Academic literature on the topic 'Heuristics methods'
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Journal articles on the topic "Heuristics methods"
Wilt, Christopher, and Wheeler Ruml. "Effective Heuristics for Suboptimal Best-First Search." Journal of Artificial Intelligence Research 57 (October 31, 2016): 273–306. http://dx.doi.org/10.1613/jair.5036.
Full textCichowicz, T., M. Drozdowski, M. Frankiewicz, G. Pawlak, F. Rytwinski, and J. Wasilewski. "Hyper-heuristics for cross-domain search." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 4 (December 1, 2012): 801–8. http://dx.doi.org/10.2478/v10175-012-0093-7.
Full textAbdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.
Full textShanklin, Roslyn, Philip Kortum, and Claudia Ziegler Acemyan. "Adaptation of Heuristic Evaluations for the Physical Environment." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1135–39. http://dx.doi.org/10.1177/1071181320641272.
Full textTriki, C. "Solution Methods for the Periodic Petrol Station Replenishment Problem." Journal of Engineering Research [TJER] 10, no. 2 (December 1, 2013): 69. http://dx.doi.org/10.24200/tjer.vol10iss2pp69-77.
Full textGonzalez-Holland, Emily, Daphne Whitmer, Larry Moralez, and Mustapha Mouloua. "Examination of the Use of Nielsen’s 10 Usability Heuristics & Outlooks for the Future." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1472–75. http://dx.doi.org/10.1177/1541931213601853.
Full textCHANG, KAI H., JAMES H. CROSS II, W. HOMER CARLISLE, and SHIH-SUNG LIAO. "A PERFORMANCE EVALUATION OF HEURISTICS-BASED TEST CASE GENERATION METHODS FOR SOFTWARE BRANCH COVERAGE." International Journal of Software Engineering and Knowledge Engineering 06, no. 04 (December 1996): 585–608. http://dx.doi.org/10.1142/s0218194096000247.
Full textПарфентьєва І. П. and Матвійчук К. О. "ЕТИМОЛОГІЯ ПОНЯТТЯ “ЕВРИСТИЧНИЙ ПІДХІД”." World Science 3, no. 8(36) (August 30, 2018): 46–49. http://dx.doi.org/10.31435/rsglobal_ws/30082018/6078.
Full textSoria-Alcaraz, Jorge A., Gabriela Ochoa, Andres Espinal, Marco A. Sotelo-Figueroa, Manuel Ornelas-Rodriguez, and Horacio Rostro-Gonzalez. "A Methodology for Classifying Search Operators as Intensification or Diversification Heuristics." Complexity 2020 (February 13, 2020): 1–10. http://dx.doi.org/10.1155/2020/2871835.
Full textWest, Todd, John Sessions, and Bogdan M. Strimbu. "Heuristic Optimization of Thinning Individual Douglas-Fir." Forests 12, no. 3 (February 28, 2021): 280. http://dx.doi.org/10.3390/f12030280.
Full textDissertations / Theses on the topic "Heuristics methods"
PINHEIRO, SAULO BORGES. "CONTAINERS ROAD TRANSPORTATION OPTIMIZATION: EXACT AND HEURISTICS METHODS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34991@1.
Full textDespite the Brazilian continental scale, the magnitude of its coastline and the proximity between the coast and the large urban centers, the transport of cargo in containers using cabotage is still very limited in Brazil. In this scenario, the Brazilian cabotage ship-owners seek to provide door-to-door services, achieving economies of scale in procurement for suppliers that perform road ends, thus increasing the competitiveness of cabotage with its main competitor, the transportation by trucks. This work presents two models that aim to minimize the total cost of hiring road suppliers to a list of demands that must be performed. The first is a mathematical model based on integer linear programming, the second is an algorithm that uses a greedy heuristic. The models were developed and tested in real scenarios, experienced by a Brazilian cabotage ship-owner for a period of time. The results of the two models, which are compared among each other and with the manually solutions performed by the company’s employees, show that the solutions of optimization models are much better than the manual solutions. The results also show that the greedy algorithm achieves very close results to the exact method, proving to be very useful given the ease of its implementation.
