Abstract
This paper aims to demonstrate the application of multi-objective evolutionary optimization, namely an adaptation of NSGA-II, to simultaneously optimize the assembly sequence plan as well as selection of the type and number of assembly stations for a production shop that produces three different models of wind propelled ventilators. The decision variables, which are the assembly sequences of each product and the machine selection at each assembly station, are encoded in a manner that allows efficient implementation of a repair operator to maintain the feasibility of the offspring. Test runs are conducted for the sample assembly system using a crossover operator tailored for the proposed encoding and some conventional crossover schemes. The results show overall good performance for all schemes with the best performance achieved by the tailored crossover, which illustrates the applicability of multi-objective GA’s. The presented framework proposed is generic to be applicable to other products and assembly systems.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
De Fazio, T., Whitney, D.: Simplified Generation of All Mechanical Assembly Sequences. IEEE J. of Robotics and Automation, Vol. 3, No. 6, (1987) 640–658.
De Mello, H., Luiz S., Sanderson, A.: A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Transactions on Robotics and Autonomous Systems, Vol. 7, No. 2 (1991) 228–240.
Garey, M., Johnson, D.,: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, NY, (1979).
Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison — Wesley Publishing Company (1989).
Awdah, N., Sepehri, N., Hwalwshka, O.: A Computer-Aided Process Planning Model Based o Genetic Algorithms. Computers and Operations Research, Vol. 22, No. 8, (1995) 841–856.
Kaeschel, J., Meier, B., Fisher, M., Teich, T.: Evolutionary Real-World Shop Floor Scheduling using Parallelization and Parameter Coevolution. Proceedings of the Genetic and Evolutionary Computation Conference, Las Vegas, NV (2000) 697–701.
Chen, J., Ho, S.: Multi-Objective Evolutionary Optimization of Flexible Manufacturing Systems. Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, CA (2001) 1260–1267.
Saitou, K., Malpathak, S., Qvam, H.: Robust Design of Flexible Manufacturing Systems using Colored Petri Net and Genetic Algorithm. Journal of Intelligent Manufacturing, Vol. 13, No. 5, (2002) 339–351.
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996).
Deb, K., Argawal, S., Pratab, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. Proceedings of the Parallel Problem Solving from Nature VI Conference, Paris, France (2000) 849–858.
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers (2002).
Kelton, W., Sadowski, R., Sadowski, D.: Simulation with ARENA. 2nd edn, McGraw Hill (2002).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hamza, K., Reyes-Luna, J.F., Saitou, K. (2003). Simultaneous Assembly Planning and Assembly System Design Using Multi-objective Genetic Algorithms. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_106
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_106
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive