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dc.contributor.authorLópez, Alvaro Cuadrosspa
dc.contributor.authorVillota Rodríguez, José Miguelspa
dc.contributor.authorVelázquez Sánchez, José Deysonspa 00:00:00 00:00:00
dc.description.abstractA veces, después de programar un proyecto, es necesario acortar su duración. Son muchos los factores que obligan a acortar la duración. Algunos factores pueden ser ahorro en costos, puesta en operación anticipada o para evitar riesgos. En este caso, es necesario asignar más recursos a las actividades para acortar su duración mientras se intenta invertir la menor cantidad de dinero posible. El problema de la compensación de tiempo y costo es un problema importante en la programación de proyectos. En este estudio se aborda el problema de la compensación tiempo-costo desde un enfoque discreto y se resuelve utilizando un algoritmo genético no dominado. La aplicación en un proyecto de construcción permitió identificar un frente de Pareto que los gerentes podían usar para la toma de decisiones. Los gerentes pudieron analizar diferentes escenarios para cumplir con la fecha de entrega, los costos y el alcance
dc.description.abstractSometimes after scheduling a project, it is necessary to shorten its duration. There are many factors that force to crash the duration. Some reasons may be saving costs, early commissioning or avoiding potential risks. In this case, it is necessary to allocate more resources to activities to shorten their duration while trying to invest as little money as possible. The time–cost tradeoff problem is one important problem in project scheduling. In this study the time–cost tradeoff problem is aborded considering a discrete approach and it is solved using a non-dominated genetic algorithm. The application in a construction project identified a Pareto front that managers could use for decision making. Managers were able to analyze different scenarios to meet delivery date, costs, and scope.eng
dc.publisherFondo Editorial EIA - Universidad EIAspa
dc.rightsRevista EIA - 2022eng
dc.subjectTime–cost tradeoffeng
dc.subjectMulti-objective problemeng
dc.subjectScheduling projecteng
dc.subjectCompensación tiempo-costospa
dc.subjectAceleración. NSGA-IIspa
dc.subjectProblema multi objetivospa
dc.subjectProgramación de proyectosspa
dc.titleAlgoritmo genético no dominado NSGA-II para la aceleración de programa considerando el problema de compensación discreta tiempo-costo (DTCTP) en un proyecto de construcciónspa
dc.typeArtículo de revistaspa
dc.typeJournal articleeng
dc.relation.referencesAdam, A., Josephson, P.-E. and Lindahl, G. (2015) ‘Implications of Cost Overruns and Time Delays on Major Public Construction Projects’, in Proceedings of the 19th International Symposium on Advancement of Construction Management and Real Estate. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 747–758. doi: 10.1007/978-3-662-46994-1_61. Agdas, D. et al. (2018) ‘Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems’, Journal of Computing in Civil Engineering, 32(1). doi: 10.1061/(ASCE)CP.1943-5487.0000718. Akkan, C., Drexl, A. and Kimms, A. (2005) ‘Network decomposition-based benchmark results for the discrete time-cost tradeoff problem’, European Journal of Operational Research, 165(2), pp. 339–358. doi: 10.1016/j.ejor.2004.04.006. Aminbakhsh, S. and Sonmez, R. (2016) ‘Discrete particle swarm optimization method for the large-scale discrete time-cost trade-off problem’, Expert Systems with Applications, 51, pp. 177–185. doi: 