Panthulu, Pradeep. "Intelligent Memory Management Heuristics." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4399/.
Full textFloriani, Lauro. "Multivariate statistical methods for studying the mechanics of heuristics." Nice, 2002. http://www.theses.fr/2002NICE5705.
Full textAlgorithms are the most effective knowledge repositories yet conceived by mankind for they automate computations on a given formal medium, the computer system. NP-Completeness tell us that among the computable treatable problems there are some which we do not know an efficient way of dealing with, i. E. We do not know how to conceive efficient algorithms for them. Nevertheless, practical experience tells us that many algorithms for these problems are indeed quite effective. That is to say that the experimental analysis of their input/output relationship reveal a far better situation than what can be derived by the mathematical analysis of these algorithms' properties. It also means that the dynamic process induced by the algorithm (i. E. The computational process) exceeds our capacity of express and communicate the knowledge we embed in such algorithm. Without the actual implementation of the algorithm we would not be able to tell their behavior. The work in this thesis concerns such dynamic process. Rather than analyze the properties of the knowledge we represent on an algorithm, we want to investigate the mechanics of the computational process in order to infer the knowledge behind it. That is what we call exploratory studies, for we want to acquire new knowledge about the treatment given by our algorithm to the problem. We approach this matter by a data analysis perspective. Our method consists on probing the computational process in search for insightful statistical evidences about its mechanics. In order to guarantee the insightful aspect of these evidences it is essential that they report on a pertinent computational process, i. E. For large input problems. Data analysis is therefore a crucial issue in such kind of study. In this work we present a systematic application of Multivariate Descriptive Statistical Analysis techniques to raise statistical evidences on the mechanics of the computational process induced by heuristic algorithms for hard optimization problems. We present a case study of a state of the art heuristic for the Traveling Salesman Problem where we interpret and discuss the evidences raised by our studies, showing their ability to provide us new knowledge about the heuristic. The practical motivation behind this work is the optimization context defined by the design of wireless telecommunications networks. Such a peculiar context poses severe constraints to the analysis of algorithms even by traditional experimentation
Amir-Hussin, Amir A. B. "Heuristic methods for coalition structure generation." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/26275.
Full textRemde, Stephen Mark. "Enhancing the performance of search heuristics : variable fitness functions and other methods to enhance heuristics for dynamic workforce scheduling." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4310.
Full textRemde, Stephen M. "Enhancing the Performance of Search Heuristics. Variable Fitness Functions and other Methods to Enhance Heuristics for Dynamic Workforce Scheduling." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4310.
Full textKim, Dong-Gook. "An Analysis of Ecological and Social Rationality: When are Lexicographic Heuristics Preferred?" unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-08182008-212550/.
Full textTitle from file title page. Thomas Whalen, committee chair; Julian Diaz III, C. S. Thachenkary, Rodney Schultz, committee members. Electronic text (168 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed November 3, 2008. Includes bibliographical references (p. 164-168).
Machac, Mary Kristin. "A Model of Expert Instructional Design Heuristics Incorporating Design Thinking Methods." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102926.
