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Academic literature on the topic 'Tabu Search'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Tabu Search"

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Tuson, A., F. Glover, and M. Laguna. "Tabu Search." Journal of the Operational Research Society 50, no. 1 (January 1999): 106. http://dx.doi.org/10.2307/3010402.

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Tuson, A. "Tabu Search." Journal of the Operational Research Society 50, no. 1 (January 1999): 106–7. http://dx.doi.org/10.1057/palgrave.jors.2600048.

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Kallab, Chadi, Samir Haddad, Imad El-Zakhem, Jinane Sayah, Mohamad Chakroun, Nisrine Turkey, Jinan Charafeddine, Hani Hamdan, and Wafaa Shakir. "Generic Tabu Search." Journal of Software Engineering and Applications 15, no. 07 (2022): 262–73. http://dx.doi.org/10.4236/jsea.2022.157016.

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de Werra, D., and A. Hertz. "Tabu search techniques." Operations-Research-Spektrum 11, no. 3 (September 1989): 131–41. http://dx.doi.org/10.1007/bf01720782.

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Stepanenko, Svetlana, and Bernd Engels. "Gradient tabu search." Journal of Computational Chemistry 28, no. 2 (2006): 601–11. http://dx.doi.org/10.1002/jcc.20564.

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Glover, Fred. "Tabu Search: A Tutorial." Interfaces 20, no. 4 (August 1990): 74–94. http://dx.doi.org/10.1287/inte.20.4.74.

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Glover, Fred. "Tabu Search—Part I." ORSA Journal on Computing 1, no. 3 (August 1989): 190–206. http://dx.doi.org/10.1287/ijoc.1.3.190.

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Glover, Fred. "Tabu Search—Part II." ORSA Journal on Computing 2, no. 1 (February 1990): 4–32. http://dx.doi.org/10.1287/ijoc.2.1.4.

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Battiti, Roberto, and Giampietro Tecchiolli. "The Reactive Tabu Search." ORSA Journal on Computing 6, no. 2 (May 1994): 126–40. http://dx.doi.org/10.1287/ijoc.6.2.126.

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Glover, Fred. "Tabu search—Uncharted domains." Annals of Operations Research 149, no. 1 (December 2, 2006): 89–98. http://dx.doi.org/10.1007/s10479-006-0113-9.

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Dissertations / Theses on the topic "Tabu Search"

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Whittley, Ian Murray. "Tabu search-revisited." Thesis, University of East Anglia, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251720.

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Slomka, Frank. "Mehrkriterienoptimierung verteilter Echtzeitsysteme mit Tabu-Search /." Düssdeldorf : VDI-Verl, 2002. http://www.gbv.de/dms/bs/toc/35430979x.pdf.

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Al-Hamad, Khaled. "Tabu search for ship routing and scheduling." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/5071.

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This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems.
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Cruz, António Manuel Costa. "IMRT beam angle optimization using Tabu search." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/17714.

