Abstract
Driven by innovations in production techniques and tools, factories are becoming more and more flexible. In addition, the growth of technologies such as the Industrial Internet of Things is making production systems holding more and more decisional nodes and entities. Thus, one of the key activities of production management is the efficient scheduling of production tasks. In addition to being a complex combinatorial problem to solve, the nature of the environment makes dynamic scheduling a very challenging problem. This paper addresses the problem of dynamic scheduling of a flexible manufacturing system (FMS), with constraints such as family-dependent setup times and interoperability. To this purpose, the proposed approach combines a set of scheduling rules optimized by an optimization-simulation mechanism. The experiments are performed on two sets of scenarios describing the dynamic arrival of products in the system.
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Zambrano Rey, G., Bonte, T., Prabhu, V., Trentesaux, D.: Reducing myopic behaviour in FMS control: a semi-heterarchical simulation–optimization approach. Simul. Model. Prac. Theor. 46, 53–75 (2014). Elsevier B.V.
Leitão, P., Alves, J., Pereira, A.I.: Solving myopia in real-time decision-making using Petri nets models’ knowledge for service-oriented manufacturing systems. In: IFAC Proceedings Volumes (IFAC-PapersOnline), pp. 144–149. Elsevier (2010)
Sawhney, R.: Interplay between uncertainty and flexibility across the value-chain: towards a transformation model of manufacturing flexibility. J. Oper. Manag. 24, 476–493 (2006)
Joseph, O.A., Sridharan, R.: Evaluation of routing flexibility of a flexible manufacturing system using simulation modelling and analysis. Int. J. Adv. Manuf. Technol. 56, 273–289 (2011). Springer
Fanjul-Peyro, L., Perea, F., Ruiz, R.: Models and metaheuristics for the unrelated parallel machine scheduling problem with additional resources. Eur. J. Oper. Res. 260, 482–493 (2017). North-Holland
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 3rd ed. Springer (2016)
Fahmy, A., Hassan, T., Bassioni, H.: What is dynamic scheduling? PM World J. III, 1–9 (2014)
Lopez, P., Roubellat, F.: Production Scheduling. ISTE, London, UK (2010)
Allahverdi, A.: The third comprehensive survey on scheduling problems with setup times/costs. Eur. J. Oper. Res. 246, 345–378 (2015)
Godinho Filho, M., Barco, C.F., Tavares Neto, R.F.: Using Genetic Algorithms to solve scheduling problems on flexible manufacturing systems (FMS): a literature survey, classification and analysis. Flex. Serv. Manuf. J. 26, 408–431 (2014)
Sabuncuoglu, I., Kizilisik, O.B.: Reactive scheduling in a dynamic and stochastic FMS environment. Int. J. Prod. Res. 41, 4211–4231 (2003)
Corning, P.A.: The re-emergence of “emergence”: a venerable concept in search of a theory. Complexity 7, 18–30 (2002)
Pannequin, R., Morel, G., Thomas, A.: The performance of product-driven manufacturing control: an emulation-based benchmarking study. Comput. Ind. 60, 195–203 (2009)
Pickardt, C.W., Branke, J.: Setup-oriented dispatching rules—a survey. Int. J. Prod. Res. 50, 5823–5842 (2012). Taylor & Francis Group
Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Oper. Res. 25, 45–61 (1977)
Sarin, S.C., Varadarajan, A., Wang, L.: A survey of dispatching rules for operational control in wafer fabrication. Prod. Plann. Control 22, 4–24 (2011)
Fan, H.L., Xiong, H.G., Jiang, G.Z., Li, G.F.: Survey of the selection and evaluation for dispatching rules in dynamic job shop scheduling problem. In: Proceedings—2015 Chinese Automation Congress, CAC 2015, pp. 1926–1931. IEEE (2016)
Jorapur, V.S., Puranik, V.S., Deshpande, A.S., Sharma, M.: A promising initial population based genetic algorithm for job shop scheduling problem. J. Softw. Eng. Appl. 9, 208–214 (2016)
Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 176–190. Springer, Berlin, Heidelberg (2001)
Pillay, N., Qu, R.: Hyper-Heuristics: Theory and Applications. Springer International Publishing, Cham (2018)
Burke, E.K., Hyde, M.R., Kendall, G., et al.: A classification of hyper-heuristic approaches: revisited. In: Potvin, J.-Y.M.G. (ed.) International Series in Operations Research and Management Science, 2nd ed., pp. 453–477. Springer, New York, NY, USA (2019)
Ochoa, G., Vázquez-Rodríguez, J.A., Petrovic, S., Burke, E.: Dispatching rules for production scheduling: a hyper-heuristic landscape analysis. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1873–1880 (2009)
Burke, E.K., Hyde, M., Kendall, G., et al.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, pp. 449–468. Springer US, Boston, MA (2010)
Hershauer, J.C., Ebert, R.J.: Search and simulation selection of a job-shop sequencing rule. Manag. Sci. 21, 833–843 (1975). INFORMS
Vázquez Rodríguez, J.A., Petrovic, S., Salhi, A.: A combined meta-heuristic with hyper-heuristic approach to the scheduling of the hybrid flow shop with sequence dependent setup times and uniform machines. In: Proceedings of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Applications, pp. 506–513. Paris, France (2007)
Nie, L., Gao, L., Li, P., Zhang, L.: Application of gene expression programming on dynamic job shop scheduling problem. In: Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Lausanne, Switzerland, pp. 291–295 (2011)
Bouazza, W., Sallez, Y., Trentesaux, D.: Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics. Int. J. Comput. Integrated Manuf. 34, 641–665 (2021). Taylor & Francis
Scenario instances dataset [Sohoma21]. https://www.researchgate.net/publication/354170341_Scenario_instances_dataset_Sohoma21. Accessed 27 August 2021
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Bouazza, W., Sallez, Y., Trentesaux, D. (2022). Toward Efficient FMS Scheduling Through Rules Combination Using an Optimization-Simulation Mechanism. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_40
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