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Advances in Production Engineering & Management

Archives > Volume 9 | Number 3 | September 2014 > pp 139–151

Advances in Production Engineering & Management
Volume 9 | Number 3 | September 2014 | pp 139–151

http://dx.doi.org/10.14743/apem2014.3.183

Scheduling optimization of a flexible manufacturing system using a modified NSGA-II algorithm
Nidhiry, N.M.; Saravanan, R.
ABSTRACT AND REFERENCES (PDF)  |  FULL ARTICLE TEXT (PDF)

A B S T R A C T
The Flexible Manufacturing System (FMS) belongs to the class of production systems in which the main characteristic is the simultaneous execution of several processes and sharing a finite set of resources. Nowadays, FMS must attend to the demands of market needs for personalized products. Consequently the life-cycle of a product tends to be shorter and a greater variety of products must be produced in a simultaneous manner. The FMS considered in this work has 16 CNC machine tools for processing 80 varieties of products. Since the minimizing of a machine's idle time and thus the minimizing of total penalty costs are contradictory objectives, the problem has a multi-objective nature. The objective of this research was to develop a modified non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization. The research will then evaluate and discuss the performance of the modified NSGA-II against the original NSGA-II. The existing NSGA II has been modified in order to improve the global optimal front and reduce the computational effort. The result has been compared with the existing NSGA-II, cuckoo search (CS), particle swarm optimization algorithm (PSO), etc. and it was found that the proposed approach was superior.

A R T I C L E   I N F O
Keywords • Flexible manufacturing system, Scheduling optimization, Multi-objective optimization, NSGA-II,
Modified NSGA-II
Corresponding authorNidhiry, N.M.
Article history • Received 20 October 2013, First Revision 16 June 2014, Second Revision 16 August 2014,
Accepted 19 August 2014
Published on-line • 7 September 2014

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