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Статті в журналах з теми "Uncertain Capacitated Arc Routing Problem"
MacLachlan, Jordan, Yi Mei, Juergen Branke, and Mengjie Zhang. "Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems." Evolutionary Computation 28, no. 4 (December 2020): 563–93. http://dx.doi.org/10.1162/evco_a_00267.
Повний текст джерелаLiu, Yuxin, Yi Mei, Mengjie Zhang, and Zili Zhang. "A Predictive-Reactive Approach with Genetic Programming and Cooperative Coevolution for the Uncertain Capacitated Arc Routing Problem." Evolutionary Computation 28, no. 2 (June 2020): 289–316. http://dx.doi.org/10.1162/evco_a_00256.
Повний текст джерелаLiu, Jialin, Ke Tang, and Xin Yao. "Robust Optimization in Uncertain Capacitated Arc Routing Problems: Progresses and Perspectives [Review Article]." IEEE Computational Intelligence Magazine 16, no. 1 (February 2021): 63–82. http://dx.doi.org/10.1109/mci.2020.3039069.
Повний текст джерелаWang, Juan, Ke Tang, Jose A. Lozano, and Xin Yao. "Estimation of the Distribution Algorithm With a Stochastic Local Search for Uncertain Capacitated Arc Routing Problems." IEEE Transactions on Evolutionary Computation 20, no. 1 (February 2016): 96–109. http://dx.doi.org/10.1109/tevc.2015.2428616.
Повний текст джерелаBabaee Tirkolaee, Erfan, Iraj Mahdavi, Mir Mehdi Seyyed Esfahani, and Gerhard-Wilhelm Weber. "A hybrid augmented ant colony optimization for the multi-trip capacitated arc routing problem under fuzzy demands for urban solid waste management." Waste Management & Research 38, no. 2 (August 13, 2019): 156–72. http://dx.doi.org/10.1177/0734242x19865782.
Повний текст джерелаBabaee Tirkolaee, Erfan, Alireza Goli, Maryam Pahlevan, and Ramina Malekalipour Kordestanizadeh. "A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization." Waste Management & Research 37, no. 11 (August 15, 2019): 1089–101. http://dx.doi.org/10.1177/0734242x19865340.
Повний текст джерелаUsberti, Fábio Luiz, Paulo Morelato França, and André Luiz Morelato França. "The open capacitated arc routing problem." Computers & Operations Research 38, no. 11 (November 2011): 1543–55. http://dx.doi.org/10.1016/j.cor.2011.01.012.
Повний текст джерелаKirlik, Gokhan, and Aydin Sipahioglu. "Capacitated arc routing problem with deadheading demands." Computers & Operations Research 39, no. 10 (October 2012): 2380–94. http://dx.doi.org/10.1016/j.cor.2011.12.008.
Повний текст джерелаBenavent, E., V. Campos, A. Corberan, and E. Mota. "The Capacitated Arc Routing Problem: Lower bounds." Networks 22, no. 7 (December 1992): 669–90. http://dx.doi.org/10.1002/net.3230220706.
Повний текст джерелаAmaya, Alberto, André Langevin, and Martin Trépanier. "The capacitated arc routing problem with refill points." Operations Research Letters 35, no. 1 (January 2007): 45–53. http://dx.doi.org/10.1016/j.orl.2005.12.009.
Повний текст джерелаДисертації з теми "Uncertain Capacitated Arc Routing Problem"
Helal, Nathalie. "An evidential answer for the capacitated vehicle routing problem with uncertain demands." Thesis, Artois, 2017. http://www.theses.fr/2017ARTO0208/document.
Повний текст джерелаThe 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
Bode, Claudia [Verfasser], and Stefan [Verfasser] Irnich. "Cut-first branch-and-price-second for the capacitated arc-routing problem / Claudia Bode, Stefan Irnich." Mainz : Universitätsbibliothek der Johannes Gutenberg-Universität Mainz, 2011. http://d-nb.info/1225558654/34.
