Relevant bibliographies by topics / Stochastic Vehicle Routing Problem (SVRP)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Stochastic Vehicle Routing Problem (SVRP)'

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Published: 5 June 2025
Last updated: 24 June 2025

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

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Stochastic Vehicle Routing Problem (SVRP)"

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Ružička, Vladimír. "Aplikace problému Obchodního cestujícího v reálném prostředí distribuční společnosti." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236578.

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This paper deals with optimal distribution issues. One may find listed problems of real life linked to distribution. Moreover, there are explained travelling salesman problem, vehicle routing problem and its variants. This work brings an overview of different ways how to solve vehicle routing problem. In practical part, there is an analysis of distribution of real company. The concept of application is presented in the second part of this paper. This concept could reduce costs of distribution in analyzed company. Testing is aimed mainly on the variant VRPCL (Vehicle Routing Problem with Continuos Loading).
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Maqueo, Rodrigo Rubio. "Dynamic-stochastic vehicle routing and inventory problem." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/10593.

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Almutairi, Abdulwahab. "Sim-heuristic algorithms for Robust Vehicle Routing Problem with Stochastic Demand." Thesis, University of Portsmouth, 2016. https://researchportal.port.ac.uk/portal/en/theses/simheuristic-algorithms-for-robust-vehicle-routing-problem-with-stochastic-demand(c418aed0-29a5-49f5-b191-ece4796e5827).html.

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The Vehicle Routing Problem with Stochastic Demand (VRPSD) is a fundamental problem underlying many operational challenges in the field of logistic and supply chain management. The VRPSD is a well-known NP-hard problem whereby a fleet of vehicles is located at a single depot. Each vehicle has a limited capacity and has to serve a number of customers whose actual demands are known only when the vehicle arrives at the customers’ locations. The VRPSD arises in practice whenever a company faces the problem of delivering to a set of customers, whose demands are uncertain. The solution to the VRPSD includes the optimisation of complete routing schedules whilst minimising the transportation costs (fixed costs and variable costs) to satisfy all the constraints in the problem. This study proposes three approaches: the robust routing model with sim-heuristic, randomised Iterated Greedy (IG) algorithm with Monte Carlo Simulation (MCS) and finally IG algorithm with local search to solve the VRPSD. The main aim of using the robust routing model with sim-heuristics is to build robust solutions by combining simulation and optimisation using heuristic methods. This is to handle uncertainty as well as to optimise against any worst instance that might arise due to data uncertainty. Several heuristics have been combined with simulation to deal with stochastic demand. In our version of the approach, the first one is a randomised Clarke and Wright Saving (CWS) algorithm step after which an MCS is incorporated in order to improve the final solutions of VRPSD. The second approach proposed the combination of randomised IG algorithm with MCS to be applied on the VRPSD. The final approach is to use an IG algorithm with local search, based on the aforementioned first approach, in order to improve the solutions generated. Local search has been proven to be an effective technique for obtaining good solutions. The developed robust routing model and sim-heuristic algorithms are tested on well-known benchmark instances and a real-life case study is considered in order to evaluate the effectiveness of the proposed methodologies. The computational results showed that the proposed methodologies are capable of finding useful solutions for the VRPSD and that they are good/robust for the stochastic nature of the problem instances. After computing the average costs from each instance, we also computed the best solution and found that they both could be highly promising and useful for decision makers. The results obtained are quite competitive when compared to the other algorithms found in the literature.
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Balun, Pairote. "A Stochastic Vendor Managed Inventory Problem and Its Variations." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4987.

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We analyze the problem of distributing units of a product, by a capacitated vehicle, from one storage location (depot) to multiple retailers. The demand processes at the retailers are stochastic and time-dependent. Based on current inventory information, the decision maker decides how many units of the product to deposit at the current retailer, or pick up at the depot, and which location to visit next. We refer to this problem as the stochastic vendor managed inventory (SVMI) problem. In the Markov decision process model of the SVMI problem, we show how a retailer continues to be the vehicle's optimal destination as inventory levels of the retailers vary. Furthermore, an optimal inventory action is shown to have monotone relations with the inventory levels. The multi-period SVMI problem and the infinite horizon (periodic) SVMI problem are analyzed. Additionally, we develop three suboptimal solution procedures, complete a numerical study, and present a case study, which involves a distribution problem at the Coca-Cola Enterprises, Inc. We consider four variations of the SVMI problem, which differ in the available state information and/or the vehicle routing procedure. Analytically, we compare the optimal expected total rewards for the SVMI problem and its variations. Our computational experience suggests a complementary relationship between the quality of state information and the size of the set of retailers that the vehicle can visit.
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Lundkvist, Henrik. "Robust Vehicle Routing in an Urban Setting." Thesis, KTH, Optimeringslära och systemteori, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186248.

