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

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    [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

    Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs

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    In this study, a conceptual framework is given for the dynamic multi-project scheduling problem with weighted earliness/tardiness costs (DRCMPSPWET) and a mathematical programming formulation of the problem is provided. In DRCMPSPWET, a project arrives on top of an existing project portfolio and a due date has to be quoted for the new project while minimizing the costs of schedule changes. The objective function consists of the weighted earliness tardiness costs of the activities of the existing projects in the current baseline schedule plus a term that increases linearly with the anticipated completion time of the new project. An iterated local search based approach is developed for large instances of this problem. In order to analyze the performance and behavior of the proposed method, a new multi-project data set is created by controlling the total number of activities, the due date tightness, the due date range, the number of resource types, and the completion time factor in an instance. A series of computational experiments are carried out to test the performance of the local search approach. Exact solutions are provided for the small instances. The results indicate that the local search heuristic performs well in terms of both solution quality and solution time

    Efficient heuristics for the parallel blocking flow shop scheduling problem

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    We consider the NP-hard problem of scheduling n jobs in F identical parallel flow shops, each consisting of a series of m machines, and doing so with a blocking constraint. The applied criterion is to minimize the makespan, i.e., the maximum completion time of all the jobs in F flow shops (lines). The Parallel Flow Shop Scheduling Problem (PFSP) is conceptually similar to another problem known in the literature as the Distributed Permutation Flow Shop Scheduling Problem (DPFSP), which allows modeling the scheduling process in companies with more than one factory, each factory with a flow shop configuration. Therefore, the proposed methods can solve the scheduling problem under the blocking constraint in both situations, which, to the best of our knowledge, has not been studied previously. In this paper, we propose a mathematical model along with some constructive and improvement heuristics to solve the parallel blocking flow shop problem (PBFSP) and thus minimize the maximum completion time among lines. The proposed constructive procedures use two approaches that are totally different from those proposed in the literature. These methods are used as initial solution procedures of an iterated local search (ILS) and an iterated greedy algorithm (IGA), both of which are combined with a variable neighborhood search (VNS). The proposed constructive procedure and the improved methods take into account the characteristics of the problem. The computational evaluation demonstrates that both of them –especially the IGA– perform considerably better than those algorithms adapted from the DPFSP literature.Peer ReviewedPostprint (author's final draft

    Using real-time information to reschedule jobs in a flowshop with variable processing times

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    Versión revisada. Embargo 36 mesesIn a time where detailed, instantaneous and accurate information on shop-floor status is becoming available in many manufacturing companies due to Information Technologies initiatives such as Smart Factory or Industry 4.0, a question arises regarding when and how this data can be used to improve scheduling decisions. While it is acknowledged that a continuous rescheduling based on the updated information may be beneficial as it serves to adapt the schedule to unplanned events, this rather general intuition has not been supported by a thorough experimentation, particularly for multi-stage manufacturing systems where such continuous rescheduling may introduce a high degree of nervousness in the system and deteriorates its performance. In order to study this research problem, in this paper we investigate how real-time information on the completion times of the jobs in a flowshop with variable processing times can be used to reschedule the jobs. In an exhaustive computational experience, we show that rescheduling policies pay off as long as the variability of the processing times is not very high, and only if the initially generated schedule is of good quality. Furthermore, we propose several rescheduling policies to improve the performance of continuous rescheduling while greatly reducing the frequency of rescheduling. One of these policies, based on the concept of critical path of a flowshop, outperforms the rest of policies for a wide range of scenarios.Ministerio de Ciencia e Innovación DPI2016-80750-

    Models and Strategies for Variants of the Job Shop Scheduling Problem

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    Recently, a variety of constraint programming and Boolean satisfiability approaches to scheduling problems have been introduced. They have in common the use of relatively simple propagation mechanisms and an adaptive way to focus on the most constrained part of the problem. In some cases, these methods compare favorably to more classical constraint programming methods relying on propagation algorithms for global unary or cumulative resource constraints and dedicated search heuristics. In particular, we described an approach that combines restarting, with a generic adaptive heuristic and solution guided branching on a simple model based on a decomposition of disjunctive constraints. In this paper, we introduce an adaptation of this technique for an important subclass of job shop scheduling problems (JSPs), where the objective function involves minimization of earliness/tardiness costs. We further show that our technique can be improved by adding domain specific information for one variant of the JSP (involving time lag constraints). In particular we introduce a dedicated greedy heuristic, and an improved model for the case where the maximal time lag is 0 (also referred to as no-wait JSPs).Comment: Principles and Practice of Constraint Programming - CP 2011, Perugia : Italy (2011

    A hybrid CFGTSA based approach for scheduling problem: a case study of an automobile industry

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    In the global competitive world swift, reliable and cost effective production subject to uncertain situations, through an appropriate management of the available resources, has turned out to be the necessity for surviving in the market. This inspired the development of the more efficient and robust methods to counteract the existing complexities prevailing in the market. The present paper proposes a hybrid CFGTSA algorithm inheriting the salient features of GA, TS, SA, and chaotic theory to solve the complex scheduling problems commonly faced by most of the manufacturing industries. The proposed CFGTSA algorithm has been tested on a scheduling problem of an automobile industry, and its efficacy has been shown by comparing the results with GA, SA, TS, GTS, and hybrid TSA algorithms
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