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Journal articles on the topic 'Stochastic Vehicle Routing Problem (SVRP)'

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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1

Berhan, Eshetie, Birhanu Beshah, Daniel Kitaw, and Ajith Abraham. "Stochastic Vehicle Routing Problem: A Literature Survey." Journal of Information & Knowledge Management 13, no. 03 (2014): 1450022. http://dx.doi.org/10.1142/s0219649214500221.

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The purpose of this paper is to develop structural classification of Stochastic Vehicle Routing Problem (SVRP) by different domains and attributes. This research used a systematic review and meta-analysis on SVRP literatures. This includes browsing relevant researches and publications, screening related articles, identifying domains, attributes and categorising the articles based on the identified domains and attributes. The findings of the study show clear differences on the number of studies under each domain and attribute. Most studied attributes are stochastic customer demand, capacitated vehicle, synthesis data and objective function with cost minimization. Whereas the least studied are maximisation objective function, stochastic service time, and an applied model using stochastic with recurs. The research helps to summarise and map a comprehensive survey on SVRP literatures so that various contributions in the field are organised in a manner that provide a clear view for the readers and identify future research directions. This paper is the first of its kind in the field of SVRP that develop a classification scheme for articles published since 1993 to enhances the development of this newly emerging discipline.

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2

Hu, Ta-Yin, Tsai-Yun Liao, and Ying-Chih Lu. "Study of Solution Approach for Dynamic Vehicle Routing Problems with Real-Time Information." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (2003): 102–8. http://dx.doi.org/10.3141/1857-12.

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Recent advances in commercial vehicle operations (CVO), especially in communication and information technologies, allow the study of dynamic vehicle routing problems under new and updated information, such as traffic conditions and new customers. Two major operational benefits of CVO include ( a) dynamically assigning vehicles to time-sensitive demands, and ( b) efficiently rerouting vehicles according to current traffic conditions. In this research, stochastic vehicle routing problems (SVRP) are considered and extended to incorporate real-time information for dynamic vehicle routing problems. The SVRP model is formulated by a chance-constrained model and is solved by CPLEX with branch-and-bound techniques. Numerical experiments are conducted in a Taichung city network to investigate dynamic vehicle routing strategies under real-time information supply strategies and to assess the effectiveness of such strategies in a dynamic perspective.

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3

González La Rotta, Elsa Cristina, Oswaldo González Yazo, and Mauricio Becerra Fernández. "Estado del arte del problema de ruteo de vehículos con componentes estocásticos." INVENTUM 13, no. 24 (2018): 2. http://dx.doi.org/10.26620/uniminuto.inventum.13.24.2018.2-14.

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<p>Este artículo presenta una revisión a la literatura del problema de ruteo de vehículos con componentes aleatorios: svrp (Stochastics Vehicle Routing Problem). A pesar de la atención reciente hacia los problemas de ruteo y la variedad de estudios al respecto, con este trabajo se pretende enfatizar en una tipología especial, la cual presenta uno o múltiples parámetros de carácter probabilístico o estocástico. Después de una búsqueda rigurosa en las bases de datos Science Direct, ebsco y Google Scholar, utilizando una<br />ventana de tiempo de los últimos diez años y clasificando dichas investigaciones, se logra establecer un concepto particular para este tipo de problemas de ruteo, sus clasificaciones y métodos de solución, lo cual resulta de gran ayuda para quienes desean investigar el tema, pues facilita la indagación acerca de enfoques de modelamiento y métodos de solución. Como conclusión principal, se determina que, debido a la complejidad de su solución, son menos los resultados y aplicaciones que contemplen este tipo de formulaciones, con respecto a las que presentan parámetros<br />deterministas; ofreciendo un amplio campo de trabajo para trabajos posteriores.</p>

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4

Sabo, Cosmin, Petrică C. Pop, and Andrei Horvat-Marc. "On the Selective Vehicle Routing Problem." Mathematics 8, no. 5 (2020): 771. http://dx.doi.org/10.3390/math8050771.