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In this thesis, the vehicle routing problem with stochastic, and time dependent, travel times is studied. The stochastic travel times are estimated from historical drive data. The variation of the drive times, as well as that of the variance, during the day was modeled.   The purpose of the thesis was to propose a method of handling the congestion related traffic impediments in an urban setting. Since the majority of times of delivery in the empirical test cases studied correlate with the time period of high traffic load, an efficient and robust handling of such traffic scenarios is of high importance.  It is shown that the stochastic models will shift the estimated arrivals to customers from the more volatile early and late extremes to more central regions of the time window. Previously delivered routes were evaluated both with the standard algorithm and the proposed stochastic algorithm. The difference between the actual drive times and the calculated drive times were analyzed by studying the correlation of the drive times between each customer in the route. It was shown that the routes of the proposed stochastic method increased this correlation. The drive times between nodes where also perturbed with a Gamma distributed noise. The results from the stochastic algorithm showed higher resilience to this disturbance than did the deterministic models.<br>I detta examensarbete har fordonsruttningsproblemet, VRP, med stokastiska och tidsberonds körtider behandlöats. De stokastiska körtiderna har estimerats från tidigare insamlad hasighetsdata. Modeller för körtidernas och variansernas förändring under dagen har tagits fram.   Syftet med examensarbetet var att föreslå en metod för hur påverkan på körtider av förutsägbar trafikträngsel i en urban trafikmiljö kan hanteras. Eftersom huvuddelen av alla leveranser sammanfaller med de tider på dygnet då trafikbelastning är som högst, ar är en effektiv och robust metod för att hantera sådana störningar av stor vikt. Det visas att den stokastiska modellen kommer att förflyttar ankomster från början och slutet av tidsfönstret till den mer okänsliga mittregionen. Tidigare, utförda leveranser studerades både med den ursprungliga deterministiska modellen och här framtagna stokastiska modellen. Skillnaden mellan de två analyserades genom att studera korrelationen mellan körtiderna som de beräknats av de två modellerna och de upmätta tiderna som de loggats av leveransfordonen. Det visas att korrelationen mellan körtiderna mellan de stokastiska körtiderna och de verkliga körtiderna är högre än korrelationen mellan de deterministiska körtiderna och de verkliga. Rutterna som föreslagits av den stokastiska modellen var också mer tlig mot störningar.
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Uyar, Emrah. "Routing in stochastic environments." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26554.

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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.<br>Committee Co-Chair: Erera, Alan L.; Committee Co-Chair: Savelsbergh, Martin W. P.; Committee Member: Ergun, Ozlem; Committee Member: Ferguson, Mark; Committee Member: Kleywegt, Anton J.. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Sadeghi, Azadeh. "Social Cost-Vehicle Routing Problem in Post-Disaster Humanitarian Logistics." Ohio University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1626446795459101.

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Coral, Daniel Bustos. "A cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29102018-160027/.

