Abstract
Industrial planning has experienced notable advancements since its beginning by the middle of the 20th century. The importance of its application within the several industries where it is used has been demonstrated, regardless of the difficulty of the design of the exact algorithms that solve the variants. Heuristic methods have been applied for planning problems due to their high complexity; especially Artificial Intelligence when developing new strategies to solve one of the most important variants called task scheduling. It is possible to define task scheduling as: .a set of N production line tasks and M machines, which can execute those tasks, where the goal is to find an execution order that minimizes the accumulated execution time, known as makespan. This paper presents a GRASP meta heuristic strategy for the problem of scheduling dependent tasks in different machines
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References
G. Miller, E. Galanter. Plans and the Structure of Behavior. Holt Editorial, 1960.
M. Drozdowski. Scheduling multiprocessor tasks: An overview. European Journal Operation Research, 1996, pp. 215–230.
R. Campello, N. Maculan. Algorithms e Heurísticas: desenvolvimiento e avaliaçao de performance. Apolo Nacional Editores. Brasil, 1992.
M. Pinedo. Scheduling: Theory, Algorithms and Systems, Prentice Hall, 2002.
T. Feo, M. Resende, Greedy Randomized Adaptive Search Procedure. In Journal of Global Optimization, No. 6, 1995, pp. 109–133.
W. Rauch, Aplicaciones de la inteligencia Artificial en la actividad empresarial, la ciencia y la industria-tomo II. Editorial Díaz de Santos. España, 1989.
P. Kumara Artificial Intelligence: Manufacturing theory and practice. NorthCross-Institute of Industrial Engineers, 1988.
A. Blum, M. Furst. Fast Planning through Plan-graph Analysis. In 14th International Joint Conference on Artificial Intelligence. Morgan-Kaufmann Editions, 1995, pp. 1636–1642.
R. Conway. Theory of Scheduling, Addison-Wesley Publishing Company, 1967
M. Tupia. Algoritmo voraz para resolver el problema de la programación de tareas dependientes en máquinas diferentes. In International Conference of Industrial Logistics (7, 2005, Uruguay) ICIIL. Editors. H. Cancela, Montevideo — Uruguay, 2005, p. 345.
M. Tupia., D. Mauricio. Un algoritmo GRASP para resolver el problema de la programación de tareas dependientes en máquinas diferentes. In CLEI (30, 2004, Perú) Editors. M. Solar, D. Fernández-Baca, E. Cuadros-Vargas, Arequipa — Perú, 2004, pp. 129–139.
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Anticona, M.T. (2006). A GRASP algorithm to solve the problem of dependent tasks scheduling in different machines. In: Bramer, M. (eds) Artificial Intelligence in Theory and Practice. IFIP AI 2006. IFIP International Federation for Information Processing, vol 217. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34747-9_34
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DOI: https://doi.org/10.1007/978-0-387-34747-9_34
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