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The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

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Korenar, Vaclav. "Vehicle Routing Problem with Stochastic Demands." Communications - Scientific letters of the University of Zilina 5, no. 4 (2003): 24–26. http://dx.doi.org/10.26552/com.c.2003.4.24-26.

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6

Akhmetbek, Yernar. "Stochastic dynamic vehicle routing problem survey." Suleyman Demirel University Bulletin Natural and Technical Sciences 62, no. 1 (2024): 75–87. https://doi.org/10.47344/sdubnts.v62i1.959.

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The present article aims to offer an exhaustive and in-depth investigation of the Stochastic Dynamic Vehicle Routing Problem, which remains a significant challenge in the field of transportation logistics. To achievethis objective, we will undertake a meticulous analysis of the latest cutting-edge techniques and methodologies deployed to tackle this complex optimization problem. Furthermore, we will delve into the intricate and multifaceted stochastic and dynamic constraints that pose formidable obstacles to effective route planning and optimization. Through this survey paper, we seek to provide a comprehensive understanding of the current state-of-the-art approaches and highlight the potential avenues for future research in this critical area of transportation logistics. In addition, we will also analyze the application of advanced reinforcement learning methods and Markov decision processes to solve the problem of stochastic dynamic vehicle routing.

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7

Yang, Wen-Huei, Kamlesh Mathur, and Ronald H. Ballou. "Stochastic Vehicle Routing Problem with Restocking." Transportation Science 34, no. 1 (2000): 99–112. http://dx.doi.org/10.1287/trsc.34.1.99.12278.

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8

Bastian, Cock, and Alexander H. G. Rinnooy Kan. "The stochastic vehicle routing problem revisited." European Journal of Operational Research 56, no. 3 (1992): 407–12. http://dx.doi.org/10.1016/0377-2217(92)90323-2.

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9

Bertsimas, Dimitris J. "A Vehicle Routing Problem with Stochastic Demand." Operations Research 40, no. 3 (1992): 574–85. http://dx.doi.org/10.1287/opre.40.3.574.

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10

Teodorovic, Dusan, Emina Krcmar-Nozic, and Goran Pavkovic. "The mixed fleet stochastic vehicle routing problem." Transportation Planning and Technology 19, no. 1 (1995): 31–43. http://dx.doi.org/10.1080/03081069508717556.

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11

Laporte, Gilbert, François Louveaux, and Hélène Mercure. "The Vehicle Routing Problem with Stochastic Travel Times." Transportation Science 26, no. 3 (1992): 161–70. http://dx.doi.org/10.1287/trsc.26.3.161.

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12

Bertazzi, Luca, and Nicola Secomandi. "Technical Note—Worst-Case Benefit of Restocking for the Vehicle Routing Problem with Stochastic Demands." Operations Research 68, no. 3 (2020): 671–75. http://dx.doi.org/10.1287/opre.2019.1901.

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In “Worst-Case Benefit of Restocking for the Vehicle Routing Problem with Stochastic Demands,” Bertazzi and Secomandi shed new light on an old yet timely problem in transportation. They analyze the benefit of planning replenishment trips in the vehicle routing problem with stochastic demands, taking as a benchmark their execution only upon stock-outs. Their worst-case investigation provides a theoretical explanation of a phenomenon that has been observed in prior numerical studies.

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13

Li, Zhenping, and Pengbo Jiao. "Two-stage stochastic programming for the inventory routing problem with stochastic demands in fuel delivery." International Journal of Industrial Engineering Computations 13, no. 4 (2022): 507–22. http://dx.doi.org/10.5267/j.ijiec.2022.7.004.