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This dissertation presents a cartographic approach to the dynamic vehicle routing problem with time windows and stochastic customers (DVRPTWSC). The objectives are to minimize the total travel time and maximize the number of new requests served. Addressing the DVRPTWSC requires solving the vehicle routing problem with time windows (VRPTW). A memetic algorithm (MA) for the VRPTW is proposed. The MA prunes the search space using the information gathered by a clustering procedure, which is applied to customers spatial data. The cartographic approach to the DVRPTWSC is incorporated into a multiagent system where a dispatcher agent plans the routes for vehicle agents. Before creating the initial routing plan, a cartographic processing is applied. This procedure uses hierarchical clustering to divide the region where customers are located into a hierarchy of nested regions. The initial routing plan considers known requests and potential requests sampled from known probability distributions. It is created using the search operators of the MA, which in turn use the information obtained from the hierarchical clustering to perform the search. Over the planning horizon, the dispatcher updates the routing plan: Potential requests that were included in the initial routing plan and do not materialize are removed and new requests are processed using the assignation of requests based on nested regions (ARNR). The ARNR procedure is aimed at reducing the number of vehicles considered for serving new requests. It tries to assign the requests among the vehicles that can serve them at low detour costs. The nested regions created in the cartographic processing are used to identify such vehicles. Experimental results show that the proposed MA performs competitively with state-of-the-art heuristics for the VRPTW. The proposed approach to the DVRPTWSC outperforms approaches that do not include potential requests in the initial routing plan. The use of the ARNR procedure significantly reduces the number of vehicles considered for serving new requests, and it yields solutions similar to those obtained when considering all vehicles in operation. The proposed approach performs consistently under three levels of dynamism: low, medium, and high.<br>Esta dissertação apresenta uma abordagem cartográfica para o problema de roteamento de veículos dinâmico com janelas de tempo e clientes estocásticos (DVRPTWSC, por sua sigla em inglês). Os objetivos considerados são minimizar o tempo total de viagem e maximizar o número de pedidos novos atendidos. Para abordar o DVRPTWSC é necessário resolver o problema de roteamento de veículos com janelas de tempo (VRPTW, por sua sigla em inglês). Assim, para tratar o VRPTW propõe-se um algoritmo memético (MA, por sua sigla em inglês). O MA reduz o espaço de busca usando informação obtida por meio de um procedimento de clusterização, o qual é aplicado aos dados espaciais dos clientes. Para o DVRPTWSC, a abordagem cartográfica é incorporada em um sistema multiagente, no qual um agente roteirizador planeja as rotas para os agentes veículos. O processamento cartográfico é aplicado antes de criar o plano de rotas inicial para o DVRPTWSC. Este procedimento usa clusterização hierárquica para dividir a região onde estão os clientes em uma hierarquia de regiões encaixadas. O plano de rotas inicial considera pedidos conhecidos e pedidos potenciais amostrados de distribuições de probabilidade conhecidas. Para obter o plano de rotas inicial, usam-se os operadores de busca do MA, os quais utilizam a informação obtida da clusterização hierárquica para fazer a busca. Ao longo do horizonte de planejamento, o roteirizador atualiza o plano de rotas: Pedidos potenciais que foram considerados no plano de rotas inicial e que não foram consolidados são removidos e novos pedidos são incluídos usando o procedimento assignation of requests based on nested regions (ARNR). O procedimento ARNR visa reduzir o número de veículos considerados para atender novos pedidos. Para isso, tenta designar os novos pedidos aos veículos disponíveis para o atendimento que possuem os menores custos de desvio da rota pré-determinada. As regiões encaixadas criadas no processamento cartográfico são utilizadas para identificar esses veículos. Para o VRPTW, resultados experimentais mostram que o MA proposto é competitivo com métodos do estado da arte. A abordagem proposta para o DVRPTWSC supera abordagens que não incluem pedidos potenciais no plano de rotas inicial. O uso do procedimento ARNR reduz significativamente o número de veículos considerados para atender novos pedidos, e produz soluções similares às produzidas quando se consideram todos os veículos em operação. A abordagem desenvolvida para o DVRPTWSC tem um desempenho consistente para três níveis de dinamismo: baixo, médio e alto.
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Helal, Nathalie. "An evidential answer for the capacitated vehicle routing problem with uncertain demands." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0208/document.

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Le problème de tournées de véhicules avec contrainte de capacité est un problème important en optimisation combinatoire. L'objectif du problème est de déterminer l'ensemble des routes, nécessaire pour servir les demandes déterministes des clients ayant un cout minimal, tout en respectant la capacité limite des véhicules. Cependant, dans de nombreuses applications réelles, nous sommes confrontés à des incertitudes sur les demandes des clients. La plupart des travaux qui ont traité ce problème ont supposé que les demandes des clients étaient des variables aléatoires. Nous nous proposons dans cette thèse de représenter l'incertitude sur les demandes des clients dans le cadre de la théorie de l'évidence - un formalisme alternatif pour modéliser les incertitudes. Pour résoudre le problème d'optimisation qui résulte, nous généralisons les approches de modélisation classiques en programmation stochastique. Précisément, nous proposons deux modèles pour ce problème. Le premier modèle, est une extension de l'approche chance-constrained programming, qui impose des bornes minimales pour la croyance et la plausibilité que la somme des demandes sur chaque route respecte la capacité des véhicules. Le deuxième modèle étend l'approche stochastic programming with recourse: l'incertitude sur les recours (actions correctives) possibles sur chaque route est représentée par une fonction de croyance et le coût d'une route est alors son coût classique (sans recours) additionné du pire coût espéré des recours. Certaines propriétés de ces deux modèles sont étudiées. Un algorithme de recuit simulé est adapté pour résoudre les deux modèles et est testé expérimentalement<br>The capacitated vehicle routing problem is an important combinatorial optimisation problem. Its objective is to find a set of routes of minimum cost, such that a fleet of vehicles initially located at a depot service the deterministic demands of a set of customers, while respecting capacity limits of the vehicles. Still, in many real-life applications, we are faced with uncertainty on customer demands. Most of the research papers that handled this situation, assumed that customer demands are random variables. In this thesis, we propose to represent uncertainty on customer demands using evidence theory - an alternative uncertainty theory. To tackle the resulting optimisation problem, we extend classical stochastic programming modelling approaches. Specifically, we propose two models for this problem. The first model is an extension of the chance-constrained programming approach, which imposes certain minimum bounds on the belief and plausibility that the sum of the demands on each route respects the vehicle capacity. The second model extends the stochastic programming with recourse approach: it represents by a belief function for each route the uncertainty on its recourses (corrective actions) and defines the cost of a route as its classical cost (without recourse) plus the worst expected cost of its recourses. Some properties of these two models are studied. A simulated annealing algorithm is adapted to solve both models and is experimentally tested
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"Stochastic vehicle routing with time windows." 2007. http://library.cuhk.edu.hk/record=b5893140.

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Chen, Jian.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.<br>Includes bibliographical references (leaves 81-85).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Background --- p.1<br>Chapter 1.2 --- Literature Review --- p.4<br>Chapter 1.2.1 --- Vehicle Routing Problem with Stochastic Demands --- p.5<br>Chapter 1.2.2 --- Vehicle Routing Problem with Stochastic Travel Times --- p.8<br>Chapter 1.3 --- The Vehicle Routing Problem with Time Windows and Stochastic Travel Times --- p.10<br>Chapter 2 --- Notations and Formulations --- p.12<br>Chapter 2.1 --- Problem Definitions --- p.12<br>Chapter 2.2 --- A Two-Index Stochastic Programming Model --- p.14<br>Chapter 2.3 --- The Second Stage Problem --- p.17<br>Chapter 3 --- The Scheduling Problem --- p.20<br>Chapter 3.1 --- The Overtime Cost Problem --- p.22<br>Chapter 3.2 --- The Waiting and Late Cost Problem --- p.27<br>Chapter 3.3 --- The Algorithm --- p.37<br>Chapter 4 --- The Integer L-Shaped Method --- p.40<br>Chapter 4.1 --- Linearization of the Objective Function --- p.41<br>Chapter 4.2 --- Handling the Constraints --- p.42<br>Chapter 4.3 --- Branching --- p.44<br>Chapter 4.4 --- The Algorithm --- p.44<br>Chapter 5 --- Feasibility Cuts --- p.47<br>Chapter 5.1 --- Connected Component Methods --- p.48<br>Chapter 5.2 --- Shrinking Method --- p.49<br>Chapter 6 --- Optimality Cuts --- p.52<br>Chapter 6.1 --- Lower Bound I for the EOT Cost --- p.53<br>Chapter 6.2 --- Lower Bounds II and III for the EOT Cost --- p.56<br>Chapter 6.3 --- Lower Bound IV for the EWL Cost --- p.57<br>Chapter 6.4 --- Lower Bound V for Partial Routes --- p.61<br>Chapter 6.5 --- Adding Optimality Cuts --- p.66<br>Chapter 7 --- Numerical Experiments --- p.70<br>Chapter 7.1 --- Effectiveness in Separating the Rounded Capacity Inequalities --- p.71<br>Chapter 7.2 --- Effectiveness of the Lower Bounds --- p.72<br>Chapter 7.3 --- Performance of the L-shaped Method --- p.74<br>Chapter 8 --- Conclusion and Future Research --- p.79<br>Bibliography --- p.81<br>Chapter A --- Generation of Test Instances --- p.86
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Book chapters on the topic "Stochastic Vehicle Routing Problem (SVRP)"

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Bianchi, Leonora, Mauro Birattari, Marco Chiarandini, et al. "Metaheuristics for the Vehicle Routing Problem with Stochastic Demands." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30217-9_46.

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Shen, Zhihong, Fernando Ordòñez, and Maged M. Dessouky. "The Stochastic Vehicle Routing Problem for Minimum Unmet Demand." In Springer Optimization and Its Applications. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88617-6_13.

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Mańdziuk, Jacek, and Maciej Świechowski. "Swarm Intelligence in Solving Stochastic Capacitated Vehicle Routing Problem." In Artificial Intelligence and Soft Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59060-8_49.