Повний текст джерелаBernardo, Papini Marcella Verfasser], Pannek [Akademischer Betreuer] Pannek, Pannek [Gutachter] Pannek, and Hans-Dietrich [Gutachter] [Haasis. "Robust Capacitated Vehicle Routing Problem with Uncertain Demands / Marcella Bernardo Papini ; Gutachter: Pannek Pannek, Hans-Dietrich Haasis ; Betreuer: Pannek Pannek." Bremen : Staats- und Universitätsbibliothek Bremen, 2019. http://d-nb.info/1196286310/34.
Повний текст джерелаUsberti, Fábio Luiz 1982. "Métodos heurísticos e exatos para o problemas de roteamento em arcos capacitado e aberto = Heuristic and exact approaches for the open capacitated arc routing problem." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260624.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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Resumo:O problema de roteamento em arcos capacitado e aberto (open capacitated arc routing problem, OCARP) é um problema de otimização combinatorial NP-difícil em que, dado um grafo não-direcionado, o objetivo consiste em encontrar um conjunto de rotas de custo mínimo para veículos com capacidade restrita que atendam a demanda de um subconjunto de arestas. O OCARP está relacionado com o problema de roteamento em arcos capacitado (capacitated arc routing problem, CARP), mas difere deste pois o OCARP não possui um nó depósito e as rotas não estão restritas a ciclos. Aplicações da literatura para o OCARP são discutidas. Uma formula ção de programação linear inteira é fornecida junto com propriedades do problema. Uma metaheurística GRASP (greedy randomized adaptive search procedure) com reconexão por caminhos (path-relinking) é proposta e comparada com outras metaheurísticas bem-sucedidas da literatura. Algumas características do GRASP são: (i) ajuste reativo de parâmetros, cujos valores são estocasticamente selecionados com viés 'aqueles valores que produziram, em média, as melhores soluções; (ii) um filtro estatístico que descarta soluções iniciais caso estas tenham baixa probabilidade de superar a melhor solução incumbente; (iii) uma busca local infactível que gera soluções de baixo custo utilizadas para explorar fronteiras factíveis/infactíveis do espaço de soluções; (iv) a reconexão por caminhos evolutiva aprimora progressivamente um conjunto de soluções de elevada qualidade (soluções elites). Testes computacionais foram conduzidos com instâncias CARP e OCARP e os resultados mostram que o GRASP é bastante competitivo, atingindo os melhores desvios entre os custos das soluções e limitantes inferiores conhecidos. Este trabalho também propõe um algoritmo exato para o OCARP que se baseia no paradigma branch-and-bound. Três limitantes inferiores são propostos e um deles utiliza o método dos subgradientes para resolver uma relaxação lagrangeana. Testes computacionais comparam o algoritmo branch-and-bound com o CPLEX resolvendo um modelo reduzido OCARP de programa ção linear inteira. Os resultados revelam que o algoritmo branch-and-bound apresentou resultados melhores que o CPLEX no que diz respeito aos desvios entre limitantes e ao número de melhores soluções
Abstract: The Open Capacitated Arc Routing Problem (OCARP) is an NP-hard combinatorial optimization problem where, given an undirected graph, the objective is to find a minimum cost set of tours that services a subset of edges with positive demand under capacity constraints. This problem is related to the Capacitated Arc Routing Problem (CARP) but differs from it since OCARP does not consider a depot, and tours are not constrained to form cycles. Applications to OCARP from literature are discussed. An integer linear programming formulation is given, followed by some properties of the problem. A Greedy Randomized Adaptive Search Procedure (GRASP) with path-relinking (PR) solution method is proposed and compared with other successful metaheuristics. Some features of this GRASP with PR are (i) reactive parameter tuning, where the metaheuristic parameters values are stochastically selected biased in favor of those values which produced the best solutions in average; (ii) a statistical filter, which discards initial solutions if they are unlikely to improve the incumbent best solution; (iii) infeasible local search, where high-quality solutions, though infeasible, are used to explore the feasible/infeasible boundaries of the solution space; (iv) evolutionary PR, a recent trend in which a pool of elite solutions is progressively improved by relinking pairs of elite solutions. Computational tests were conducted for both CARP and OCARP instances, and results reveal that the GRASP with PR is very competitive, achieving the best overall deviation from lower bounds. This work also proposes an exact algorithm for OCARP, based on the branch-and-bound paradigm. Three lower bounds are proposed, one of them uses a subgradient method to solve a Lagrangian relaxation. The computational tests compared the proposed branch-and-bound with a commercial state-of-the-art ILP solver. Results reveal that the branch-and-bound outperformed CPLEX in the overall average deviation from lower bounds
Doutorado
Automação
Doutor em Engenharia Elétrica
Franc, Zdeněk. "Kapacitní problém listonoše." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193813.
