different dynamic events. The results demonstrate that the proposed scheduling scheme is reliable for coping with various dynamic events, and our findings can be used to guide the policy making of flex-route transit services.
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50

Modares, A., and M. Sepehri. "Development of an integrated system for distribution planning in supply chain." South African Journal of Business Management 40, no. 4 (December 31, 2009): 13–23. http://dx.doi.org/10.4102/sajbm.v40i4.547.

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Distribution planning, which includes Vehicle Routing and Scheduling Problem (VRSP), has become an important element in Supply Chain impacting its service level and efficiency. Computer Aided Routing and Scheduling (CARS) has been developed and implemented, which can handle complicated distribution models using advanced heuristic optimization algorithms. A classification scheme is introduced to classify various types of routing and scheduling problems in a structured manner, based on the main objects of VRSP. The integrated system described in this paper can manage the dynamic aspects of the Supply Chain in practice. The modelling and solution approach in the CARS optimization engine, its user interface, sample performance measurements, and planning and operational features of the system are described in detail.
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