Abstract
Many researchers developed algorithms for a dual-resource constrained flexible job shop (DRC-FJSP) where both machines and workers need to be simultaneously scheduled. In those models and algorithms in the literature, the authors assumed that workers are machine operators responsible for performing the production process steps from the beginning to the end of the operation. However, because of increased automation and the adoption of numerically controlled machines, workers become machine tenders and should not be bottleneck and constraining resources. On the other hand, skilled setup operators remain being constraining limited resources in industries. Unlike machine tenders, a setup operator can leave the machine once setup is completed and take on another setup operation on another machine. In this paper, for the first time, we develop a genetic algorithm for a new DRC-FJSP where setup operators and machine tools are constraining resources. Numerical examples of varying problem sizes are presented to show the performance of the algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Azizi, N., Zolfaghari, S., Liang, M.: Modeling job rotation in manufacturing systems: the study of employee’s boredom and skill variations. Int. J. Prod. Econ. 123(1), 69–85 (2010)
Caldeira, R., Gnanavelbabu, A.: Solving the flexible job shop scheduling problem using an improved Jaya algorithm. Comput. Ind. Eng. 137 (2019)
Defersha, F.M., Rooyani, D.: 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. Comput. Ind. Eng. 147 (2020)
Dhiflaoui, M., Nouri, H., Driss, O.: Dual-resource constraints in classical and flexible job shop problems: a state-of-the-art review. Procedia Comput. Sci. 126, 1507–1515 (2018)
Fantahun M. Defersha, Chen, M.: A parallel genetic algorithm for a flexible job-shop scheduling problem with sequence dependent setups. Int. J. Adv. Manuf. Technol. 49(1–4), 263–279 (2010)
Fruggiero, F., Riemma, S., Ouazene, Y., Macchiaroli, R., Guglielmi, V.: Incorporating the human factor within manufacturing dynamics. IFAC-PapersOnLine 49(12), 1691–1696 (2016)
Gel, E.S., Hopp, W.J., Van Oyen, M.P.: Hierarchical cross-training in work-in-process-constrained systems. IIE Trans. 39(2), 125–143 (2007)
Givi, Z., Jaber, M., Neumann, W.: Production Planning in DRC systems considering worker performance. Comput. Ind. Eng. 87, 317–327 (2015)
Nembhard, D.A., Nembhard, H.B., Qin, R.: A real options model for workforce cross training. Eng. Econ. 36(10), 919–940 (2005)
Obimuyiwa, D.: Solving Flexible Job Shop Scheduling Problem in the Presence of Limited Number of Skilled Cross-Trained Setup Operators. Masc, University of Guelph (2020). http://www.defersha.ca/thesis/Dolapo Thesiss.pdf
Peng C., Fang Y., Lou P., Yan, J.: Analysis of double-resource flexible job shop scheduling problem based on genetic algorithm. In: Proceedings of the 15th International Conference on Networking, Sensing and Control, ICNSC ’18, pp. 1–6 (2018)
Pezzella, F., Morganti, G., Ciaschetti, G.: A genetic algorithm for the Flexible Job-Shop Scheduling Problem. J. Comput. Oper. Res. 35(10), 3202–3212 (2008)
Wu, R., Li, S., Guo, S., Xu, W.: Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm. Adv. Mech. Eng. 10(10), 1–14 (2018)
Xu, J., Xu, X., Xie, S.Q.: Recent developments in dual resource constrained (DRC) system research. Eur. J. Oper. Res. 215(2), 309–318 (2011)
Zhang, J., Wang, W., Xu, X.: A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility. J. Intell. Manuf. 28(8), 1961–1972 (2015). https://doi.org/10.1007/s10845-015-1082-0
Acknowledgement
The authors would like to than the Natural Science and Engineering Research Council of Canada (NSERC) for the financial support in conducting this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Obimuyiwa, D., Defersha, F. (2021). A Genetic Algorithm for Flexible Job Shop Scheduling Problem with Scarce Cross Trained Setup Operators. In: Rossit, D.A., Tohmé, F., Mejía Delgadillo, G. (eds) Production Research. ICPR-Americas 2020. Communications in Computer and Information Science, vol 1407. Springer, Cham. https://doi.org/10.1007/978-3-030-76307-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-76307-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-76306-0
Online ISBN: 978-3-030-76307-7
eBook Packages: Computer ScienceComputer Science (R0)