Full textDoctor of Philosophy
Instructional design is a system of procedures for developing education and training curricula in a consistent and reliable fashion (Branch and Merrill, 2011; Branch and Kopcha, 2014). It embodies an iterative process for outlining outcomes, selecting teaching and learning strategies, choosing support technologies, identifying media, and measuring performance (Branch and Kopcha, 2014). Instructional designers use models of instructional design and instructional development to communicate tasks and procedures of the instructional design process (Andrews and Goodson, 1980). Over the years, numerous models of instructional design have been developed and adapted to meet the varying needs of instructional designers and developers. There is a consensus that most instructional processes consist of five core elements or stages: analysis, design, development, implementation, and evaluation, which are commonly referred to as ADDIE (Seels and Glasgow, 1990; Branch and Kopcha, 2014). While often considered generic, the ADDIE framework contains a useful set of common criteria, which most designers state as important or necessary as a part of any instructional design process (Pittenger, Janke, and Bumgardner, 2009; York and Ertmer, 2011; 2016). Novice instructional designers have limited experience working with ill-structured problems, and often do not possess the mental models (prior experience) to effectively analyze, manage, and communicate the overall design process of new instructional design projects (Wedman and Tessmer, 1993; Rowland, 1992; Perez and Emery, 1995; Liu, Gibby, Quiros, and Demps, 2002). In their 2016 article of expert instructional design principles applied by experienced designers in practice, York and Ertmer proposed the following questions for future research, "(a) Can we teach principles to novice instructional designers? (b) What methods should we use to provide this information?" (York and Ertmer, 2016, p. 189). This research further explored these questions and offers a new model of expert instructional design heuristics incorporating design thinking methods. For this study, heuristics were defined as generalized stages of an instructional designer's process and design thinking was defined as a human-centered design process for solving complex problems. The purpose of this study was to identify design thinking methods that aligned with heuristics of expert instructional design practitioners, and to design and develop a new model of heuristics and design thinking methods, which could assist novice instructional designers as they enter the instructional design field. The literature outlines challenges reported among novice instructional designers throughout the instructional design process, which includes their ability to solve ill-structured problems; conduct thorough analyses; collaborate in teams; negotiate priorities; generate a variety of ideas for solutions; overcome resource, budget and time constraints; communicate and manage projects with stakeholders; and prototype, iterate and pilot new design solutions (Rowland, 1992; Hoard, Stefaniak, Baaki, and Draper, 2019; Roytek, 2010; Liu, Gibby, Quiros, and Demps, 2002; Chang and Kuwata, 2020; Tracey and Boling, 2014; Perez and Emery, 1995; Williams van Rooij, 1993). The model offers novice instructional designers specific methods and combinations of methods to use for every stage of the instructional design process. As instructional designers implement design thinking methods within the context of their daily situations, they should become more comfortable and begin to adapt the methods to meet their individual needs for each stage of their process.
Ozdemir, Ersin. "Evolutionary methods for the design of digital electronic circuits and systems." Thesis, Cardiff University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326874.
Full textSucu, Seda. "Solving crew scheduling problem in offshore supply vessels : heuristics and decomposition methods." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=29270.
Full textBooks on the topic "Heuristics methods"
Rabadi, Ghaith, ed. Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26024-2.
Full textLewis, John N. Expert systems development utilizing heuristic methods. Monterey, Calif: Naval Postgraduate School, 1996.
Find full textMartí, Rafael, and Gerhard Reinelt. Exact and Heuristic Methods in Combinatorial Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64877-3.
Full textEggen, Loren G. Approximating the chromatic number of an arbitrary graph using a supergraph heuristic. Monterey, Calif: Naval Postgraduate School, 1997.
Find full textMahtani, Pravesh. Heuristic methods for backup lightpath routing in WDM networks. Ottawa: National Library of Canada, 2001.
Find full textRoos, N. TUD Faculty of Technical Mathematics and Informatics: Heuristic reasoning. Amsterdam, Netherlands: National Aerospace Laboratory, 1988.
Find full textNicol, David M. A multistage linear array assignment problem. Hampton, Va: ICASE, 1988.
Find full textNagy, Gabor. Heuristic methods for the many-to-many location-routing problem. Birmingham: University of Birmingham, 1996.
Find full textLebak, James M. Toward a portable parallel library for space-time adaptive methods. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.
Find full textGerhard, Reinelt, and SpringerLink (Online service), eds. The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.
Find full textBook chapters on the topic "Heuristics methods"
Li, PhD, Haksun. "Heuristics." In Numerical Methods Using Java, 625–54. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-6797-4_11.
Full textMartí, Rafael, Jose A. Lozano, Alexander Mendiburu, and Leticia Hernando. "Multi-start Methods." In Handbook of Heuristics, 155–75. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07124-4_1.
Full textParsopoulos, Konstantinos E. "Particle Swarm Methods." In Handbook of Heuristics, 639–85. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07124-4_22.
