Scheduling optimization of a flexible manufacturing system using a modified NSGA-II algorithm

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 nondominated 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.