Busqueda local iterada para resolver problemas de planificación

El diseño de técnicas y algoritmos eficientes que resuelvan adecuadamente problemas complejos de optimización es uno de los campos dentro de la investigación en Informática con mayor repercusión en la actualidad. Por tanto, se han estudiado, diseñado y desarrollado un conjunto heterogéneo de metaheurísticas para resolver importantes problemas de optimización en el campo de la ingeniería. Por tal motivo nuestra propuesta es proporcionar herramientas de software que permitan resolver problemas comunes en la mayoría de las empresas de la región en el sector productivo y en la logística de las mismas.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Busqueda local iterada para resolver problemas de planificación

El diseño de técnicas y algoritmos eficientes que resuelvan adecuadamente problemas complejos de optimización es uno de los campos dentro de la investigación en Informática con mayor repercusión en la actualidad. Por tanto, se han estudiado, diseñado y desarrollado un conjunto heterogéneo de metaheurísticas para resolver importantes problemas de optimización en el campo de la ingeniería. Por tal motivo nuestra propuesta es proporcionar herramientas de software que permitan resolver problemas comunes en la mayoría de las empresas de la región en el sector productivo y en la logística de las mismas.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Busqueda local iterada para resolver problemas de planificación

El diseño de técnicas y algoritmos eficientes que resuelvan adecuadamente problemas complejos de optimización es uno de los campos dentro de la investigación en Informática con mayor repercusión en la actualidad. Por tanto, se han estudiado, diseñado y desarrollado un conjunto heterogéneo de metaheurísticas para resolver importantes problemas de optimización en el campo de la ingeniería. Por tal motivo nuestra propuesta es proporcionar herramientas de software que permitan resolver problemas comunes en la mayoría de las empresas de la región en el sector productivo y en la logística de las mismas.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

