Integrating Preventive Maintenance Planning and Production Scheduling under Reentrant Job Shop

This paper focuses on a preventive maintenance plan and production scheduling problem under reentrant Job Shop in semiconductor production. Previous researches discussed production scheduling and preventive maintenance plan independently, especially on reentrant Job Shop. Due to reentrancy, reentrant Job Shop scheduling is more complex than the standard Job Shop which belongs to NP-hard problems. Reentrancy is a typical characteristic of semiconductor production. What is more, the equipment of semiconductor production is very expensive. Equipment failure will affect the normal production plan. It is necessary to maintain it regularly. So, we establish an integrated and optimal mathematical model. In this paper, we use the hybrid particle swarm optimization algorithm to solve the problem for it is highly nonlinear and discrete. The proposed model is evaluated through some simple simulation experiments and the results show that the model works better than the independent decision-making model in terms of minimizing maximum completion time

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). 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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). 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Swarm intelligence for scheduling: a review

Swarm Intelligence generally refers to a problem-solving ability that emerges from the interaction of simple information-processing units. The concept of Swarm suggests multiplicity, distribution, stochasticity, randomness, and messiness. The concept of Intelligence suggests that problem-solving approach is successful considering learning, creativity, cognition capabilities. This paper introduces some of the theoretical foundations, the biological motivation and fundamental aspects of swarm intelligence based optimization techniques such Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Artificial Bees Colony (ABC) algorithms for scheduling optimization