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The inventory routing problem (IRP) arises in the joint practices of vendor-managed inventory (VMI) and vehicle routing problem (VRP), aiming to simultaneously optimize the distribution, inventory and vehicle routes. This paper studies the multi-vehicle multi-compartment inventory routing problem with stochastic demands (MCIRPSD) in the context of fuel delivery. The problem with maximum-to-level (ML) replenishment policy is modeled as a two-stage stochastic programming model with the purpose of minimizing the total cost, in which the inventory management and routing decisions are made in the first stage while the corresponding resource actions are implemented in the second stage. An acceleration strategy is incorporated into the exact single-cut Benders decomposition algorithm and its multi-cut version respectively to solve the MCIRPSD on the small instances. Two-phase heuristic approaches based on the single-cut decomposition algorithm and its multi-cut version are developed to deal with the MCIRPSD on the medium and large-scale instances. Comparing the performance of the proposed algorithms with the Gurobi solver within limited time, the average objective value obtained by the proposed algorithm has decreased more than 7.30% for the medium and large instances, which demonstrates the effectiveness of our algorithms. The impacts of the instance features on the results are further analyzed, and some managerial insights are concluded for the manager.

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14

Qiang, Ning, and Feng Ju Kang. "A Hybrid Particle Swarm Optimization for Solving Vehicle Routing Problem with Stochastic Demands." Advanced Materials Research 971-973 (June 2014): 1467–72. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1467.

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As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

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15

Almutairi, Ahmed, and Mahmoud Owais. "Reliable Vehicle Routing Problem Using Traffic Sensors Augmented Information." Sensors 25, no. 7 (2025): 2262. https://doi.org/10.3390/s25072262.

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The stochastic routing transportation network problem presents significant challenges due to uncertainty in travel times, real-time variability, and limited sensor data availability. Traditional adaptive routing strategies, which rely on real-time travel time updates, may lead to suboptimal decisions due to dynamic traffic fluctuations. This study introduces a novel routing framework that integrates traffic sensor data augmentation and deep learning techniques to improve the reliability of route selection and network observability. The proposed methodology consists of four components: stochastic traffic assignment, multi-objective route generation, optimal traffic sensor location selection, and deep learning-based traffic flow estimation. The framework employs a traffic sensor location problem formulation to determine the minimum required sensor deployment while ensuring an accurate network-wide traffic estimation. A Stacked Sparse Auto-Encoder (SAE) deep learning model is then used to infer unobserved link flows, enhancing the observability of stochastic traffic conditions. By addressing the gap between limited sensor availability and complete network observability, this study offers a scalable and cost-effective solution for real-time traffic management and vehicle routing optimization. The results confirm that the proposed data-driven approach significantly reduces the need for sensor deployment while maintaining high accuracy in traffic flow predictions.

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16

Bernardo, Marcella, and Jürgen Pannek. "Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem." Journal of Advanced Transportation 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/9848104.

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The dynamic and stochastic vehicle routing problem (DSVRP) can be modelled as a stochastic program (SP). In a two-stage SP with recourse model, the first stage minimizes the a priori routing plan cost and the second stage minimizes the cost of corrective actions, performed to deal with changes in the inputs. To deal with the problem, approaches based either on stochastic modelling or on sampling can be applied. Sampling-based methods incorporate stochastic knowledge by generating scenarios set on realizations drawn from distributions. In this paper we proposed a robust solution approach for the capacitated DSVRP based on sampling strategies. We formulated the problem as a two-stage stochastic program model with recourse. In the first stage the a priori routing plan cost is minimized, whereas in the second stage the average of higher moments for the recourse cost calculated via a set of scenarios is minimized. The idea is to include higher moments in the second stage aiming to compute a robust a priori routing plan that minimizes transportation costs while permitting small changes in the demands without changing solution structure. Additionally, the approach allows managers to choose between optimality and robustness, that is, transportation costs and reconfiguration. The computational results on a generic dynamic benchmark dataset show that the robust routing plan can cover unmet demand while incurring little extra costs as compared to the preplanning. We observed that the plan of routes is more robust; that is, not only the expected real cost, but also the increment within the planned cost is lower.

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17

Wu, Yachao, Min Zhou, Dezhi Zhang, and Shuangyan Li. "A Biobjective Vehicle Routing Problem with Stochastic Demand and Split Deliveries." Scientific Programming 2022 (July 13, 2022): 1–16. http://dx.doi.org/10.1155/2022/2790258.