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Mestrado em Matemática e Aplicações
O número de pacientes com cancro continua a crescer no mundo e a Organização Mundial da Saúde considerou mesmo esta como uma das principais ameaças para a saúde e o desenvolvimento humano. Dependendo da localização e das especi cidades do tumor, existem muitos tratamentos que podem ser usados, incluindo cirurgia, quimioterapia, imunoterapia e radioterapia. A Radioterapia de Intensidade Modulada (IMRT | Intensity Modulated Radiation Therapy) é uma das modalidades mais avançadas de radioterapia, onde a otimização pode ter um papel importante no que diz respeito à qualidade do tratamento aplicado. Em IMRT, o feixe de radiação pode ser visto como se fosse constituído por vários pequenos feixes, pelo uso de um colimador multifolhas, que permite que a intensidade seja modulada. Este complexo problema de otimização pode ser dividido em três subproblemas, que estão relacionados entre si e que podem ser resolvidos sequencialmente. Para cada paciente, os ângulos de onde a radiação ir a ocorrer têm de ser determinados (problema geométrico | otimização angular). Depois, para cada um desses ângulos, o mapa de intensidades (ou fluências) tem de ser calculado (problema das intensidades | otimização das fluências). Finalmente, e necessário determinar o comportamento do colimador multifolhas, de forma a garantir que as intensidades são, de facto, atribuídas (problema de realiza ção). Em cada um destes problemas de otimização, a qualidade do tratamento atribuído depende dos modelos e algoritmos usados. Neste trabalho, a nossa atenção estará particularmente focada na otimização angular, um problema conhecido por ser altamente não-convexo, com muitos mínimos locais e com uma função objetivo que requer muito tempo de computação para ser calculada. Tal significa, respetivamente, que os algoritmos que sejam baseados no cálculo de gradientes ou que requeiram muitas avaliações da função objetivo podem não ser adequados. Assim, os procedimentos metaheurísticos podem ser uma boa alternativa para abordar este problema, visto que são capazes de escapar de mínimos locais e são conhecidos por conseguirem calcular boas soluções em problemas complexos. Neste trabalho ser a descrita uma aplicação para Pesquisa Tabu. Serão ainda apresentados os testes computacionais realizados, considerando dez casos clínicos de pacientes previamente tratados por radioterapia, pretendendo-se mostrar que a Pesquisa Tabu e capaz de melhorar os resultados obtidos através da solução equidistante, cujo uso e comum na prática clínica.
The number of cancer patients continues to grow worldwide and the World Health Organization has even considered cancer as one of the main threats to human health and development. Depending on the location and speci cities of the tumor, there are many treatments that can be used, including surgery, chemotherapy, immunotherapy and radiation therapy. Intensity Modulated Radiation Therapy (IMRT) is one of the most advanced radiation therapy modalities, and optimization can have a key role in the quality of the treatment delivered. In IMRT, the radiation beam can be thought of as being composed by several small beams, through the use of a multileaf collimator, allowing radiation intensity to be modulated. This complex optimization problem can be divided in three related subproblems that can be solved sequentially. For each patient, the angles from which the radiation will be delivered have to be determined (geometric problem | beam angle optimization). Then, for each of these angles, the radiation intensity map is calculated ( uence or intensity optimization). Finally, it is necessary to determine the behavior of the multileaf collimator that guarantees that the desired radiation intensities are, indeed, delivered (realization problem). In each of these optimization problems, the quality of the treatment delivered depends on the models and algorithms used. In this work the attention will be focused in beam angle optimization, a problem known to be highly non{convex, with many local minima and with an objective function that is time expensive to calculate, which, respectively, means that algorithms that are gradient{based or that require many objective function evaluations will not be adequate. Metaheuristics can be the right tool to tackle this problem, since they are capable of escaping local minima and are known to be able to calculate good solutions for complex problems. In this work, an application of Tabu Search to beam angle optimization is described. Computational results considering ten clinical cases of head{and{neck cancer patients are presented, showing that Tabu Search is capable of improving the equidistant solution usually used in clinical practice.
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Stepanenko, Svetlana. "Global Optimization Methods based on Tabu Search." Doctoral thesis, kostenfrei, 2008. http://www.opus-bayern.de/uni-wuerzburg/volltexte/2008/3060/.

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SHARMA, VIKAS. "QUANTITATIVE ANALYSIS OF TABU SEARCH ALGORITHM FOR A VLSI PLACEMENT APPLICATION." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990820742.

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Achan, Kannan. "Tabu search in multi-hop optical network design." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0013/MQ52497.pdf.

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Lightner, Carin Ann. "A Tabu Search Approach to Multiple Sequence Alignment." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-05312008-191232/.

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Sequence alignment methods are used to detect and quantify similarities between different DNA and protein sequences that may have evolved from a common ancestor. Effective sequence alignment methodologies also provide insight into the structure function of a sequence and are the first step in constructing evolutionary trees. In this dissertation, we use a tabu search approach to multiple sequence alignment. A tabu search is a heuristic approach that uses adaptive memory features to align multiple sequences. The adaptive memory feature, a tabu list, helps the search process avoid local optimal solutions and explores the solution space in an efficient manner. We develop two main tabu searches that progressively align sequences. A randomly generated bifurcating tree guides the alignment. The objective is to optimize the alignment score computed using either the sum of pairs or parsimony scoring function. The use of a parsimony scoring function provides insight into the homology between sequences in the alignment. We also explore iterative refinement techniques such as a hidden Markov model and an intensification heuristic to further improve the alignment. This approach to multiple sequence alignment provides improved alignments as compared to several other methods.
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Lin, Yu-Min. "Tabu Search and Genetic Algorithm for Phylogeny Inference." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-10092008-235130/.