Full textMartí, Rafael, Jose A. Lozano, Alexander Mendiburu, and Leticia Hernando. "Multi-start Methods." In Handbook of Heuristics, 1–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-07153-4_1-1.
Full textParsopoulos, Konstantinos E. "Particle Swarm Methods." In Handbook of Heuristics, 1–47. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-07153-4_22-1.
Full textMartí, Rafael, and Gerhard Reinelt. "Meta-Heuristics." In Exact and Heuristic Methods in Combinatorial Optimization, 59–123. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-64877-3_3.
Full textWright, Mike B., and Richard C. Marett. "A Preliminary Investigation into the Performance of Heuristic Search Methods Applied to Compound Combinatorial Problems." In Meta-Heuristics, 299–317. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1361-8_19.
Full textLowndes, Val, Ovidiu Bagdasar, and Stuart Berry. "Case Studies: Using Heuristics." In Simulation Foundations, Methods and Applications, 175–97. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55417-4_6.
Full textMichalewicz, Zbigniew, and David B. Fogel. "Traditional Methods — Part 1." In How to Solve It: Modern Heuristics, 55–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07807-5_4.
Full textMichalewicz, Zbigniew, and David B. Fogel. "Traditional Methods — Part 2." In How to Solve It: Modern Heuristics, 87–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-07807-5_5.
Full textConference papers on the topic "Heuristics methods"
"Multiple RNA Interaction - Formulations, Approximations, and Heuristics." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004341402420249.
Full textHu, Shuli, and Nathan R. Sturtevant. "Direction-Optimizing Breadth-First Search with External Memory Storage." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/175.
Full textBerzish, Murphy, Vijay Ganesh, and Yunhui Zheng. "Z3str3: A String Solver with Theory-aware Heuristics." In 2017 Formal Methods in Computer-Aided Design (FMCAD). IEEE, 2017. http://dx.doi.org/10.23919/fmcad.2017.8102241.
Full textDe Filippo, Allegra, Michele Lombardi, and Michela Milano. "Methods for off-line/on-line optimization under uncertainty." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/177.
Full textTromp, Nynke, Reint Jan Renes, and Jaap Daalhuizen. "Heuristics for selecting and using behavioural design methods." In Design Research Society Conference 2018. Design Research Society, 2018. http://dx.doi.org/10.21606/drs.2018.607.
Full textKureychik, Viktor, Vladimir Kureychik, Liliya Kureychik, and Roman Potarusov. "Heuristics methods for solving the block packing problem." In Information Technologies in Science, Management, Social Sphere and Medicine. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/itsmssm-16.2016.62.
Full textGross, Ari D. "Shape from contour methods using object-based heuristics." In Applications in Optical Science and Engineering, edited by Robert A. Melter and Angela Y. Wu. SPIE, 1993. http://dx.doi.org/10.1117/12.142170.
Full textTuisov, Alexander, and Michael Katz. "The Fewer the Merrier: Pruning Preferred Operators with Novelty." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/576.
Full textTkachuk, Anton, and Mykola Tkachuk. "DISPERSION DESIGN OF 1D PERIODIC DISCRETE SYSTEMS USING LOG-DET HEURISTICS." In 8th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Methods in Structural Dynamics and Earthquake Engineering. Athens: Institute of Structural Analysis and Antiseismic Research National Technical University of Athens, 2021. http://dx.doi.org/10.7712/120121.8611.18609.
Full textDe Rango, F., A. F. Santamaria, M. Tropea, and S. Marano. "Meta-Heuristics Methods for a NP-Complete Networking Problem." In 2008 IEEE 68th Vehicular Technology Conference (VTC 2008-Fall). IEEE, 2008. http://dx.doi.org/10.1109/vetecf.2008.279.
Full textReports on the topic "Heuristics methods"
Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.
Full textRotemberg, Julio. A Heuristic Method for Extracting Smooth Trends from Economic Time Series. Cambridge, MA: National Bureau of Economic Research, December 1999. http://dx.doi.org/10.3386/w7439.
Full textEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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