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This study addresses a biobjective vehicle routing problem with stochastic demand and split deliveries. Apart from minimizing the total travel cost that is widely considered in classical vehicle routing problems, we aim to balance the workload of all routes. A recourse policy for paired vehicles that allows the distribution service to fail once and meet part of the demand of customers first is provided. To solve the proposed biobjective vehicle routing problem, an adaptive large neighborhood search embedded with an improved optimization method is developed. The objective value is calculated based on the normalized values of two goals using the improved weighted sum approach. To evaluate the proposed optimization model and corresponding algorithm, some experiments modified from Solomon’s instances are conducted. Computational results show that the performance of the proposed heuristic approach is effective.

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18

Gannouni, Asmae, Rachid Ellaia, and El-Ghazali Talbi. "Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic." MATEC Web of Conferences 105 (2017): 00001. http://dx.doi.org/10.1051/matecconf/201710500001.

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19

Zare Mehrjerdi, Yahia. "A multiple objective stochastic approach to vehicle routing problem." International Journal of Advanced Manufacturing Technology 74, no. 5-8 (2014): 1149–58. http://dx.doi.org/10.1007/s00170-014-5895-3.

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20

Komatsu, Masahiro, Ryota Omori, Tetsuya Sato, and Takayuki Shiina. "Solution Algorithm for Vehicle Routing Problem with Stochastic Demand." International Journal of Service and Knowledge Management 7, no. 2 (2023): 1. http://dx.doi.org/10.52731/ijskm.v7.i2.710.

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21

Komatsu, Masahiro, Ryota Omori, Tetsuya Sato, and Takayuki Shiina. "Solution Algorithm for Vehicle Routing Problem with Stochastic Demand." International Journal of Service and Knowledge Management 7, no. 1 (2023): 1. http://dx.doi.org/10.52731/ijskm.v7.i1.710.

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22

Wang, Chuan Sheng, and Yue Qiu. "Vehicle Routing Problem with Stochastic Demands and Simultaneous Delivery and Pickup." Applied Mechanics and Materials 148-149 (December 2011): 810–13. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.810.

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In this paper vehicle routing problem with stochastic demands and simultaneous delivery and pickup is developed and analyzed, which is an important expansion of classical Vehicle Routing Problem(VRP).An effective algorithm based on Important Sampling is designed to solve the model. The optimal importance sampling distribution function was obtained by making use of the expection constructed by likelihood ratio. Numerical experiments have been conducted and the results indicate that the method can effectively solve this problem.

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23

Yu, Nan, Bin Dong, Yuben Qu, et al. "Drones Routing with Stochastic Demand." Drones 7, no. 6 (2023): 362. http://dx.doi.org/10.3390/drones7060362.

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Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme of resplitting customers into different individual delivery trips while the stochastic demands are determined. Moreover, we consider a variety of MORE, MORE-TW, and design an effective algorithm to address it. We conduct simulation experiments for MORE to verify our theoretical findings. The results show that our algorithm outperforms other comparison algorithms by at least 79.60%.

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24

Bhuranda, Lokesh Kumar, Mohd Rizwanullah, Arpit Kumar Sharma, Kamlesh Gautam, and Yash Chawla. "Stochastic optimization of multi-capacitated vehicle routing problem with pickup and delivery using saving matrix algorithm." Journal of Information and Optimization Sciences 44, no. 3 (2023): 541–52. http://dx.doi.org/10.47974/jios-1413.

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The Multi-Capacitated Problem is an optimization problem. To reduce the overall distance, the optimization problem seeks out vehicle routes that connect every customer to a storage facility. This article uses a saving matrix approach to propose an extended Vehicle Routing Problem that considers a stochastic environment and multiple capacitors. Stochastic customers are an essential element of the problem. A computational analysis also supports the suggested approach to obtain the best route.

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25

Zhang, Qun, and Rui Yan. "Stochastic Demand Vehicle Routing Problem on Immune and Genetic Algorithm." Advanced Materials Research 452-453 (January 2012): 823–28. http://dx.doi.org/10.4028/scientific5/amr.452-453.823.

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26

Zhang, Qun, and Rui Yan. "Stochastic Demand Vehicle Routing Problem on Immune and Genetic Algorithm." Advanced Materials Research 452-453 (January 2012): 823–28. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.823.

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In this paper, stochastic demand vehicle routing problem and model are studied. Hybrid algorithm based on immune algorithm and genetic algorithm is proposed in order to gain solution. IA-GA use basic principles and processes of genetic algorithm, and includes the promotion-inhibition function and memory function of immune algorithm. The premature convergence problem of genetic algorithm is conquered, and we can get different offspring with improved crossover. Comparing experiment shows that IA-GA has better computing performance.

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27

Lei, Hongtao, Gilbert Laporte, and Bo Guo. "The Vehicle Routing Problem with Stochastic Demands and Split Deliveries." INFOR: Information Systems and Operational Research 50, no. 2 (2012): 59–71. http://dx.doi.org/10.3138/infor.50.2.059.

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28

Shanmugam. "Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands." Journal of Computer Science 7, no. 4 (2011): 533–42. http://dx.doi.org/10.3844/jcssp.2011.533.542.

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29

Murakami, Keisuke, and Hiroshi Morita. "Hybrid model for the vehicle routing problem with stochastic demand." International Journal of Applied Management Science 2, no. 3 (2010): 224. http://dx.doi.org/10.1504/ijams.2010.033566.

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30

Zhang, Jingling, Wanliang Wang, and Yanwei Zhao. "Model and algorithms for dynamic and stochastic vehicle routing problem." International Journal of Modelling, Identification and Control 18, no. 4 (2013): 364. http://dx.doi.org/10.1504/ijmic.2013.053542.

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31

Chang, Mei‐Shiang. "A vehicle routing problem with time windows and stochastic demands." Journal of the Chinese Institute of Engineers 28, no. 5 (2005): 783–94. http://dx.doi.org/10.1080/02533839.2005.9671048.

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32

Secomandi, Nicola, and François Margot. "Reoptimization Approaches for the Vehicle-Routing Problem with Stochastic Demands." Operations Research 57, no. 1 (2009): 214–30. http://dx.doi.org/10.1287/opre.1080.0520.

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33

Erera, Alan L., Juan C. Morales, and Martin Savelsbergh. "The Vehicle Routing Problem with Stochastic Demand and Duration Constraints." Transportation Science 44, no. 4 (2010): 474–92. http://dx.doi.org/10.1287/trsc.1100.0324.

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34

Bianchi, Leonora, Mauro Birattari, Marco Chiarandini, et al. "Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands." Journal of Mathematical Modelling and Algorithms 5, no. 1 (2005): 91–110. http://dx.doi.org/10.1007/s10852-005-9033-y.

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35

Ma, Wanjing, Lin Zeng, and Kun An. "Dynamic vehicle routing problem for flexible buses considering stochastic requests." Transportation Research Part C: Emerging Technologies 148 (March 2023): 104030. http://dx.doi.org/10.1016/j.trc.2023.104030.

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36

Alvarez, Aldair, Jean-François Cordeau, and Raf Jans. "The consistent vehicle routing problem with stochastic customers and demands." Transportation Research Part B: Methodological 186 (August 2024): 102968. http://dx.doi.org/10.1016/j.trb.2024.102968.

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37

Heydari, Meysam, Hassan Torabi, and Meghdad Jahromi. "mathematical model of routing problem for hazardous biomedical waste: A multi-objective particle swarm optimization solution approach." Journal of Multidisciplinary Academic and Practice Studies 1, no. 4 (2023): 339–51. http://dx.doi.org/10.35912/jomaps.v1i4.1794.