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Phylogenetics is the study of evolutionary relations between different organisms. Phylogenetic trees are the representations of these relations. Researchers have been working on finding fast and systematic approaches to reconstruct phylogenetic trees from observed data for over 40 years. It has been shown that, given a certain criterion to evaluate each tree, finding the best fitted phylogenetic trees among all possible trees is an NP-hard problem. In this study, we focus on the topology searching techniques for the maximum-parsimony and maximum-likelihood phylogeny inference. We proposed two search methods based on tabu search and genetic algorithms. We first explore the feasibility of using tabu search for finding the maximum-parsimony trees. The performance of the proposed algorithm is evaluated based on its efficiency and accuracy. Then we proposed a hybrid method of the tabu search and genetic algorithm. The experimental results indicate that the hybrid method can provide maximum-parsimony trees with a ggood level of accuracy and efficiency. The hybrid method is also implemented for finding maximum-likelihood trees. The experimental results show that the proposed hybrid method produce better maximum-likelihood trees than the default-setting dnaml program in average on the tested data sets. On a much larger data set, the hybrid method outperforms the default-setting dnaml program and has equally good performance as the dnaml program with the selected jumble option.
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黃頌詩 and Chung-sze Wong. "Tabu search-based techniques for clustering data sets." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B42576556.

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Books on the topic "Tabu Search"

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Manuel, Laguna, ed. Tabu search. Boston: Kluwer Academic Publishers, 1997.

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Glover, Fred, and Manuel Laguna. Tabu Search. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0.

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Lim, C. S. Tabu search and applications. Manchester: UMIST, 1993.

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César, Rego, and Alidaee Bahram, eds. Metaheuristic optimization via memory and evolution: Tabu search and scatter search. Norwell, Mass: Kluwer Academic Publishers, 2005.

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Basu, Sumanta. Implementing tabu search to exploit sparsity in ATSP instances. Ahmedabad: Indian Institute of Management, 2008.

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Basta, T. Studies in distribution planning and operations. Manchester: UMIST, 1993.

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Basu, Sumanta. A review of the tabu search literature on traveling salesman problems. Ahmedabad: Indian Institute of Management, 2008.

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Indian Institute of Management, Ahemdabad., ed. A probabilistic tabu search algorithm for the generalized minimum spanning tree problem. Ahmedabad: Indian Institute of Management, 2003.

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D, Karaboga, ed. Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks. London: Springer London, 2000.

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1965-, Karaboga Dervis, ed. Intelligent optimisation techniques: Genetic algorithms, tabu search, simulated annealing and neural networks. London: Springer, 2000.

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Book chapters on the topic "Tabu Search"

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Glover, Fred, and Manuel Laguna. "Tabu Search Background." In Tabu Search, 1–24. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_1.

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Glover, Fred, and Manuel Laguna. "Neglected Tabu Search Strategies." In Tabu Search, 343–57. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_10.

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Glover, Fred, and Manuel Laguna. "Tabu Search Foundations: Short Term Memory." In Tabu Search, 25–57. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_2.

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Glover, Fred, and Manuel Laguna. "Tabu Search Foundations: Additional Aspects of Short Term Memory." In Tabu Search, 59–91. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_3.

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Glover, Fred, and Manuel Laguna. "Tabu Search Foundations: Longer Term Memory." In Tabu Search, 93–124. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_4.

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Glover, Fred, and Manuel Laguna. "Tabu Search Principles." In Tabu Search, 125–51. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_5.

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Glover, Fred, and Manuel Laguna. "Tabu Search in Integer Programming." In Tabu Search, 153–226. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_6.

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Glover, Fred, and Manuel Laguna. "Special Tabu Search Topics." In Tabu Search, 227–66. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_7.

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Glover, Fred, and Manuel Laguna. "Tabu Search Applications." In Tabu Search, 267–303. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_8.

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Glover, Fred, and Manuel Laguna. "Connections, Hybrid Approaches and Learning." In Tabu Search, 305–42. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6089-0_9.

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Conference papers on the topic "Tabu Search"

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Halavati, Ramin, Saeed Bagheri Shouraki, Bahareh Jafari Jashmi, and Mojdeh Jalali Heravi. "Symbiotic tabu search." In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277244.

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Janvattanavong, Photjanat, and Pornsiri Muenchaisri. "Software Remodularization Using Tabu Search." In ICICSE '20: 2020 International Conference on Internet Computing for Science and Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3424311.3424321.

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Smaili, Ahmad, and Naji Atallah. "A Tabu-Gradient Search-Based Optimization Technique for Synthesis of Mechanisms." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57420.