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Skålnes, Jørgen, Lars Dahle, Henrik Andersson, Marielle Christiansen, and Lars Magnus Hvattum. "The Multistage Stochastic Vehicle Routing Problem with Dynamic Occasional Drivers." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59747-4_17.

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Ma, Jun, Jihui Zhang, and Yiyun Guo. "The Stochastic and Dynamic Vehicle Routing Problem: A Literature Review." In Proceedings of 2021 Chinese Intelligent Automation Conference. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6372-7_39.

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Hämmerle, Alexander, and Martin Ankerl. "Solving a Vehicle Routing Problem with Ant Colony Optimisation and Stochastic Ranking." In Computer Aided Systems Theory - EUROCAST 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53856-8_33.

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Ando, Ei, Binay Bhattacharya, Yuzhuang Hu, Tsunehiko Kameda, and Qiaosheng Shi. "Selecting Good a Priori Sequences for Vehicle Routing Problem with Stochastic Demand." In Theoretical Aspects of Computing – ICTAC 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23283-1_6.

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Brinkmann, Jan. "The Stochastic-Dynamic Multi-Vehicle Inventory Routing Problem for Bike Sharing Systems." In Active Balancing of Bike Sharing Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35012-3_5.

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Marinakis, Yannis, Magdalene Marinaki, and Paraskevi Spanou. "A Memetic Differential Evolution Algorithm for the Vehicle Routing Problem with Stochastic Demands." In Adaptation, Learning, and Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14400-9_9.

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Biesinger, Benjamin, Bin Hu, and Günther R. Raidl. "A Variable Neighborhood Search for the Generalized Vehicle Routing Problem with Stochastic Demands." In Evolutionary Computation in Combinatorial Optimization. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16468-7_5.

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Conference papers on the topic "Stochastic Vehicle Routing Problem (SVRP)"

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Tang, Kaiqiang, Huiqiao Fu, Jiasheng Liu, Guizhou Deng, Yuanyang Lu, and Chunlin Chen. "Dynamic Capacitated Vehicle Routing Problem with Stochastic Requests Using Deep Reinforcement Learning." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831573.

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Rios, Brenner Humberto Ojeda, and Eduardo C. Xavier. "The Capacitated Multi-Depot Vehicle Routing Problem with Stochastic Pickups and Deliveries." In 2024 L Latin American Computer Conference (CLEI). IEEE, 2024. http://dx.doi.org/10.1109/clei64178.2024.10700341.

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Ngoc Hoai, Nguyen Hoang, Le The Kien, and Ha Thi Xuan Chi. "Simheuristics Algorithms for Two-Echelon Vehicle Routing Problem with Stochastic Travel Time and Mobile Satellites Synchronization." In 2024 International Conference on Logistics and Industrial Engineering (ICLIE). IEEE, 2024. https://doi.org/10.1109/iclie61478.2024.11015381.

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Sathyanarayanan, S., and K. Suresh Joseph. "A survey on stochastic vehicle routing problem." In 2014 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2014. http://dx.doi.org/10.1109/icices.2014.7033895.

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Isomura, S., T. Sato, T. Shiina, and J. Imaizumi. "L-shaped Method for the Stochastic Vehicle Routing Problem." In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2019. http://dx.doi.org/10.1109/ieem44572.2019.8978752.

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Sun, Liucheng, Yan Sun, and Wenjia Zheng. "The Stochastic and Dynamic Vehicle Routing Problem with Crowdshipping." In 17th COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784480915.159.

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Hsueh, Che-Fu. "The Green Vehicle Routing Problem with Stochastic Travel Speeds." In 16th COTA International Conference of Transportation Professionals. American Society of Civil Engineers, 2016. http://dx.doi.org/10.1061/9780784479896.001.

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Mustakhov, Taukekhan, Yernar Akhmetbek, and Aigerim Bogyrbayeva. "Deep Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problem." In 2023 17th International Conference on Electronics Computer and Computation (ICECCO). IEEE, 2023. http://dx.doi.org/10.1109/icecco58239.2023.10147154.

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Omori, R., and T. Shiina. "New Formulation for the Vehicle Routing Problem with Stochastic Demands." In 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2020. http://dx.doi.org/10.1109/iiai-aai50415.2020.00144.

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Omori, Ryota, and Takayuki Shiina. "Solution Algorithm for the Vehicle Routing Problem with Stochastic Demands." In 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS). IEEE, 2020. http://dx.doi.org/10.1109/scisisis50064.2020.9322773.

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