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Purpose: This model aims at solving a Green Heterogeneous and Stochastic Capacitated Vehicle Routing Problem that takes into account the risks and environmental hazards. Research Methodology: Regarding an NP-hard and complex problem, and after confirming the accuracy of the problem-solving in smaller dimensions by GAMS software, the problem is solved by the metaheuristic algorithm of multi-objective particle swarm optimization (MOPSO) and its coding in MATLAB software. Results: The results urge that using random sampling and probability distribution, non-deterministic parameters turned into deterministic ones, and high-quality solutions were obtained. Limitation: The proposed method is a routing problem and has been applied for the Green Heterogeneous and Stochastic Capacitated Vehicle Routing Problem. Future researchers may work on real data sets and hazardous biomedical waste data. Contribution: Based on the results presented, the model derived in this paper can support decisions such as routing, prioritization, time to reach each node, etc. so that the costs of routing, system reliability, environmental issues, and penalties for violation of the priority and maximum time elapsed for vehicles are taken into account.

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38

Xue, Lian, and Xiao Xia Dai. "Research on the Vehicle Routing Problem with Fuzzy Demands." Advanced Materials Research 186 (January 2011): 570–75. http://dx.doi.org/10.4028/www.scientific.net/amr.186.570.

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In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.

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39

Ak, Aykagan, and Alan L. Erera. "A Paired-Vehicle Recourse Strategy for the Vehicle-Routing Problem with Stochastic Demands." Transportation Science 41, no. 2 (2007): 222–37. http://dx.doi.org/10.1287/trsc.1060.0180.

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40

Zou, Han, and Maged M. Dessouky. "A look-ahead partial routing framework for the stochastic and dynamic vehicle routing problem." Journal on Vehicle Routing Algorithms 1, no. 2-4 (2018): 73–88. http://dx.doi.org/10.1007/s41604-018-0006-5.

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41

Li, Xinran, Haoxuan Kan, Xuedong Hua, and Wei Wang. "Simulation-Based Electric Vehicle Sustainable Routing with Time-Dependent Stochastic Information." Sustainability 12, no. 6 (2020): 2464. http://dx.doi.org/10.3390/su12062464.

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We propose a routing method for electric vehicles that finds a route with minimal expected travel time in time-dependent stochastic networks. The method first estimates whether the vehicle can reach the destination with the current battery level and selects potential reasonable charging stations if needed. Then, the route-search problem is formulated as a shortest path problem with time-dependent stochastic disruptions, using a Markov decision process. The shortest path problem is solved by an approximate dynamic programming algorithm to improve calculation efficiency in complex networks. Several simulation cases and a scenario-based example are given to prove the validity of the method.

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42

RODONAIA, Irakli, and Artioma MERABIANI. "Real-World Applications of the Vehicle Routing Problem in Georgia." Journal of Technical Science and Technologies 5, no. 2 (2017): 45–49. http://dx.doi.org/10.31578/jtst.v5i2.109.

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This study will examine the value of real-time traffic information to optimalvehicle routing in a non- stationary stochastic network. The goal is to find asystematic approach to aid in the implementation of transportation systemsintegrated with real-time information technology. Finding the way to develop decision making procedures for determining the optimal driver attendance time, optimal departure times, and optimal routing policies is the primary goal. With studies based on a road network in Georgia, we aim to reduce vehicle usage whilesatisfying or improving service levels for just-in-time delivery.

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43

Hu, Shan Liang, and Chang Shi Liu. "An Effective Tabu Search Algorithm for the Vehicle Routing Problem with Stochastic Demands." Advanced Materials Research 282-283 (July 2011): 375–78. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.375.

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The vehicle routing problem with stochastic demands is considered in this paper, and an effective tabu search algorithm for the proposed problem. The goal consists of minimizing the vehicle number and expected distance traveled in order to serve all customers’ demands. Finally, a numerical example is given to show the effectiveness of the algorithm.

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44

Marković, Danijel, Goran Petrovć, Žarko Ćojbašić, and Aleksandar Stanković. "THE VEHICLE ROUTING PROBLEM WITH STOCHASTIC DEMANDS IN AN URBAN AREA – A CASE STUDY." Facta Universitatis, Series: Mechanical Engineering 18, no. 1 (2020): 107. http://dx.doi.org/10.22190/fume190318021m.

