Coto Palacio, JessicaMéndez Hernández, Beatriz MaríaMartínez Jiménez, YailenNowé, AnnRodríguez Bazan, Erick D.2018-07-172018-07-172018-03-23Citar según la fuente original: 1. Zhang W. Reinforcement Learning for JobShop Scheduling. Architecture. Oregon State University; p. 190; 1996. 2. Herroelen W, Leus R. Project scheduling under uncertainty: Survey and research potentials. Eur. J. Oper. Res. 165(2): 289–306; 2005. 3. Graham RL, Lawler EL, Lenstra JK, Rinnooy Kan AHG. Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discret. Math.5:287–326; 1979. 4. Martinez Jiménez Y. A Generic Multi-Agent Reinforcement Learning Approach for Scheduling Problems. PhD, Vrije Universiteit Brussel. p. 128; 2012. 5. Sivasankaran P, Sornakumar T, Panneerselvam R. Efficient Heuristic to Minimize Makespan in Single Machine Scheduling Problem with Unrelated Parallel Machines. Intelligent Information Management. March 2010:188–98; 2010. 6. Nhu Binh HO, Joc Cing TAY. Evolving Dispatching Rules for solving the Flexible Job-Shop Problem. IEEE Congr. Evol. Comput. p. 2848–55. 2005.. 7. Van De Velde S. L. Duality-based algorithms for scheduling unrelated parallel machines. ORSA Journal of Computing. Vol 5:182–205; 1993. 8. Glass C. A., Potts C. N. , Shade P. Unrelated parallel machine scheduling using local search. Mathematical and Computer Modeling. Vol 20:41–52; 1994. 9. Sourd F. Scheduling tasks on unrelated machines: Large neighbourhood improvement procedures. Journal of Heuristics. Vol 7:519–31; 2001. . 10. Alharkan I. M. Algorithms for Sequencing and Scheduling. Industrial Engineering Department, King Saud University, 2010. 11. Al-Turki U, Andijani A, Arifulsalam S. A New Dispatching Rule for the Stochastic Single-Machine Scheduling Problem. SIMULATION: Transactions of The Society for Modeling and Simulation International. Vol 80:165–70; 2014. 12. Monien B. , Woclaw A. Scheduling unrelated parallel machines computational results. Experimental Algorithms, Springer, Berlin / Heidelberg. Vol 4007:195–206; 2006. 13. Gairing M, Monien B, Woclaw A. A faster combinatorial approximation algorithm for scheduling unrelated machines. Theoretical Computer Science. Vol 380:87–99; 2007. 14. Ghirardi M, Potts C. N. Makespan minimization for scheduling unrelated parallel machines: A recovering beam search approach. European Journal of Operations Research. Vol 165:457–67; 2005. 15. Hariri A. M. A, Potts C. N.: Heuristics for scheduling unrelated parallel machines. Computer and Operations Research. Vol 18:323–31; 1991. 16. Mokotoff E, Chretienne P. A cutting plane algorithm for the unrelated parallel machine-scheduling problem. European Journal of Operational Research. Vol 141:515–25; 2002. 17. Mokotoff E, Jimeno J. L.:Heuristics based on partial enumeration for the unrelated parallel processor scheduling problem. Annals of Operations Research. Vol 117:133–50; 2002. 18. Murata T, Gen M. Performance Evaluation of Solution-Based GA and Rule-Based GA for Scheduling Problems. Annals of Operations Research. 2000. . 19. Piersman N, Van Dijk W. A local search heuristic for unrelated parallel machine scheduling with efficient neighbourhood search. Mathematical and Computer Modeling. Vol 24:11–9; 1996. 20. Shahzad A, Mebarki N. Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation. Computers [Internet]. Available from: www.mdpi.com/journal/omputers; 2016. 21. Šori K, Vojvodić Rosenzweig V. SGP heuristics for one machine scheduling problem. Proceedings of 7th International Symposium on Operations Research in Slovenia (SOR 2003) p: 1–6, 2003. 22. Zahmani M. H, Atmani B. Bekrar A. Efficient dispatching rules based on data mining for the single machine-scheduling problem. Computer Science & Information Technology (CS & IT). 199–208; 2015.978 959 7255 00-0https://dspace.uclv.edu.cu/handle/123456789/9659La secuenciación de trabajos es un área muy amplia en la cual muchos investigadores se han enfocado en los últimos años. En las empresas generalmente esta planificación se realiza de forma manual o semiautomática. Este trabajo propone un algoritmo para la secuenciación de trabajos en máquinas paralelas no relacionadas. El algoritmo utiliza dos variantes de solución: una heurística simple basada en una generación pseudoaleatoria y una regla de despacho basada en la máquina que más tiempo de procesamiento tiene pendiente. Para analizar el desempeño de las mismas se utiliza un caso de estudio donde los resultados obtenidos demuestran que la regla de despacho proporciona mejores resultados, conclusión que fue validada mediante pruebas estadísticas.Scheduling is a wide research area in which many researchers have focused in recent years. In companies, this planning is usually done manually or semi-automatically. This work proposes an algorithm for the job sequencing in parallel unrelated machines. The algorithm uses two solution alter natives: a simple heuristic based on a pseudo-random generation and a dispatching rule based on the machine with most work remaining. To analyze the performance of the alternatives a study case is used, where the results obtained show that the dispatching rule provides better results, a conclusion that was validated using statistical tests.esEste documento es Propiedad Patrimonial del Sello editorial InfoTIC y se socializa en este Repositorio gracias a la política de acceso abierto de la XVII Convención y Feria Internacional Informática 2018Secuenciación de ReportesMáquinas Paralelas no RelacionadasHeurísticasReglas de DespachoReport SchedulingUnrelated Parallel MachinesHeuristicsDispatching RulesAlgoritmo para la optimización del proceso de secuenciación de reportesProceedingsMinisterio de Comunicaciones y la Unión de Informáticos de Cuba