Повний текст джерелаNunes, Ana Catarina de Carvalho. "Sectorização de redes em problemas com procura nos arcos e limitações de capacidade." Doctoral thesis, Instituto Superior de Economia e Gestão, 2009. http://hdl.handle.net/10400.5/1252.
Повний текст джерелаO problema de sectorização e rotas nos arcos (SARP) e definido para modelar as actividades associadas as ruas de zonas urbanas, como sendo o caso da recolha municipal de resíduos sólidos. Com o SARP pretende obter-se uma partição da rede de ruas em sectores e construir um conjunto de viagens em cada sector, minimizando a duração total das viagens. São apresentadas formulações para o SARP, conhecidas por formulações de fluxos por utilizarem variáveis de fluxo. Estas formulações tem a vantagem de impedir a formação de viagens ilegais usando um número polinomial de restrições, em alternativa as habituais restrições de eliminação de subcircuitos, que são em número exponencial. Com base nestas formulações são determinados limites inferiores para o valor óptimo do SARP e, para instancias de baixa dimensão, são obtidas soluções óptimas. São propostas duas heurísticas em duas fases e uma heurística de melhor inserção, com várias versões cada. Nas heurísticas em duas fases, na fase 1 constroem-se os sectores usando duas heurísticas possíveis, enquanto que na fase 2 e resolvido um problema de rotas com procura nos arcos e restrições de capacidade num grafo misto (MCARP) para determinar as viagens em cada sector. A heurística de melhor inserção determina os sectores e as viagens em simultâneo. Para alem do custo da solução, os algoritmos são comparados usando outros critérios de avaliação, tais como o desequilíbrio, o diâmetro e medidas da dispersão. São mostrados e analisados os resultados obtidos para três conjuntos de instâncias, incluindo instâncias de grandes dimensões com ate 401 nodos e 1056 ligacões (arcos ou arestas).
The Sectoring-Arc Routing Problem (SARP) is introduced to model activities associated with the streets of large urban areas, like municipal waste collection. The aim is to partition the street network into a given number of sectors and to build a set of vehicle trips in each sector, to minimize the total duration of the trips. SARP formulations using flow variables, known as flow formulations, are given. One of the advantages of this type of formulation is that it involves a polynomial number of constraints to eliminate illegal trips, instead of the usual subtour elimination constraints, which are in exponential number. From these formulations lower bounds for the SARP are derived and, for small instances, optimal solutions are obtained. Two two-phase heuristics and one best insertion method are proposed. In the two-phase methods, phase 1 constructs the sectors using two possible heuristics, while phase 2 solves a Mixed Capacitated Arc Routing Problem (MCARP) to compute the trips in each sector. The best insertion method determines sectors and trips simultaneously. In addition to solution cost, some evaluation criteria such as imbalance, diameter and dispersion measures are used to compare algorithms. Numerical results on large instances with up to 401 nodes and 1056 links (arcs or edges) are reported and analysed.
Al-Hasani, Firas Ali Jawad. "Multiple Constant Multiplication Optimization Using Common Subexpression Elimination and Redundant Numbers." Thesis, University of Canterbury. Electrical and Computer Engineering, 2014. http://hdl.handle.net/10092/9054.
Повний текст джерелаSu, Chia-Nan, and 蘇家男. "The Study of Lower Bound of Capacitated Arc Routing Problem." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/37183606308000649687.