10.1016/j.eswa.2015.12.041. Aminbakhsh, S. and Sonmez, R. (2017) ‘Pareto Front Particle Swarm Optimizer for Discrete Time-Cost Trade-Off Problem’, Journal of Computing in Civil Engineering. doi: 10.1061/(asce)cp.1943-5487.0000606. Anagnostopoulos, K. P. and Kotsikas, L. (2010) ‘Experimental evaluation of simulated annealing algorithms for the time-cost trade-off problem’, Applied Mathematics and Computation. doi: 10.1016/j.amc.2010.05.056. Bettemir, Ö. H. and Talat Birgönül, M. (2017) ‘Network analysis algorithm for the solution of discrete time-cost trade-off problem’, KSCE Journal of Civil Engineering. doi: 10.1007/s12205-016-1615-x. De, P. et al. (1997) ‘Complexity of the Discrete Time-Cost Tradeoff Problem for Project Networks’, Operations Research, 45(2), pp. 302–306. doi: 10.1287/opre.45.2.302. De, P. P. et al. (1995) ‘The discrete time-cost tradeoff problem revisited’, European Journal of Operational Research, 81(2), pp. 225–238. doi: 10.1016/0377-2217(94)00187-H. Deǧirmenci, G. and Azizoǧlu, M. (2013) ‘Branch and bound based solution algorithms for the budget constrained discrete time/cost trade-off problem’, Journal of the Operational Research Society, 64(10), pp. 1474–1484. doi: 10.1057/jors.2012.14. Demeulemeester, E. et al. (1998) ‘New computational results on the discrete time/cost trade-off problem in project networks’, Journal of the Operational Research Society, 49, pp. 1153–1163. doi: 10.1057/palgrave.jors.2600634. Feng, C. W., Liu, L. and Burns, S. A. (1997) ‘Using genetic algorithms to solve construction time-cost trade-off problems’, Journal of Computing in Civil Engineering, 11(3), pp. 184–189. doi: 10.1061/(ASCE)0887-3801(1997)11:3(184). González, M. J. (2013) ‘La Lógica Fuzzy y su Aplicación en la Limitación de Recursos’, p. 93. Gray, C. and Larson, E. (2009) ‘Administración de proyectos’, p. 6. Hadjiconstantinou, E. and Klerides, E. (2010) ‘A new path-based cutting plane approach for the discrete time-cost tradeoff problem’, Computational Management Science, 7(3), pp. 313–336. doi: 10.1007/s10287-009-0115-6. He, Z. et al. (2017) ‘Variable neighbourhood search and tabu search for a discrete time/cost trade-off problem to minimize the maximal cash flow gap’, Computers and Operation Research, 78, pp. 564–577. doi: 10.1016/j.cor.2016.07.013. Hindelang, T. J. and Muth, J. F. (1979) ‘DYNAMIC PROGRAMMING ALGORITHM FOR DECISION CPM NETWORKS.’, Oper Res. doi: 10.1287/opre.27.2.225. Kaveh, A. and Mahdavi, V. R. (2015) ‘Colliding bodies optimization: Extensions and applications’, Colliding Bodies Optimization: Extensions and Applications, pp. 1–284. doi: 10.1007/978-3-319-19659-6. Ke, H., Ma, W. and Chen, X. (2012) ‘Modeling stochastic project time–cost trade-offs with time-dependent activity durations’, Applied Mathematics and Computation, 218(18), pp. 9462–9469. doi: 10.1016/J.AMC.2012.03.035. Li, H., Xu, Z. and Wei, W. (2018) ‘Bi-objective scheduling optimization for discrete time/cost trade-offin projects’, Sustainability (Switzerland), 10(8), p. 2802. doi: 10.3390/su10082802. Liberatore, M. J., Pollack-Johnson, B. and Smith, C. A. (2001) ‘PROJECT MANAGEMENT IN CONSTRUCTION: SOFTWARE USE AND RESEARCH DIRECTIONS’, JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 127, pp. 101–107. doi: 10.1061/(ASCE)0733-9364(2001)127:2(101). Meyer, L. and Shaffer, L. (1965) The Critical-path Method, McGraw-Hill. Mitchell, G. and Klastorin, T. (2007) ‘An effective methodology for the stochastic project compression problem’, IIE Transactions (Institute of Industrial Engineers), 39(10), pp. 957–969. doi: 10.1080/07408170701315347. Mokhtari, H., Baradaran Kazemzadeh, R. and Salmasnia, A. (2011) ‘Time-cost tradeoff analysis in project management: An ant system approach’, IEEE Transactions on Engineering Management, 58(1), pp. 36–43. doi: 10.1109/TEM.2010.2058859. Moussourakis, J. and Haksever, C. (2010) ‘Project Compression with Nonlinear Cost Functions’, (February), pp. 251–260. Robinson, D. R. (1975) ‘DYNAMIC PROGRAMMING SOLUTION TO COST-TIME TRADEOFF FOR CPM.’, Management Science. doi: 10.1287/mnsc.22.2.158. Shahriari, M. (2016) ‘Multi-objective optimization of discrete time–cost tradeoff problem in project networks using non-dominated sorting genetic algorithm’, Journal of Industrial Engineering International, 12(2), pp. 159–169. doi: 10.1007/s40092-016-0148-8. Sonmez, R. and Bettemir, Ö. H. (2012) ‘A hybrid genetic algorithm for the discrete time-cost trade-off problem’, Expert Systems with Applications, 39(13), pp. 11428–11434. doi: 10.1016/j.eswa.2012.04.019. Tareghian, H. R. and Taheri, S. H. (2007) ‘A solution procedure for the discrete time, cost and quality tradeoff problem using electromagnetic scatter search’, Applied Mathematics and Computation, 190(2), pp. 1136–1145. doi: 10.1016/j.amc.2007.01.100. Tavares, L. . (1990) ‘A multi-stage non-deterministic model for project scheduling under resources constraints’, European Journal of Operational Research, 49(1), pp. 92–101. doi: 10.1016/0377-2217(90)90123-S. Vanhoucke, M. (2005) ‘New computational results for the discrete time/cost trade-off problem with time-switch constraints’, European Journal of Operational Research, 165(2), pp. 359–374. doi: 10.1016/j.ejor.2004.04.007. Vanhoucke, M., Demeulemeester, E. and Herroelen, W. (2002) ‘Discrete time/cost trade-offs in project scheduling with time-switch constraints’, Journal of the Operational Research Society, 53(7), pp. 741–751. doi: 10.1057/palgrave.jors.2601351. Wei, H., Su, Z. and Zhang, Y. (2020) ‘Preprocessing the Discrete Time-Cost Tradeoff Problem with Generalized Precedence Relations’. doi: 10.1155/2020/6312198. Wglarz, J. et al. (2011) ‘Project scheduling with finite or infinite number of activity processing modes - A survey’, European Journal of Operational Research, 208(3), pp. 177–205. doi: 10.1016/j.ejor.2010.03.037. Wiest, J. D. (1967) ‘A Heuristic Model for Scheduling Large Projects with Limited Resources’, Management Science, 13(6), p. B-359-B-377. doi: 10.1287/mnsc.13.6.b359. Wood, D. A. (2017) ‘Gas and oil project time-cost-quality tradeoff: Integrated stochastic and fuzzy multi-objective optimization applying a memetic, nondominated, sorting algorithm’, Journal of Natural Gas Science and Engineering. doi: 10.1016/j.jngse.2017.04.033. Yang, Y. et al. (no date) ‘Effect of Schedule Compression on Project Effort’, 2000(Cii). Zheng, D. X. M., Ng, S. T. and Kumaraswamy, M. M. (2005) ‘Applying pareto ranking and niche formation to genetic algorithm-based multiobjective time-cost optimization’, Journal of Construction Engineering and Management, 131(1), pp. 81–91. doi: 10.1061/(ASCE)0733-9364(2005)131:1(81).eng
dc.rights.creativecommonsEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.eng
dc.relation.citationeditionNúm. 38 , Año 2022 : .spa
dc.relation.citationstartpage3827 pp. 1
dc.relation.ispartofjournalRevista EIAspa
dc.title.translatedNon-dominated NSGA-II genetic algorithm for schedule acceleration considering the discrete time-cost compensation problem (DTCTP) in a construction projecteng

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