Abstract
Future productions are facing an increasingly complex environments, customized, flexible and high-quality production. Moreover, low costs, high reactivity and high quality products are necessary criteria for industries to achieve competitiveness in nowadays market. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfill these requirements. This chapter addresses the multi-objective process plan generation problem in RMS environment. Three approaches are proposed and compared: an iterative multi-objective integer linear program (I-MOILP) and adapted versions of the well-known evolutionary algorithms, respectively, archived multi-objective simulated annealing (AMOSA) and the non-dominated sorting genetic algorithm (NSGA-II). Moreover, in addition to the minimization of the classical total production cost and the total completion time, the minimization of the maximum machines exploitation time is considered as a novel optimization criterion, in order to have high quality products. To illustrate the applicability of the three approaches, an example is presented and the obtained numerical results are analysed.
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
A. Azab, H. ElMaraghy, Mathematical modeling for reconfigurable process planning. CIRP Ann. Manuf. Technol. 56(1), 467–472 (2007)
A. Bensmaine, M. Dahane, L. Benyoucef, A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Comput. Ind. Eng. 66(3), 519–524 (2013)
A. Bensmaine, M. Dahane, L. Benyoucef, A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems. Int. J. Prod. Res. 52(12), 3583–3594 (2014)
A. Chaube, L. Benyoucef, M.K. Tiwari, An adapted nsga-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system. J. Intell. Manuf. 23(4), 1141–1155 (2012)
A.I. Shabaka, H. ElMaraghy, A model for generating optimal process plans in rms. Int. J. Comput. Integr. Manuf. 21(2), 180–194 (2008)
S.S. Bandyopadhyay, U. Maulik, K. Deb, A simulated annealing-based multiobjective optimization algorithm: Amosa. IEEE Trans. Evol. Comput. 12(3), 269–283 (2008)
F. Musharavati, A.S.M. Hamouda, Enhanced simulated-annealing-based algorithms and their applications to process planning in reconfigurable manufacturing systems. Adv. Eng. Softw. 45(1), 80–90 (2012)
F.A. Touzout, L. Benyoucef, Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches. Int. J. Prod. Res. 57(8), 2531–2547 (2019)
G. Nallakumarasamy, P.S.S. Raja, K.V. Srinivasanand, R. Malayalamurthi, Optimization of operation sequencing in capp using superhybrid genetic algorithms-simulated annealing technique. ISRN Mech. Eng. (2011)
H. ElMaraghy, Reconfigurable process plans for responsive manufacturing systems, in Digital Enterprise Technology (Springer, 2007), pp. 35–44
H. Haddou Benderbal, M. Dahane, L. Benyoucef, Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (rms) design under unavailability constraints. Int. J. Prod. Res. 55(20), 6033–6051 (2017)
H. Haddou Benderbal, M. Dahane, L. Benyoucef, Modularity assessment in reconfigurable manufacturing system (rms) design: an archived multi-objective simulated annealing-based approach. Int. J. Adv. Manuf. Technol. 94(1–4), 729–749 (2018)
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-ii. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
M. Maniraj, V. Pakkirisamy, R. Jeyapaul, An ant colony optimization-based approach for a single-product flow-line reconfigurable manufacturing systems. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 231(7), 1229–1236 (2017)
Q. Xia, A. Etienne, J. Dantan, A. Siadat, Reconfigurable machining process planning for part variety in new manufacturing paradigms: Definitions, models and framework. Comput. Ind. Eng. 115, 206–219 (2018)
Y. Koren, General RMS characteristics. Comparison with dedicated and flexible systems, in Reconfigurable Manufacturing Systems and Transformable Factories (Springer, 2006), pp. 27–45
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Touzout, F.A., Haddou Benderbal, H., Khezri, A., Benyoucef, L. (2021). Process Plan Generation for Reconfigurable Manufacturing Systems: Exact Versus Evolutionary-Based Multi-objective Approaches. In: Yalaoui, F., Amodeo, L., Talbi, EG. (eds) Heuristics for Optimization and Learning. Studies in Computational Intelligence, vol 906. Springer, Cham. https://doi.org/10.1007/978-3-030-58930-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-58930-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58929-5
Online ISBN: 978-3-030-58930-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)