Повний текст джерела國立交通大學
運輸工程與管理系
87
The Capacitated Arc Routing Problem (CARP) is a commonly seen problem in logistic. CARP finds a set of routes that serve each service edge exactly once while satisfying the capacity constraint.CARP was proved to be a NP-Hard problem. Thus, is difficult to find the optimal solution with a reasonable amount of time. Thus, the study focuses in finding a good lower bound which can be used to speed up the B-B process This paper introduces two lower bounds of CARP:1.the Arc Scanning Lower Bound (ASLB). In steady of considering nodes as most reaches do, ASLB use edge to compute the lower bound. We also prove our ASLB is no worse than the performance of NSLB. 2.the Hybrid Lower Bound (HLB). We also propose a HLB method to find the Lower Bound. This method use the idea From Benavent(1992) and Yasufumi(1992). We observe that HLB perform better who the demand of some edges exceed half of the vehicle capacity and the costs of edges differ significantly.
Lu, Li-Chun, and 呂理鈞. "An Iterated Local Search for the Periodic Capacitated Arc Routing Problem." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2h62zk.
Повний текст джерела元智大學
工業工程與管理學系
106
The Periodic Capacitated Arc Routing Problem (PCARP) is a challenging topic with many important applications, such as snow plowing, street sweeping, winter gritting and waste collection. PCARP is an extension of the well-known Capacitated Arc Routing Problem (CARP) which planned over a multi-period horizontal. The objective is to minimize the total distance of the trips on the entire planning horizon to serve the required arcs. The first step is to assign the required arcs to each day by the date combination, and then solve the resulting CARP for each day. Because the PCARP is an NP-hard problem, we propose an effective Iterated Local Search (ILS) to solve it. The result is improved by the Randomized Variable Neighborhood Descent (RVND) which is combined by different kinds of local search. RVND is an efficient way to find more possibilities of results. The proposed ILS is tested on eight sets of classical CARP benchmarks and three sets of PCARP benchmark instances from the literature, and compared with the other algorithms. The computational result shows that ILS is effective to solve the CARP and reaches 80% of best known solutions in all instances. On the other hand, ILS has a good performance in solving PCARP as well. It indicates that ILS is applicable to CARP and PCARP. In the practical application, we applies the ILS to solve the real street washing planning problem in Taipei City. The result shows that the travel distance of street washing planning problem can be reduced by arranging and planning the new routes with our ILS algorithm. In the future, we hope that this research can contribute to public planning.
Tsai, Han-Shiuan, and 蔡函軒. "The Capacitated Arc Routing Problem and Its Application in Street Cleaning Planning." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/60605495043603213545.
Повний текст джерела元智大學
工業工程與管理學系
104
The capacitated arc routing problem (CARP) is a special routing problem which has a variety of practical applications, such as snow plowing, street sweeping, winter gritting, household waste collection, The classical CARP is defined on an undirected graph with a set of edges. Each edge has a routing cost and a demand. The edges with positive demand make up the subset of the required edges. A set of identical vehicles with limited capacity is available. Each required edge has to be served exactly once by one vehicle. Each route must start and end at the depot. The objective of CARP is to find a set of vehicle routes to minimize the total cost. Due to that CARP is a NP-hard problem, this research intends to present an Ant Colony Optimization (ACO) meta-heuristic to solve the CARP. The result is further improved by a local search with path-relinking for ACO. ACO is tested on eight groups of benchmark instances from the literature. The computational results show that ACO is effective to solve the CARP and its performance is highly competitive. ACO reaches 90% best known solutions in all instances. In the practical application, this research applies the proposed ACO to solve the street cleaning planning problem with intermediate refill points in Kaohsiung city. The results are presented in a road network on the Google Map. The results show that the travel distance of street cleaning can be reduced by arranging and planning the new routes under our ACO algorithm. Finally, we hope that this research will add time windows and fuel costs constraints in the future.
Частини книг з теми "Uncertain Capacitated Arc Routing Problem"
MacLachlan, Jordan, Yi Mei, Juergen Branke, and Mengjie Zhang. "An Improved Genetic Programming Hyper-Heuristic for the Uncertain Capacitated Arc Routing Problem." In AI 2018: Advances in Artificial Intelligence, 432–44. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_40.
Повний текст джерелаAnsari Ardeh, Mazhar, Yi Mei, and Mengjie Zhang. "A Novel Genetic Programming Algorithm with Knowledge Transfer for Uncertain Capacitated Arc Routing Problem." In PRICAI 2019: Trends in Artificial Intelligence, 196–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29908-8_16.