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The vehicle routing problem with stochastic demands (VRPSD) is a combinatorial optimization problem. The VRPSD looks for vehicle routes to connect all customers with a depot, so that the total distance is minimized, each customer visited once by one vehicle, every route starts and ends at a depot, and the travelled distance and capacity of each vehicle are less than or equal to the given maximum value. Contrary to the classical VRP, in the VRPSD the demand in a node is known only after a vehicle arrives at the very node. This means that the vehicle routes are designed in uncertain conditions. This paper presents a heuristic and meta-heuristic approach for solving the VRPSD and discusses the real problem of municipal waste collection in the City of Niš.

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Florio, Alexandre M., Richard F. Hartl, and Stefan Minner. "New Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands." Transportation Science 54, no. 4 (2020): 1073–90. http://dx.doi.org/10.1287/trsc.2020.0976.

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This paper considers the vehicle routing problem with stochastic demands under optimal restocking. We develop an exact algorithm that is effective for solving instances with many vehicles and few customers per route. In our experiments, we show that in these instances, solving the stochastic problem is most relevant (i.e., the potential gains over the deterministic equivalent solution are highest). The proposed branch-price-and-cut algorithm relies on an efficient labeling procedure, exact and heuristic dominance rules, and completion bounds to price profitable columns. Instances with up to 76 nodes could be solved in less than five hours, and instances with up to 148 nodes could be solved in long runs of the algorithm. The experiments also allowed new findings on the problem. The solution to the stochastic problem is up to 10% less costly than the deterministic equivalent solution. Opening new routes reduces restocking costs and in many cases results in solutions with less transportation costs. When the number of routes is not fixed, the optimal solutions under detour-to-depot and optimal restocking are nearly equivalent. However, when the number of routes is limited and the expected demand along a route is allowed to exceed the vehicle capacity, optimal restocking may be significantly more cost-effective than the detour-to-depot policy.

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Wang, Zheng, and Lin Lin. "A Simulation-Based Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Travel Times." Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/127156.

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This paper presents a flexible solution methodology for the capacitated vehicle routing problem with stochastic travel times (CVRPSTT). One of the basic ideas of the methodology is to consider a vehicle working time lower than the actual maximum vehicle working time when designing CVRPSTT solutions. In this way, the working time surplus can be used to cope with unexpected congestions when necessary. Another important idea is to transform the CVRPSTT instance to a limited set of capacitated vehicle routing problems (CVRP), each of which is defined by a given percentage of the maximum vehicle working time. Thus, our approach can take advantage of any efficient heuristic that already exists for the CVRP. Based on the two key ideas, this paper presents a simulation-based algorithm, in which Monte Carlo simulation is used to obtain estimates of the cost and the reliability of each solution, and the Clarke and Wright heuristic is improved to generate more reliable solutions. Finally, a number of numerical experiments are done in the paper with the purpose of analyzing the efficiency of the described methodology under different uncertainty scenarios.

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Gonzalez-L., Elsa Cristina, Wilson Adarme-Jaimes, and Javier Arturo Orjuela-Castro. "Stochastic mathematical model for vehicle routing problem in collecting perishable products." DYNA 82, no. 189 (2015): 199–206. http://dx.doi.org/10.15446/dyna.v82n189.48549.

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Chang, Mei-Shiang, Yi-Chen Lin, and Che-Fu Hsueh. "Vehicle Routing and Scheduling Problem with Time Windows and Stochastic Demand." Transportation Research Record: Journal of the Transportation Research Board 1882, no. 1 (2004): 79–87. http://dx.doi.org/10.3141/1882-10.

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Bertsimas, Dimitris J., and Garrett van Ryzin. "A Stochastic and Dynamic Vehicle Routing Problem in the Euclidean Plane." Operations Research 39, no. 4 (1991): 601–15. http://dx.doi.org/10.1287/opre.39.4.601.

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Secomandi, Nicola. "A Rollout Policy for the Vehicle Routing Problem with Stochastic Demands." Operations Research 49, no. 5 (2001): 796–802. http://dx.doi.org/10.1287/opre.49.5.796.10608.

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