Article-detailsAdvances in Industrial Engineering and Management
 Article-details | AIEM
 


2017(Volume 6)
Vol. 6, No. 2 (2017)
Vol. 6, No. 1 (2017)
2016(Volume 5)
Vol. 5, No. 2 (2016)
Vol. 5, No. 1 (2016)
2015(Volume 4)
Vol. 4, No. 2 (2015)
Vol. 4, No. 1 (2015)
2014(Volume 3)
Vol.3, No.4 ( 2014 )
Vol.3, No.3 ( 2014 )
Vol.3, No.2 ( 2014 )
Vol.3, No.1 ( 2014 )
2013 ( Volume 2 )
Vol.2, No.2 ( 2013 )
Vol.2, No.1 ( 2013 )
2012 ( Volume 1 )
Vol. 1, No.1 ( 2012 )

 

 


ADVANCES IN INDUSTRIAL ENGINEERING AND MANAGEMENT
ISSN:2222-7059 (Print);EISSN: 2222-7067 (Online)
Copyright © 2000- American Scientific Publishers. All Rights Reserved.


Title : Scheduling Optimization of FMS Using NSGA-II
Author(s) : Nidhish Mathew Nidhiry1, R. Saravanan2
Author affiliation : 1Karapagam University, 2 JCT College of engineering and technology
Corresponding author img Corresponding author at : Corresponding author img  

Abstract:
The Flexible Manufacturing Systems (FMS) belong to that class of productive systems whose main characteristic is the simultaneous execution of several processes and the sharing of a finite set of resources. Now days, FMS must attend to the demand of the market for personalized products. Consequently, the life cycle of the product tends to be shorter and a greater variety of products must be produced simultaneously. FMS considered in this work has 32 CNC Machine tools for processing 40 varieties of products. Since minimizing machine idle time and minimizing total penalty cost are contradictory objectives the problem has a multi objective nature. In this work, we have developed a multi-objective optimization procedure based on NSGA-II and software has been developed using .net programming for setting the optimum product sequence. A Global–optimal front was then obtained using the software after 3000 generations. Keywords: Flexible manufacturing system, Multi–objective optimization, NSGA II, Scheduling Optimization, genetic algorithms.

Key words:Flexible manufacturing system, Multi–objective optimization, NSGA II, Scheduling Optimization, genetic algorithms

Cite it:
Nidhish Mathew Nidhiry, R. Saravanan, Scheduling Optimization of FMS Using NSGA-II, Advances in Industrial Engineering and Management, Vol.3, No.1, 2014, pp.63-72, doi: 10.7508/AIEM-V3-N1-63-72.

Full Text : PDF(size: 891.76 kB, pp.63-72, Download times:1606)

DOI : 10.7508/AIEM-V3-N1-63-72

References:
[1] AVS Sreedhar kumar and Dr.V. Ve- eranna 2010, Optimization of FMS scheduling using non-traditional techniques. nternational Journal of Engineering Science and Technology. pp:7289-7296.
[2] W He, Kusiak A., 1992, Scheduling of manufacturing systems. Int. J Comput Ind., pp:163–175.
[3] Hoitomt DJ, Luh PB, Pattipati KR 1993, A practical approach to job-shop scheduling problems. IEEE Trans Robot Automat, pp:1–13.
[4] Deb, K., 2001. Multi-Objective Optim- ization using Evolutionary Algorithms, John Wiley & Sons, Ltd.
[5] J. Jerald, P. Asokan , G. Prabaharan, R. Saravanan. 2004, Scheduling optimisati- on of flexible manufacturing systems using particle swarm optimisation algorithm, Int. Journ. Of Advanced Manufacturing Technology, pp:964 – 971.
[6] Lee DY, Dicesare F, 1994, Scheduling of flexible manufacturing systems: using Petri nets and heuristic search. IEEE Trans Robot Automat, pp:23–132.
[7] R Kumar, M K Tiwari and R Shankar 2003, Scheduling of flexible manufacturing systems: an ant colony optimization approach. Proc. Instn Mech. Engrs. Vol. 217 Part B: J. Engineering Manufacture .
[8] Shnits B, Sinreich D. 2006. Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy. Int J Flex Manuf Syst. pp:1–27.
[9] Tiwari M.K, Vidiyarthi N.K. 2000, Solving Machine loading problem in flexible manufacturing system using genetic algorithm based heuristic approach. International journal of production research, pp:3357-87.
[10] Toker A, Kondakci S, Erkip N., 1994, Job shop Scheduling under a nonrenewable resource constraint. J Oper Res Soc., pp:942–947.
[11] Yu MC, Greene TJ., 2006. A simulation analysis of controlling rules for flexible pull systems. Int J Manuf Res. pp:314–331.
[12] Z. X. Guo , W. K. Wong, S. Y. S. Leung, J. T. FanS, F. Chan., 2008, A genetic-algorithm-based optimization model for scheduling flexible assembly lines. Int J Adv Manuf Technol . pp:156–168.
[13] Saravanan M. and Noorul haq A., 2008, 'Evaluation of Scatter Search Approach for Scheduling Optimization of Flexible Manufacturing Systems'. International journal of Advanced manufacturing Technology, pp. 978-986.
[14] Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T. Meyarivan., 2002, A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II, ieee transactions on evolutionary computation.
[15] Kim, Suzuki, Narikiyo., 2007, FMS scheduling based on time petro net model and reactive graph search. Appl Math Model. pp:955 – 970.
[16] Lee J, Lee SJ., 2010, Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix. Comput Ind Eng. pp:799-806.
[17] Liu J et al., 2009. A live subclass of Petri nets and their application in modeling flexible manufacturing system. Int J Adv Manuf Technol. pp:66-74.
[18] Rossi A, Dini G., 2007. Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimization method. Robot Comput Integrated Manuf. pp:503–516.
[19] Dong-Sheng Xu et al., 2009, An Improved Ant Colony Optimization for Flexible Job Shop Scheduling Problems. International Joint Conference on Computational Sciences and Optimization, pp.517-519.
[20] Sankar S et al., 2003. A multiobjective genetic algorithm for scheduling a flexible manufacturing system. Int J Adv Manuf Technol. pp:229–236.
[21] Shashikant Burnwal , Sankha Deb, 2013. Scheduling optimization of flexible manufacturing system using cuckoo search-based approach. Int J Adv Manuf Technol. pp:951–959.

Terms and Conditions   Privacy Policy  Copyright©2000- 2014 American Scientific Publishers. All Rights Reserved.