Повний текст джерелаArdeh, Mazhar Ansari, Yi Mei, and Mengjie Zhang. "A Parametric Framework for Genetic Programming with Transfer Learning for Uncertain Capacitated Arc Routing Problem." In AI 2020: Advances in Artificial Intelligence, 150–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64984-5_12.
Повний текст джерелаPrins, Christian. "Chapter 7: The Capacitated Arc Routing Problem: Heuristics." In Arc Routing, 131–57. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611973679.ch7.
Повний текст джерелаMuyldermans, Luc, and Gu Pang. "Chapter 10: Variants of the Capacitated Arc Routing Problem." In Arc Routing, 223–53. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611973679.ch10.
Повний текст джерелаBelenguer, José Manuel, Enrique Benavent, and Stefan Irnich. "Chapter 9: The Capacitated Arc Routing Problem: Exact Algorithms." In Arc Routing, 183–221. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611973679.ch9.
Повний текст джерелаLacomme, P., C. Prins, and M. Sevaux. "Multiobjective Capacitated Arc Routing Problem." In Lecture Notes in Computer Science, 550–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36970-8_39.
Повний текст джерелаAhr, Dino, and Gerhard Reinelt. "Chapter 8: The Capacitated Arc Routing Problem: Combinatorial Lower Bounds." In Arc Routing, 159–81. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611973679.ch8.
Повний текст джерелаMei, Yi, Ke Tang, and Xin Yao. "Evolutionary Computation for Dynamic Capacitated Arc Routing Problem." In Studies in Computational Intelligence, 377–401. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38416-5_15.
Повний текст джерелаSaruwatari, Yasufumi, Ryuichi Hirabayashi, and Naonori Nishida. "Subtour Elimination Algorithm for the Capacitated Arc Routing Problem." In Operations Research Proceedings, 334–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-77254-2_37.
Повний текст джерелаТези доповідей конференцій з теми "Uncertain Capacitated Arc Routing Problem"
Mei, Yi, Ke Tang, and Xin Yao. "Capacitated arc routing problem in uncertain environments." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586031.
Повний текст джерелаMacLachlan, Jordan, and Yi Mei. "Look-Ahead Genetic Programming for Uncertain Capacitated Arc Routing Problem." In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504785.
Повний текст джерелаWang, Juan, Ke Tang, and Xin Yao. "A memetic algorithm for uncertain Capacitated Arc Routing Problems." In 2013 IEEE Workshop on Memetic Computing (MC). IEEE, 2013. http://dx.doi.org/10.1109/mc.2013.6608210.
Повний текст джерелаWang, Shaolin, Yi Mei, and Mengjie Zhang. "Novel ensemble genetic programming hyper-heuristics for uncertain capacitated arc routing problem." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3321707.3321797.
Повний текст джерелаMei, Yi, and Mengjie Zhang. "Genetic programming hyper-heuristic for multi-vehicle uncertain capacitated arc routing problem." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3205661.
Повний текст джерелаWang, Shaolin, Yi Mei, John Park, and Mengjie Zhang. "Evolving Ensembles of Routing Policies using Genetic Programming for Uncertain Capacitated Arc Routing Problem." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002749.
Повний текст джерелаArdeh, Mazhar Ansari, Yi Mei, and Mengjie Zhang. "Genetic programming hyper-heuristic with knowledge transfer for uncertain capacitated arc routing problem." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3321988.
Повний текст джерелаArdeh, Mazhar Ansari, Yi Mei, and Mengjie Zhang. "A novel multi-task genetic programming approach to uncertain capacitated Arc routing problem." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459322.
Повний текст джерелаWang, Shaolin, Yi Mei, John Park, and Mengjie Zhang. "A Two-Stage Genetic Programming Hyper-Heuristic for Uncertain Capacitated Arc Routing Problem." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002912.
Повний текст джерелаArdeh, Mazhar Ansari, Yi Mei, and Mengjie Zhang. "A GPHH with Surrogate-assisted Knowledge Transfer for Uncertain Capacitated Arc Routing Problem." In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308398.
Повний текст джерела