Método híbrido para optimizar el Flexible Job Shop Scheduling Problem

Palabras clave: • Flexible Job Shop Scheduling Problem, algoritmos genéticos, escalada de colina, optimización híbrida

Resumen

Este artículo aborda la programación de tareas en el Flexible Job Shop Scheduling Problem (FJSSP). En este sistema de manufactura es necesario incrementar el número de trabajos a procesar debido a las condiciones actuales del sector industrial en donde existe un aumento en la demanda de productos, lo que conlleva a incrementar la producción. Para encontrar una programación de tareas cercana al óptimo. Se propone un método de optimización híbrida utilizando una búsqueda global basada en algoritmos genéticos (AG) que tienen buena diversificación y para la búsqueda local se aplica una escalada de colinas simple con reinicio (ECR) para mejorar cada solución. La combinación de estas metaheurísticas obtiene el equilibrio necesario para encontrar la mejor programación de tareas con el fin de minimizar el makespan como función costo. Se implementó el algoritmo propuesto en Matlab, para comprobar su eficiencia se compararon los resultados con investigaciones recientemente publicadas.

Descargas

La descarga de datos todavía no está disponible.

Citas

Aarts, E. H., & Korst, J. H. (1989). Simulated Annealing and Boltzmann Machines.

Adiri, I., Bruno, J., Frostig, E., & Rinnooy Kan, A. (1989). Single machine flow-time scheduling with a single breakdown. (Springer-Verlag, Ed.) 26(7), 679–696.

Alzaqebah, M., Jawarneh, S., Alwohaibi, M., Alsmadi, M. K., Almarashdeh, I., & Mohammad, R. M. (2020). Hybrid Brain Storm Optimization algorithm and Late Acceptance Hill Climbing to solve the Flexible Job-Shop Scheduling Problem. Journal of King Saud University – Computer and Information Sciences, 1-12.

Amjad, M. K., Butt, S. I., Kousar, R., Riaz, A., Agha, M., Faping, Z., . . . Asghe, U. (2018). Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems. Mathematical Problems en Engineering, 32.

Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of operations research, 41(3), 157-183.

Brucker, P., & Schlie, R. (1990). Job-shop scheduling with multi-purpose machines. Computing, 45(4), 369-375.

Chen, R., Gen, M., & Tsujimura, Y. (1996). A tutorial Survey of job-shop scheduling. Elsevier Science Ltd, 983-997.

Dai, M., Tang, D., Giret, A., & Salido, M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59, 143-157.

Defersha, F. M., & Rooyani, D. (2020). An efficient two-stage genetic algorithm for a flexible job-shop scheduling problem with sequence dependent attached/detached setup, machine release date and lag-time. Computers & Industrial Engineering, 147, 19.

Deng, Q., Gong, G., Gong, X., Zhang, L., Liu, W., & Ren, Q. (2017). A bee evolutionary guiding nondominated sorting genetic algorithm II for multiobjetive flexible job shop scheduling. 1-20.

Ding, H., & Gu, X. (2020). Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem. Neurocomputing, 414, 313-332.

Dong, X., Chen, P., Huang, H., & Nowak, M. (2013). A multi restart iterated local search algorthm for the permutation flow shop problem minimizing total flow time. Computers & Operations Research, 627-632.

Dorigo, M. (1992). Optimization, Learning and Natural Algorithms,. PhD thesis. Italie.

Fisher, H., & Thompson, G. (1963). Probabilistic learning combinations of local job-shop scheduling rules. Englewood Cliffs, 225-51.

Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). rtificial Intelligence through Simulated Evolution. New York, USA: AWiley.

Gao, L., Peng, C., Zhou, C., & Li, P. (2016.). Solving flexible job shop scheduling problem using general particle swarm optimization. In:Proceedings of the 36th CIE Conference on Computers and Industrial Engineering, (págs. pp. 3018-3027).

Garey, M., Johnson, D., & Sethi, R. (1976). The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research, 1(2), 117-129.

Glover, F. (1989). Tabu seach - Part I. ORSA Journal on Computing 1, 190-206.

Goldberg, D. E. (1989). Genetic algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.

Holland, H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, 208.

Hurink, E., Jurisch, B., & Thole, M. (1994). Tabu search for the job shop scheduling problem with multi-purpose machine. Operations Research Spektrum 1, 15, 205–215.

Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. Purdue School of Engineering and Technology Indianapolis.

Kern, W. (1989). A probabilistic analysis of the switching algorithm for the Euclidean TSP. Mathematical Programming, 44, 213-219.

Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Mitt.

Lawrence, S. (1984). esource constrained project scheduling: an experimental investigation of heuristic scheduling techniques (Supplement). Pittsburgh, Pennsylvania, Carnegie-Mellon University: Graduate School of Industrial Administration.

Li, X., & Gao, L. (2016). An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int. J. Production Economics, 174, 93-110.

Mailing, G. (2003). Algortitmos heurísticos y el problema de job shop scheduling. Buenos Aires: Facultad de Ciencias Exactos y Naturales.

Mastrolilli, M., & Gambardella, L. (2000). Effective neighbourhood functions for the flexible job shop problem. Journal of Scheduling, 3(1), 3-20.

Michallet, J., Prins, C., Amodeo, L., Yalaoui, F., & Vitry, G. (2014). Multi- Start iterated local search for the periodic vehicle routing problem with time windows and time spread constraints on services. Computers & Operacions Research, 41, 196-207.

Rechenberg, I. (1973). Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann-Holzboog, Stuttgart, Germany.

Rodriguez, K. E., de Aguilar, A. G., & Hideaki, T. R. (2018). A new approach to solve the flexible job shop problem based on a hybrid particle swarm optimization and Random- Restart Hill Climbing. Computers and Industrial Engineering, 178-189.

Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2016). A genetic algorithm for energy- efficiency in job-shop scheduling. Springer- Verlag London, 85, 1303-1314.

Shen, L., Dauzère-Pérès, S., & Neufeld, J. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 503-516.

Sreekara, M., Ratnam, C., Rajyalakshmi, G., & Manupati, V. (2018). An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem. Measurement, 114, 78-90.

Xia, W., & Wu, Z. (m de 2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48(2), 409-425.

Xie, N., & Chen, N. (2018). Flexible job shop scheduling problem with interval grey processingtime. Applied Soft Computing, 70, 513–524.

Yang, Y., Huang, M., Yu, Z., & Bing, Q. (2020). Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert Systems with Applications, 158, 1-12.

Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering(56), 1309–1318.

Zobolas, G. I., Tarantilis, C., & Ioannou, G. (2008). Exact, heuristic and meta-heuristic algorithms for solving shop scheduling problems. En F. Xhafa, & A. Abraham, Metaheuristics for Scheduling in Industrial and Manufacturing Applications (Vol. 128, págs. 1-40). Studies in Computational Intelligence.

Publicado
2022-06-24
Cómo citar
Escamilla-Serna, N. J., Seck-Tuoh-Mora, J. C., Medina-Marín, J., Barragan-Vite, I., & Corona-Armenta, J. R. (2022). Método híbrido para optimizar el Flexible Job Shop Scheduling Problem. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 10(Especial2), 56-64. https://doi.org/10.29057/icbi.v10iEspecial2.8651
Tipo de manuscrito
Artículos de investigación

Artículos más leídos del mismo autor/a