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Mechanism synthesis requires the use of optimization methods to obtain approximate solution whenever the desired number of positions the mechanism is required to traverse exceeds a few (five in a 4R linkage). Deterministic gradient-based methods are usually impractical when used alone because they move in the direction of local minima. Random search methods on the other hand have a better chance of converging to a global minimum. This paper presents a tabu-gradient search based method for optimum synthesis of planar mechanisms. Using recency-based short-term memory strategy, tabu-search is initially used to find a solution near global minimum, followed by a gradient search to move the solution ever closer to the global minimum. A brief review of tabu search method is presented. Then, tabu-gradient search algorithm is applied to synthesize a four-bar mechanism for a 10-point path generation with prescribed timing task. As expected, Tabu-gradient base search resulted in a better solution with less number of iterations and shorter run-time.
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Zhao, Hui, Hang Long, and Wenbo Wang. "Tabu Search Detection for MIMO Systems." In 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2007. http://dx.doi.org/10.1109/pimrc.2007.4394688.

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Abdelkafi, Omar, Lhassane Idoumghar, and Julien Lepagnot. "Distributed Multistart Hybrid Iterative Tabu Search." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.342.

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Adamczewski, Kamil, Yumin Suh, and Kyoung Mu Lee. "Discrete Tabu Search for Graph Matching." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.21.

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NG, MICHAEL K. "CLUSTERING PROBLEMS USING TABU SEARCH TECHNIQUES." In Proceedings of the International Conference on Inverse Problems. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704924_0034.

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Dyce, W., N. Rodriguez, B. Lange, S. Andary, and A. Seilles. "Tabu search for human pose recognition." In IS&T/SPIE Electronic Imaging, edited by Atilla M. Baskurt and Robert Sitnik. SPIE, 2014. http://dx.doi.org/10.1117/12.2040563.

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Kargahi, M., and J. C. Anderson. "Structural Weight Optimization with Tabu Search." In 10th Biennial International Conference on Engineering, Construction, and Operations in Challenging Environments and Second NASA/ARO/ASCE Workshop on Granular Materials in Lunar and Martian Exploration. Reston, VA: American Society of Civil Engineers, 2006. http://dx.doi.org/10.1061/40830(188)163.

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Kokubo, Tatsuya, and Yoshikazu Fukuyama. "Train crew scheduling using tabu search." In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, 2017. http://dx.doi.org/10.23919/sice.2017.8105640.

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Reports on the topic "Tabu Search"

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O'Rourke, Kevin P., T. G. Bailey, Raymond Hill, and William B. Carlton. Dynamic Routing of Unmanned Aerial Vehicles Using Reactive Tabu Search. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada380543.

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Çavuşlar, Gizem, Bülent Çatay, and Serkan Mehmet Apaydın. A tabu search approach for the NMR protein structure-based assignment problem. Sabancı University, December 2011. http://dx.doi.org/10.5900/su_fens_wp.2011.18983.

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Barnes, J. W. Using Advanced Tabu Search Approaches to Perform Enhanced Air Mobility Command Operational Airlift Analyses. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada585803.

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Barnes, J. W. Using Advanced Tabu Search Approaches to Perform Enhanced Air Mobility Command Operational Airlift Analyses - Phases II and III. Fort Belvoir, VA: Defense Technical Information Center, October 2006. http://dx.doi.org/10.21236/ada463631.

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Jessop, Colin P. Search for Neutrinoless tau Decays: tau -> e gamma and tau -> mu gamma. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/813113.

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Aubert, B., R. Barate, D. Boutigny, F. Couderc, Y. Karyotakis, J. P. Lees, V. Poireau, et al. A Search for the Rare Decay B0 to tau+tau- atBaBar. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/878014.

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Jessop, Colin P. Search for phi Mesons in tau Lepton Decay. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/813131.

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Potter, Christopher Thomas, and /Oregon U. A Search for the Rare Decay B->tau+tau- at the BaBar Experiment. US: Stanford Linear Accelerator Center (SLAC), March 2006. http://dx.doi.org/10.2172/877222.

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Kolb, Jeffrey A. A Search for Neutrinoless Tau Decays to Three Leptons. Office of Scientific and Technical Information (OSTI), June 2008. http://dx.doi.org/10.2172/938633.

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Aubert, B. Search for the Decay $\tau^- \rightarrow 3\pi^- 2\pi^+2\pi^0 \nu_\tau$. Office of Scientific and Technical Information (OSTI), April 2006. http://dx.doi.org/10.2172/881120.

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