A hybrid algorithm for flexible job-shop scheduling problem with setup times

[EN] Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.Azzouz, A.; Ennigrou, M.; Ben Said, L. (2017). A hybrid algorithm for flexible job-shop scheduling problem with setup times. International Journal of Production Management and Engineering. 5(1):23-30. doi:10.4995/ijpme.2017.6618SWORD233051Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. European Journal of Operational Research, 246(2), 345-378. doi:10.1016/j.ejor.2015.04.004Azzouz, A., Ennigrou, M., & Jlifi, B. (2015). Diversifying TS using GA in Multi-agent System for Solving Flexible Job Shop Problem. Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics. doi:10.5220/0005511000940101Azzouz, A., Ennigrou, M., Jlifi, B., & Ghedira, K. (2012). Combining Tabu Search and Genetic Algorithm in a Multi-agent System for Solving Flexible Job Shop Problem. 2012 11th Mexican International Conference on Artificial Intelligence. doi:10.1109/micai.2012.12Bagheri, A., & Zandieh, M. (2011). Bi-criteria flexible job-shop scheduling with sequence-dependent setup times—Variable neighborhood search approach. Journal of Manufacturing Systems, 30(1), 8-15. doi:10.1016/j.jmsy.2011.02.004Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41(3), 157-183. doi:10.1007/bf02023073Cheung, W., & Zhou, H. (2001). Annals of Operations Research, 107(1/4), 65-81. doi:10.1023/a:1014990729837Fattahi, P., Saidi Mehrabad, M., & Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18(3), 331-342. doi:10.1007/s10845-007-0026-8González, M. A., Rodriguez Vela, C., Varela, R. (2013). An efficient memetic algorithm for the flexible job shop with setup times. In Twenty-Third International Conference on Automated, pp. 91-99.Hurink, J., Jurisch, B., & Thole, M. (1994). Tabu search for the job-shop scheduling problem with multi-purpose machines. OR Spektrum, 15(4), 205-215. doi:10.1007/bf01719451Imanipour, N. (2006). Modeling&Solving Flexible Job Shop Problem With Sequence Dependent Setup Times. 2006 International Conference on Service Systems and Service Management. doi:10.1109/icsssm.2006.320680KIM, S. C., & BOBROWSKI, P. M. (1994). Impact of sequence-dependent setup time on job shop scheduling performance. International Journal of Production Research, 32(7), 1503-1520. doi:10.1080/00207549408957019Moghaddas, R., Houshmand, M. (2008). Job-shop scheduling problem with sequence dependent setup times. Proceedings of the International MultiConference of Engineers and Computer Scientists,2, 978-988.Mousakhani, M. (2013). Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness. International Journal of Production Research, 51(12), 3476-3487. doi:10.1080/00207543.2012.746480Naderi, B., Zandieh, M., & Fatemi Ghomi, S. M. T. (2008). Scheduling sequence-dependent setup time job shops with preventive maintenance. The International Journal of Advanced Manufacturing Technology, 43(1-2), 170-181. doi:10.1007/s00170-008-1693-0Najid, N. M., Dauzere-Peres, S., & Zaidat, A. (s. f.). A modified simulated annealing method for flexible job shop scheduling problem. IEEE International Conference on Systems, Man and Cybernetics. doi:10.1109/icsmc.2002.1176334Nouiri, M., Bekrar, A., Jemai, A., Niar, S., & Ammari, A. C. (2015). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. Journal of Intelligent Manufacturing, 29(3), 603-615. doi:10.1007/s10845-015-1039-3Oddi, A., Rasconi, R., Cesta, A., & Smith, S. (2011). Applying iterative flattening search to the job shop scheduling problem with alternative resources and sequence dependent setup times. In COPLAS 2011 Proceedings of the Workshopon Constraint Satisfaction Techniques for Planning and Scheduling Problems, pp. 15-22.Pezzella, F., Morganti, G., & Ciaschetti, G. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35(10), 3202-3212. doi:10.1016/j.cor.2007.02.014Sadrzadeh, A. (2013). Development of Both the AIS and PSO for Solving the Flexible Job Shop Scheduling Problem. Arabian Journal for Science and Engineering, 38(12), 3593-3604. doi:10.1007/s13369-013-0625-ySaidi-Mehrabad, M., & Fattahi, P. (2006). Flexible job shop scheduling with tabu search algorithms. The International Journal of Advanced Manufacturing Technology, 32(5-6), 563-570. doi:10.1007/s00170-005-0375-4Vilcot, G., & Billaut, J.-C. (2011). A tabu search algorithm for solving a multicriteria flexible job shop scheduling problem. International Journal of Production Research, 49(23), 6963-6980. doi:10.1080/00207543.2010.526016Shi-Jin, W., Bing-Hai, Z., & Li-Feng, X. (2008). A filtered-beam-search-based heuristic algorithm for flexible job-shop scheduling problem. International Journal of Production Research, 46(11), 3027-3058. doi:10.1080/00207540600988105Wang, S., & Yu, J. (2010). An effective heuristic for flexible job-shop scheduling problem with maintenance activities. Computers & Industrial Engineering, 59(3), 436-447. doi:10.1016/j.cie.2010.05.016Zandieh, M., Yazdani, M., Gholami, M., & Mousakhani, M. (2009). A Simulated Annealing Algorithm for Flexible Job-Shop Scheduling Problem. Journal of Applied Sciences, 9(4), 662-670. doi:10.3923/jas.2009.662.670Zambrano Rey, G., Bekrar, A., Prabhu, V., & Trentesaux, D. (2014). Coupling a genetic algorithm with the distributed arrival-time control for the JIT dynamic scheduling of flexible job-shops. International Journal of Production Research, 52(12), 3688-3709. doi:10.1080/00207543.2014.881575Zhang, G., Gao, L., & Shi, Y. (2011). An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Systems with Applications, 38(4), 3563-3573. doi:10.1016/j.eswa.2010.08.145Zhang, 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(4), 1309-1318. doi:10.1016/j.cie.2008.07.021Zhou, Y., Li, B., & Yang, J. (2005). Study on job shop scheduling with sequence-dependent setup times using biological immune algorithm. The International Journal of Advanced Manufacturing Technology, 30(1-2), 105-111. doi:10.1007/s00170-005-0022-0Ziaee, M. (2013). A heuristic algorithm for solving flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 71(1-4), 519-528. doi:10.1007/s00170-013-5510-zZribi, N., Kacem, I., Kamel, A. E., & Borne, P. (2007). Assignment and Scheduling in Flexible Job-Shops by Hierarchical Optimization. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(4), 652-661. doi:10.1109/tsmcc.2007.89749

Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment

An Efficient Memetic Algorithm for the Flexible Job Shop with Setup Times

This paper addresses the flexible job shop scheduling problem with sequence-dependent setup times (SDSTFJSP). This is an extension of the classical job shop scheduling problem with many applications in real production environments. We propose an effective neighborhood structure for the problem, including feasibility and non improving conditions, as well as procedures for fast neighbor estimation. This neighborhood is embedded into a genetic algorithm hybridized with tabu search. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art in the SDST-FJSP and also in the standard FJSP. In this study, our algorithm has obtained better results than those from other methods. Moreover, it has established new upper bounds for a number of instances

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI